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A decision-making framework for process plant maintenance
Abstract
An optimal maintenance approach is a key support to industrial production in the contemporary process industry. This paper presents a Multiple-Criteria Decision Making (MCDM) methodology for selecting the optimal mix of maintenance approaches -- Corrective Maintenance (CM), Time-Based Preventive Maintenance (TBPM) and Condition-Based Predictive Maintenance (CBPM) -- for different equipment in a typical process plant. First, the criticality of each equipment from the point of view of maintenance is achieved by risk ranking them (based on the worst-case failure mode), thus prioritising them for maintenance interventions. Next, the MCDM methodology with a fuzzy adaptation of the Analytic Hierarchy Process (AHP) technique is applied to individual equipment. The criteria used as part of the MCDM model are safety, maintenance investment, business interruption loss and maintenance technique feasibility. Furthermore, this technique is embedded into a Goal Programming (GP) model to optimise multiple objectives such as risk reduction and cost minimisation that are subject to resource constraints. The present approach is an improvement over the contemporary approaches to decision making for maintenance management in that it integrates risk assessment with the GP-fuzzy AHP technique and is sufficiently generalised. The approach can aid in the formulation of a cost-effective maintenance approach for a plant. [Received 11 August 2008; Revised 31 December 2008; Revised 20 February 2009; Accepted 25 February 2009]
... The literature shows how multicriteria techniques have been successfully applied to choosing maintenance policies or strategies for the oil and gas industries [15][16][17], paper mills and pumping stations [18], the weapons system of the Norwegian Army [19], in manufacturing companies [20][21][22], an urban waste water treatment plant [23], a petrochemical plant and the food industry [24], a thermal power plant [25], the textile industry [26,27], aircraft systems [28], a chemical plant [29], a processing plant [30], the railway industry [31], a newspaper printing facility [32], a mining company [33], a dump truck [34], a steel company [35], and a cogeneration system for an ethanol and sugar plant [36]. ...
... Although in reality industrial plants apply combinations of different maintenance policies to assets, in the literature, except in Bertolini and Bevilacqua (2006) [16] and Ghosh and Roy (2010) [30], a single maintenance policy is chosen for each asset; this is worth making clear because the choice of predictive maintenance for a model would eliminate the possibility of applying corrective maintenance, when in real life this does not happen and several maintenance policies are applied together to the same asset. ...
... Although in reality industrial plants apply combinations of different maintenance policies to assets, in the literature, except in Bertolini and Bevilacqua (2006) [16] and Ghosh and Roy (2010) [30], a single maintenance policy is chosen for each asset; this is worth making clear because the choice of predictive maintenance for a model would eliminate the possibility of applying corrective maintenance, when in real life this does not happen and several maintenance policies are applied together to the same asset. ...
Background:
Healthcare organizations have far greater maintenance needs for their medical equipment than other organization, as many are used directly with patients. However, the literature on asset management in healthcare organizations is very limited. The aim of this research is to provide more rational application of maintenance policies, leading to an increase in quality of care.
Methods:
This article describes a multicriteria decision-making approach which integrates Markov chains with the multicriteria Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to facilitate the best choice of combination of maintenance policies by using the judgements of a multi-disciplinary decision group. The proposed approach takes into account the level of acceptance that a given alternative would have among professionals. It also takes into account criteria related to cost, quality of care and impact of care cover.
Results:
This multicriteria approach is applied to four dialysis subsystems: patients infected with hepatitis C, infected with hepatitis B, acute and chronic; in all cases, the maintenance strategy obtained consists of applying corrective and preventive maintenance plus two reserve machines.
Conclusions:
The added value in decision-making practices from this research comes from: (i) integrating the use of Markov chains to obtain the alternatives to be assessed by a multicriteria methodology; (ii) proposing the use of MACBETH to make rational decisions on asset management in healthcare organizations; (iii) applying the multicriteria approach to select a set or combination of maintenance policies in four dialysis subsystems of a health care organization. In the multicriteria decision making approach proposed, economic criteria have been used, related to the quality of care which is desired for patients (availability), and the acceptance that each alternative would have considering the maintenance and healthcare resources which exist in the organization, with the inclusion of a decision-making group. This approach is better suited to actual health care organization practice and depending on the subsystem analysed, improvements are introduced that are not included in normal maintenance policies; in this way, not only have different maintenance policies been suggested, but also alternatives that, in each case and according to viability, provide a more complete decision tool for the maintenance manager.
... The real application of maintenances policies consists of integrating several possible strategies to fulfil the requirements of the organisation in terms of availability, efficiency and profitability. However, it should be noted that in the literature, only Bertolini and Bevilacqua [23] and Ghosh and Roy [24] combine different maintenance strategies; The rest of the literature chooses a single maintenance policy, so the decision-making process is not adapted to the conditions which would allow real application in a company. This study considers as alternatives the combination of maintenance policies, so it is in line with the actual practice of maintenance in organisations. ...
... The equivalent repair rate μ i for the systems is calculated using Eq. (24). λ j and μ j are the failure and repair rates of each of the constituent sections of a system. ...
Background
The real-world application of maintenance in organisations brings together a number of maintenance policies in order to achieve the desired availability, efficiency and profitability. However, the literature mostly chooses a single maintenance policy, and so the decision process is not suited to the real conditions in the company to which it is applied. Our study takes a combination of maintenance policies as alternatives, and so conforms to the actual practice of maintenance in organisations. Furthermore, it introduces the possibility of including extra spare parts, or outsourcing maintenance policies. Although the selection of maintenance policies has been applied to many kinds of business and of machine, there is almost no instance of its application to hospitals, and it has never been applied to delivery systems for cytostatic drugs.
Methods
The model uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is recognised as being highly suitable for solving group decision-making problems in a fuzzy environment. Fuzzy set theory is also considered to be more proficient than crisp numbers for handling the ambiguity, imprecisions, data scarcity, and uncertainty inherent in decisions made by human beings. The judgements required were obtained from a decision group comprising the heads of facilities maintenance, maintenance of medical equipment, health and safety at work, environment, and programming-admission. The group also included care staff; specifically, the heads of the main clinical services, and the medical supervisors. The model includes original criteria, such as Quality of health care, which measures impact on care as a function of mean availability of each alternative. It also considers Impact on hospital management via the criteria: Working environment in the organisation and Impact on health care; the former criterion measures equality among care services in the hospital, while the latter assesses the effect on regional health cover. The model was built using real data obtained from a state hospital in Spain. The model can also be easily applied to other national and international healthcare organisations, providing weights specific to the criteria. These are produced by a decision group from each healthcare organisation and the alternatives are updated in accordance with what is considered important in each hospital.
Results
The results obtained from the model recommend changing the alternative that is currently in use, Corrective and Preventive Maintenance, to Corrective and Preventive Maintenance plus two spare hoods. This alternative would lead to an availability of 1 (the highest possible) in the systems for preparing personalised cytotoxic drugs, and so the quality of service is therefore very high. Additionally, it could offer services to all the users of the hospital, and also offer cover in the preparation of cytotoxic medicines to other hospitals in the catchment area.
Conclusions
The results suggest the possibility that improvements to the support and logistical systems, which include maintenance, traditionally held to have no effect on quality of care, may be key to improving care quality, but also in reducing risk to patients, care and non-care staff, and the environment.
... Then, a lot of authors explored the use on this theme of combinations between different methods: among the others, Bevilacqua [9] implemented the AHP by integrating goal programming to determine the optimal maintenance policy in an oil refinery; Ilangkumaran [10] proposed a combination of fuzzy AHP with TOPSIS, in order to select the optimal maintenance policy for textile industry. Ghosh [11] introduced an integration of AHP, goal programming with fuzzy logic; Chen [12] tried using AHP, TOPSIS and grey relational analysis to evaluate the performance and decided the optimal maintenance policies that suited semiconductor company in a more effective and accurate manner. ...
... The materials which production affects the most the environment is certainly the cement. Its environmental impact can be assessed in function of the energy that is released during its production, by considering that the ratio between the energy and the mass of cement is 4,882 MJ/kg [11] and by adopting a density value of 1.360 kg/m 3 for cement. The energy consumption caused by an intervention of total reconstruction on a surface of 2000 m 2 is then 166.600 MJ, while interventions of partial reconstruction, considered to occur on the 50% of the surface, dissipate 83.300 MJ. ...
The life cycle of building components can be managed according to different maintenance strategies, which mainly differ in performance and economic terms. What is the most convenient one among the possible scenarios? It has been shown in the past that the typology of maintenance interventions and the consequent periodicity are closely related to performance decay, and can lead to choices that, however, generally concern the purely economic sphere. In this sense, it seems interesting to know the modalities of the performance decay, which may allow-even though for many components the "measurement" of its values is problematic-the construction of performance / time curves. This result was possible, in other experiments conducted in the past, for one of the components to be considered most critical for the whole building (the plaster), thanks to a study that sampled 53 masonry buildings with homogeneous characteristics (both from the technological point of view and from the era of realization), observed within 20 years. This paper highlights that the only economic evaluation is not enough to identify the ideal solution, because-inter alia-there is a more suitable solution depending on the context framework in which the decision maker is operating. Commitment, budget, component typology, time span to consider, are the main factors influencing the choice, not ignoring design issues. A TOPSIS multi-criteria analysis is proposed, the results of which are an interesting starting point for defining maintenance plans characterized by greater reliability, not only technical but also economic.
... Even the concept of customised maintenance has been developed to incorporate data in a company using information and communication technology (ICT) (Waeyenbergh & Pintelon, 2002). Another piece of research was presented in this area, detailing a methodology for selecting an optimal mix of maintenance approaches (Roy & Ghosh, 2010). The emphasis is on linking tactical and operational planning across all decision-making levels, which will enable world-class maintenance to be realised (Pintelon & Parodi-Herz, 2008). ...
... prioritising sustainable maintenance strategies ( Pires et al., 2016), cost-based criticality (CBC) strategies for maintenance prioritisation (Moore & Starr, 2006), maintenance criticality analysis (MCA)-based prioritisation for improving machine availability ( Silvestri et al., 2014) and multi-criteria decision- making (MCDM)-based maintenance priorities for risk reduction and cost minimisation (Roy & Ghosh, 2010). Marquez et al. (2009) in their maintenance management framework, create priorities for assets in planning maintenance strategies. ...
Productivity is an important factor in the global competitiveness of manufacturing companies. Most industrialised countries around the globe have started initiatives to transform their manufacturing companies through digitalisation and thus achieve competitiveness. Digitalisation has drastically increased expectations that production systems will have substantially higher productivity increments, increased automation and greater resource efficiency. This makes maintenance management strategically important to manufacturing companies. However, the traditional approach to maintenance has been to maximise machine availability. Machine availability does not necessarily mean machine utilisation. Machines are under-utilised because the ripple effects of breakdowns result in idling losses and subsequent loss of productivity. Therefore, there is a need for transformation in the maintenance organisation, moving from a component focus to achieving a systems perspective for solving maintenance problems.
The purpose of this thesis is to enable the successful transformation of maintenance organisations, so that they contribute directly to increased productivity and cost-effectiveness. In order to transform maintenance organisations, particularly the maintenance decision support needs to be fact-based. Therefore, the aim of this thesis is to investigate maintenance prioritisation decisions as well as develop and validate maintenance decision support that enables productivity to increase. The investigation of the current industrial practices led to identifying the gaps between practice and research (RQ1). Based on the identified gaps, a maintenance prioritisation decision support to increase productivity was developed (RQ2). The results were achieved by employing a mixed-methods research approach in the form of five empirical studies, which were conducted using surveys, interviews, experiments and case studies.
The gaps identified in maintenance prioritisation practice and research provided maintenance and productivity improvement potentials: (i) Low OEE figures (avg. 51.5 percent) indicate that maintenance needs to focus on improving the operational efficiency and availability of machines, (ii) Most companies prioritise their maintenance activities according to production operator influence or based on maintenance technicians’ experience. So, facts-based decision support tools are needed and (iii) On further examination of the decision support, the criticality classification was found to be static, subjective, using multiple factors and lacking a clear goal. In order to develop a new decision support for maintenance prioritisation, the maintenance priorities and data requirement were assessed. It was identified that system throughput increased when maintenance of bottleneck machines was prioritised and many companies generate automated data from machines that can be used to develop decision support. A data-driven machine criticality assessment framework was developed and validated using industrial cases. The framework provided guidelines on using the data and what maintenance decisions can be made.
The data-driven criticality assessment supports maintenance engineers and planners in making tactical and operational maintenance decisions that are dynamic, fact-based and factory-focused, with the goal of increasing productivity. This thesis provides a pathway for maintenance organisations to transform from their narrow focus (solving machine-level problems) to achieving a systems perspective (solving the maintenance problems of the whole production system). By connecting maintenance to productivity, maintenance organisations can help manufacturing companies compete in global production, especially within digitalised manufacturing.
... One of the modern ways of analyzing criticality is based on RCM. Particularly, using FMEA methods to identify criticality (Roy and Ghosh, 2010;Bevilacqua et al., 2009). As an improvement of FMEA, a criticality classification was inducted into the FMEA analysis named failure mode effect and criticality analysis (FMECA). ...
... Multiple criteria can include machine utilization (cumulative and current), failure rate, last repair, Preventive Maintenance (PM) delay (Gopalakrishnan et al., 1997); time, investments on maintenance, and budget (Tam and Price, 2008); and production flow, time, maintenance cost, and failures (Silvestri et al., 2014). Improving the production system based on multiple criteria priorities may include risk reduction and cost minimization (Roy and Ghosh, 2010) and increase return on investment (Tam and Price, 2008) among other aspects. Additionally, a multi criteria subjective approach was used to prioritize PM using collaborative efforts across various teams (Zanazzi et al., 2014). ...
Purpose – The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.
Design/methodology/approach – An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.
Findings – The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.
Originality/value – In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.
... Goal programming along with the Fuzzy Analytic Hierarchy Process (FAHP) is a supple tool to accomplish the goals under various constraints [16]. Hence, neither of them is adequate to consider the numerical and non-numerical information alone [17]. ...
... Constraint (2-16) expresses the expected cumulative cash in period N. Constraints (2-17) and (2-18) determine the maximum budget of investors during each construction period. Constraint(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) ensures the sum of all input and output cash flows up to period is positive, so that the projects income could cover their costs up to . ...
Due to the increase of investments in construction projects and the lack of practical models in this area, developing new practical models is essential. In this paper, researchers suggest a new model in which (1) Its assumptions are adopted based on the real world, (2) Goal programming is used because of the soft nature of the budget constraints, and (3) Risk of variations in cash flows is considered. The presented model chooses the most profitable portfolio of projects and determines their respective financing resources, area under construction, and pre-sale and sale amounts for each period, such that the cumulative cash flow at the end of the time horizon is maximized. The Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the weight of the objectives. The exact solution to the model is obtained using the ILOG CPLEX software. The presented solution seems efficient since it yields very small elapsed times to solve exactly the real-world-sized problems. Also, the sensitivity analysis is performed and the results are deliberately studied and analyzed. Parameters, such as pre-sale prices, mean, and variance of the sale price and construction costs, are among the highly sensitive parameters.
... The priority vector is a goal-programming model with three goals: global scores of maintenance policies, local scores of maintenance policies based on risk contribution, and local scores of maintenance policies based on cost. In [34], Ghosh and Roy (2010) use the worst-case failure mode to calculate criticality of equipment, and with this information they prioritize maintenance activity. They then use fuzzy AHP and a goal-programming model to optimize the goals of risk reduction and cost minimization. ...
... The priority vector is a goal-programming model with three goals: global scores of maintenance policies, local scores of maintenance policies based on risk contribution, and local scores of maintenance policies based on cost. In [34], Ghosh and Roy (2010) use the worst-case failure mode to calculate criticality of equipment, and with this information they prioritize maintenance activity. They then use fuzzy AHP and a goal-programming model to optimize the goals of risk reduction and cost minimization. ...
Health Care Organizations are large consumers of energy resources. This is due to the large number of services they must offer continuously, the strict requirements of temperature and humidity for patients and comfort for all visitors. Facilities for thermal energy production are critical as they guarantee the proper working of care services by producing primary air, eliminating legionella, and providing air conditioning to theatres, emergency areas, ICUs, neonatology departments, etc. Nonetheless, despite the importance of thermal energy production systems, there is no prior literature analysing the best maintenance to be applied to these systems. This study describes an innovative multicriteria model designed with the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) approach and Markov chains, in choosing the best combination of maintenance policies to guarantee the best quality of care. The model was created with the co-operation of a decision group made up of those in charge of different areas of a Health Care Organization. This gives the current availability of the thermal energy production systems, and the availability that would be achieved by applying other alternatives. In the system that produces hot water for the air conditioning units, the best alternative is found to be corrective and preventive maintenance. In the cold-water production system for air conditioning, the model recommends the use of corrective, preventive and predictive maintenance, monitored by vibration analysis. For the systems producing hot domestic water and hot and cold water for other systems, corrective and preventive maintenance is recommended. In the legionella treatment system, it is best to apply corrective, preventive and periodic predictive maintenance (quarterly by combustion analysis). Finally, the implications for quality of care of changing the maintenance alternatives are considered. This research was carried out on thermal energy production systems currently operational in the University General Hospital of Ciudad Real (Spain). The methodology used in this study, together with the criteria, descriptors, weightings, etc., may serve as a standard for other Health Care Organizations, with the final goal of improving quality of care.
... Notable examples include Labib et al. (1998), Ramadhan et al. (1999), Bevilacqua and Braglia (2000), Emblemsvag and Tonning (2003), Carnero (2006), Bertolini and Bevilacqua (2006) and Goossens and Basten (2015), who use the multicriteria technique Analytic Hierarchy Process (AHP). Wang et al. (2007) and Ghosh and Roy (2009) apply fuzzy AHP. Ilangkumaran and Kumanan (2009) combine fuzzy AHP and TOPSIS; Ishizaka and Nemery (2014) use ELECTRE-SORT and Cavalcante and Lopes (2015) use a multi-attribute value function. ...
... Notable examples include Labib et al. (1998), Ramadhan et al. (1999), Bevilacqua and Braglia (2000), Emblemsvag and Tonning (2003), Carnero (2006), Bertolini and Bevilacqua (2006) and Goossens and Basten (2015), who use the multicriteria technique Analytic Hierarchy Process (AHP). Wang et al. (2007) and Ghosh and Roy (2009) However, there is a serious shortage in the literature of work on choice of maintenance policies in Health Care Organizations. There is only one example, from Taghipour et al. (2011), which uses AHP to obtain a prioritization of medical devices; from the total criticality score values guidelines are established to select appropriate maintenance strategies. ...
This article shows an innovative decision support system built by integrating Markov chains with the multicriteria Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) for managing medical assets in a Health Care Organization. This model makes a choice of optimal maintenance policies on different typologies of subsystems for the distribution of medicinal gases and vacuum. The model uses a decision group made up of various departmental heads of a Health Care Organization. It should be noted that it has also been applied to a public general hospital.
... The Multiple-Criteria Decision-making methodology has been proposed to select an optimal mix of maintenance approaches (Ghosh, Roy 2009). The framework evaluates Corrective Maintenance (CM), Time-Based Preventive Maintenance (TBPM) and Condition-Based Predictive Maintenance (CBPM) for different equipment conditions. ...
... Lack of data to understand irregularities and make decisions. (Ghosh, Roy 2009) Three-tier AHP hierarchy combining risk ranking, fuzzy AHP and GP Integrates many methods to deal with decision uncertainty in different environments ...
Railway maintenance is faced with increasing demands, including the need to improve service. Data measuring the track state and suitable models or applications are needed to make good maintenance decisions. This critical review paper investigates many research papers on the use of information assurance (IA) within condition-based maintenance (CBM) on a railway track. An IA framework sheds light on the data and information used to make maintenance decisions. The paper considers work on data processing and decision-making in CBM. The results show condition monitoring suffers from an inability to determine exact positioning on the track; some data are inaccurate or unavailable. Existing studies have not adequately dealt with data content or the various technologies used. They focus on integrity, availability, authentication, authorisation and accuracy, but do not consider other IA principles important to understand data. CBM models and algorithms have difficulty understanding degradation models, and data problems mean it is difficult to make good decisions. There is a lack of long term maintenance plans. Models also need to be integrated for more realistic but not necessarily optimum solutions and to ensure practical predictions of maintenance. Some models focus on degradation, others consider prediction, and still others calculate the maintenance cost; it is difficult to combine these. Overall, data are inaccurate, there is no testing phase using realistic data, and existing models are insufficient. This has a negative impact on maintenance decisions.
... It plays a vital role in the business management of many industries, primarily to meet the ever-changing market conditions, maintain asset health, revise production targets and influence many other variables which can generate financial uncertainty and increase business risk. Understanding these business risks and how industries can use them to drive sound decision-making practices, minimise downtime, enable risk control, improvise operational efficiency, enable defect elimination, and increase overall market competitiveness has become strategically important [16]. The AM system plans and controls asset-related activities and their relationships to ensure that asset performance meets the intended competitive strategy of the organisation [17]. ...
Engineering asset management (EAM) has received a lot of attention in the last few decades. Despite this, industries struggle to identify the best strategies for maintaining assets. The decision-making around selecting a relevant maintenance strategy generally considers factors like risk, performance and cost. Risk management is, usually, largely subjective and industries consequently make investments in a subjective manner, making the allocation of budget unstructured and arbitrary. Generally, industries focus only on either overt risks or basic performance of assets, thus creating uncertainties in the decision-making process. Recently, however, maintenance decision-making has evolved from a subjective assessment, chiefly dependent on expert opinions, to utilizing live-data-sensor technology. The attitude towards component failures and how to address them has changed drastically with the evolution of maintenance strategies. Additionally, the emergence and use of several tools and models have assisted the drafting and implementation of effective maintenance strategies. These advancements, however, have only considered discrete parameters while modelling, instead of using an integrated approach. One of the primary factors which can address this shortfall and make the decision-making process more robust is the economic element. To enable an effective decision-making process, it is imperative to consider quantifiable determinants and include economic parameters while drafting maintenance policies. This paper reviews maintenance decision-making strategies in EAM and also highlights its relevance through an economic lens.
... Fuzzy AHP and Goal Programming were successfully implemented by Ghosh and Roy (2010) in the selection of optimal mix of maintenance approaches including Corrective ...
... In this sense, MCDM techniques can be used for selecting the optimal mix in terms of maintenance policy application. For example, the fuzzy Analytic Hierarchy Process (AHP) and Goal Programming have been successfully implemented to this end by Ghosh & Roy (2009). Additionally, AHP, graph theory, and TOPSIS are successively applied to prioritize items from a green maintenance perspective, considering environmental aspects, like environmental compatibility, energy efficiency, and human health (Ajukumar & Gandhi, 2013). ...
Paper aims: This paper aims to develop a proper maintenance policy directly related to defining critical components for ensuring a high level of safety and high-level in-service quality for all hydro generator units. Originality: An innovative integrated tool that contributes to ensuing assertiveness in decision-making to determine the critical components is presented in this study. Specifically, hydro-generator unit type Kaplan belonging to a Brazilian Hydroelectric power plant is used as an application case to highlight the choice of the most suitable maintenance policy in light of the proposed approach. The selection of the case study is based on the fact that hydroelectric power plants are the basis of the Brazilian energy matrix, accounting for 75% of the demand in the country. Therefore, the need to maintain hydroelectric plants’ availability and operational reliability is clear not to compromise the continuity and conformity (quality) of the electrical energy supply. Research method: Seven multi-criteria decision-making methods were applied in addition to two methods for deciding weight (Critic Method and Entropy) have been compared to determine the critical components of the hydro-generator. To investigate the robustness of the classification of the applied Multi-Criteria Decision Making approaches, a sensitivity analysis was performed based on the weight change of each decision criterion. Main findings: As a main result, the Entropy-Multi-Attribute Utility Theory model is proposed as the best approach to guarantee the selection of critical components for the Brazilian hydroelectric power plant case study. The validation sensitivity analysis by critical Fuzzy K-means groups guarantees that it is a robust tool for decision-making. Implications for theory and practice: Ensuring the availability and reliability of hydroelectric plants can be achieved by employing appropriate maintenance policies that reduce the likelihood of failure or even eliminate its root causes, preventing failure from occurring. Consequently, a robust tool for decision-making regarding the Kaplan hydro generator’s critical components’ monitoring was developed © This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
... Eventually, the technique for order of preference by similarity to ideal solution (TOPSIS) was employed in choosing the optimum provider of the hexane solvent. Ghosh and Roy [20] described how to find the optimum maintenance mix across various components of aplant's manufacturing process by applying a fuzzy decision-making methodology. ...
People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed framework’s stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model.
... It is possible to reduce these costs through the implementation of the right and appropriate R&M method (De Silva et al., 2012), which leads to maximizing the utilization of the building. Table 1 represents the different R&M methods adopted in the AEC/FM industry, as illustrated by Katebi and Almasian (2016) -of course, others have been produced (see Abu Dabous & Alkass, 2008;Ghosh & Roy, 2010), but this example seems the most complete and developed classification. An efficient R&M method is aimed at improving the operating conditions of machinery, reducing the need for repair, and completely eliminating the causes of failure (Kizim, 2013). ...
All buildings require Repair and Maintenance (R&M) in their life cycle period. However, if R&M activities are not carried out properly, deterioration will occur, service life of buildings will be reduced, and maintenance costs will increase. Hence, selecting the appropriate R&M methods is pivotal, especially for developing countries, such as Iran, which are featured by highly constrained resources. The present study aims to identify and prioritize the main criteria for selecting the suitable R&M methods for Commercial Buildings (CBs), which is considered as a profound challenge for the Architecture , Engineering and Construction/Facility Management (AEC/FM) industry. A total of 20 senior experts in the AEC/FM industry and CBs in Iran were invited to participate in a Delphi survey to solicit their perceptions and opinions on the selection criteria. The total number of individual criteria identified is 16, which are further divided into five categories: human resources, flexibility and technical capability, risks, cost of maintenance, together with facilities and technology. Then, the Fuzzy Analytic Hierarchy Process (FAHP) technique was applied to prioritize the identified criteria. Among the 5 main selection criteria, the cost of maintenance is the most important criterion for selecting appropriate R&M methods for CBs whereas the criterion of human resources (HR) was recognized as the least important.
... The analytical level of the solution increased with the combined use of multicriteria decision making methods, but these methods were insufficient in systems where the problem size increased. At this point, researchers have solved the problem of maintenance strategy selection for multiple equipment by using GP, one of the multi-criteria decision making methods [4,8,24,29]. At this stage, the maintenance strategy selection problem has been replaced by MSO. ...
Countries need to develop sustainable energy policies based on the principles of environmental sensitivity, reliability, efficiency, economy and uninterrupted service and to maintain their energy supply in order to increase their global competitiveness. In addition to this impact of sustainable energy supply on the global world, maintenance processes in power plants require high costs due to allocated time, materials and labor, and generation loss. Thus, the maintenance needs to be managed within a system. This makes analytical and feasible maintenance planning a necessity in power plants. In this context, this study focuses on maintenance strategy optimization which is the first phase of maintenance planning for one of the large-scale hydroelectric power plants with a direct effect on Turkey's energy supply security with its one fifth share in total generation. In this study, a new model is proposed for the maintenance strategy optimization problem considering the multi-objective and multicriteria structure of hydroelectric power plants with hundreds of complex equipment and the direct effect of these equipment on uninterrupted and cost-effective electricity generation. In the model, two multi-criteria decision-making methods, AHP and COPRAS methods, are integrated with integer programming method and optimal maintenance strategies are obtained for 571 equipment.
... The model considers fully backlogged shortages and it is solved using an artificial neuro-fuzzy inference system approach. Ghosh and Roy (2010) presented a multi-criteria decision-making model for choosing an optimal combination of PM and CM strategies for different machines in a process plant. Sana and Chaudhuri (2010) developed an economic manufacturing quantity (EMQ) model with an importance to the role of PM and CM costs in the optimal decision. ...
Unreliable conditions and process deterioration of machines are the major issues in several manufacturing systems. These issues not only affect the production schedules but also affect the product quality significantly. A process warm-up production and amalgamation of the preventive and corrective maintenance are important for the smooth functioning of production activities and improving the product quality. The management needs to handle the product shortages carefully and raise the backorders to upgrade the business economy. To accomplish all these objectives, we develop two production inventory models in this paper by considering a process warm-up production run, hybrid maintenance schedule, anticipated shortages during the maintenance period, and rework of imperfect items. Two different rates are used to distinguish the production of imperfect items in the warm-up and standard production periods. We use fixed and variable lengths to represent the warm-up production period in the two models respectively. The maximum customer waiting time for receiving the backorders is calculated under the consideration of finite replenishment policy and simultaneous regular demand satisfaction. Numerical examples and sensitivity analysis are provided to illustrate the models. The two models are compared based on their annual variable costs. The results suggest that a trade-off between the rework and holding costs of defective items is necessary for better inventory decisions. Determining the optimal value of the length of the warm-up production period is necessary to minimize the annual variable cost.
... A combination of fuzzy logic and AHP was done in Wang and Elhag [5] and Labib [38]. A number of studies have discovered the business related to the combination of AHP and GP (e.g., [5,39]). Given that AHP is a mainstream strategy utilized as a part of taking care of MPEP, it confronts a few constraints including uneven scale judgments, instability, and imprecision in the pair-wise correlation process. ...
... Some more advanced maintenance strategies are also included in the decision models, including, for example, reliability-centered maintenance (RCM), proactive maintenance, or total productive maintenance (TPM). The use of fuzzy MCDM techniques is increasing in the literature, for example in [21][22][23][24][25][26][27][28][29][30]. ...
Systems that supply medicinal gases—oxygen, nitrous oxide and medical air—serve all care units of a hospital; for example, they feed distribution systems for operating theatres, neonatal and pediatric units, dialysis, X-ray, casualty, special tests, outpatients, etc. Systems for the provision of medicinal gases are therefore critical in guaranteeing hospital sustainability, since the functionality or availability of other hospital systems depends on them. Availability of 100% in these systems would avoid the need to reschedule patient appointments. It would also eliminate repeat testing, which poses risk to staff and patients, and could avoid affecting people’s lives through unavailability of, for example, operating theatres or intensive care units. All this contributes to a more rational resource consumption and an increase in quality of care both for the hospital itself and for patients and visitors. Although these systems are of vital importance to health care organizations, no previous work has been found in the literature that optimizes the technical decisions on supply in these systems. This research describes a model for these systems via continuous-time Markov chains. The results obtained are used in a multicriteria model constructed with the measuring attractiveness by a categorical-based evaluation technique (MACBETH) approach. In order to assess reliability when incorporating doubt or uncertainty via the MACBETH approach, the model has been validated by means of the fuzzy analytic hierarchy process. The aim is to obtain the best objective decision, with respect to the design of these systems, by analyzing the use of economic resources, the risks, and the impact on hospital activity, all with the aim of guaranteeing the best quality of care. The models constructed by means of MACBETH and the fuzzy analytic hierarchy process give, as the most suitable alternatives, duplicate the external supply in medical oxygen systems and maintain the original design conditions for supply systems of nitrous oxide and medicinal air.
... In seguito, molti altri autori sperimentarono sullo stesso tema l'utilizzo di combinazioni tra i diversi metodi: fra gli altri, Bevilacqua [15] integrò l'AHP con la ricerca operative per determinare la migliore tipologia di manutenzione in una raffineria petrolifera; Ilangkumaran [16] propose una combinazione di AHP e TOPSIS, per selezionare la migliore strategia manutentiva per un'industria tessile. Ghosh [17] introdusse un'integrazione di AHP, ricerca operativa e logica fuzzy; Chen [18] provò ad utilizzare l'AHP, il TOPSIS e il GRA (Grey Relational Analysis) per stimare le prestazioni e stabilire le migliori strategie manutentive in modo più preciso ed efficace per una compagnia di semiconduttori. Oltre a costoro, Vahdani [19] utilizzò il VIKOR per la selezione di una strategia manutentiva, e più tardi Ahmadi [20] utilizzò per lo stesso scopo una combinazione di VIKOR, AHP e TOPSIS. ...
... Generally, maintenance prioritization is the primary objective when assessing machine criticality (Bengtsson, 2011;Márquez et al., 2009). However, priorities can also be assigned with regard to reliability (Roy and Ghosh, 2010;Bevilacqua et al., 2009), PM (de León Hijes andCartagena, 2006), RM Wedel et al., 2016) and costs (Moore and Starr, 2006). Additionally, the productivity as an objective (Moss and Woodhouse, 1999;Stadnicka et al., 2014;Ni and Jin, 2012) and production scheduling quality (Petrovic et al., 2008) are also presented in the literature. ...
Purpose
The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.
Design/methodology/approach
An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.
Findings
The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.
Originality/value
Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.
... Furthermore, Chakraborty and Giri (2012) extended the precedent models and optimised the safety stock level. Ghosh and Roy (2009) and Ahmad and Kamaruddin (2012) demonstrated that appropriate maintenance could be properly planned before the predicted equipment condition reaches the failure limit. Desai et al. (2012) implemented different methods to solve the maintenance policy selection issue. ...
This paper addresses the problem of integration of maintenance and
quality control in manufacturing systems which are subject to a degradation
process that directly affects the quality of the produced items. Process and
product quality control is carried out using an ‘x-bar’ control chart. According
to the average of the measurements of the quality indicator x compared to
monitoring and control limits, it is decided to undertake or not a maintenance
action that can be either preventive or corrective. A mathematical model is
developed to determine the optimal values of the decision variables that are: the
sample size, the sampling interval, the number of samples, as well as the
monitoring and the control limits of the control chart. The objective is to
minimise the average total cost per time unit integrating maintenance and
quality costs. A numerical example and a sensitivity analysis are presented to
illustrate the proposed approach.
... The goals pursued with predictive maintenance also vary. Amongst others, approaches target the prediction of emerging defects (Woldman et al., 2015;Traore et al., 2015;Sayed et al., 2015;Peng et al., 2010), the Remaining Useful Life (RUL) of components (Prytz et al., 2015), the probability for exceeding a particular timeframe called prediction horizon (Prytz et al., 2015), and deriving decisions for maintenance actions (Galar et al., 2012;Ghosh and Roy, 2010;Huynh et al., 2015;Wang et al., 2010). As regards fields of application, next to industrial production predictive maintenance has focused aircrafts (Austin et al., 2003), railways (Umiliacchi et al., 2011), oil and gas operations (Nadj et al., 2016), military vehicles (Woldman et al., 2015), and electronic systems (López-Campos et al., 2013). ...
The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learn-ing have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive mainte-nance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized exten-sively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency.
... Devarun Ghosh et al. [7] performed transition from the systemic level to the equipment level by risk based criticality analysis (based on the worst-case failure mode) to identify critical equipment in a chemical process plant then they apply Goal Programming and fuzzy AHP to identify suitable maintenance strategies for that equipment. The incorporation of Goal Programming (GP) was to optimize multiple objectives such as risk reduction and cost minimization that are subject to resource constraints. ...
In today’s competitive world most of the problems composes multiple conflicting criteria’s. Therefore, it is necessary to use suitable multi criteria decision making (MCDM). Analytical hierarchy process (AHP) is one of the most widely used MCDM technique by researchers from around the globe due to its simplicity and versatility with higher accuracy. In order to systematize available information, this paper is an attempt to review the work conducted by various researchers in applications and improvement areas of AHP.
... Wang et al. (2007) presented the fuzzy AHP approach application for searching the best maintenance decision for boiler unit in a thermal power industry located in China. Ghosh and Roy (2009) presented the use of a fuzzy decision making framework to select the optimal mix of maintenance for various components of the considered process plant. Ilangkumaran and Kumanan (2009) expounded the application of combined fuzzy decision making approaches to decide upon the best maintenance strategy for spinning mill. ...
This paper proposes a novel integrated MCDM (multi-criteria decision-making) framework based on fuzzy AHP (Analytical Hierarchy Process) and a new fuzzy CODAS (Combinative Distance Based Assessment) approaches for solving the maintenance decision problem in a process industry. Under fuzzy AHP, a hierarchy structure related to the decision problem has been developed and the weights for different criteria and sub-criteria were computed using Geometric Mean (GM) method. These weights are further included in fuzzy CODAS approach to obtain the final ranking of the considered alternative maintenance strategies. Sensitivity analysis has also been performed for investigating the stability and validation of the proposed framework. The proposed framework was employed for selecting an optimal maintenance strategy for an Ammonia Synthesis Unit (ASU) of a urea fertilizer industry located in North India. © 2017, Bucharest University of Economic Studies. All rights reserved.
... Researches on technical asset-related activities have been treated extensively in the literature such as researches on maintenance and its strategy (Tsang, 1998(Tsang, , 2002Mather, 2005;Pinjala et al., 2006); performance measurement and optimisation (Dekker and Scarf, 1998;Garg and Deshmukh, 2006); replacement and remaining asset life determination (Jardine and Tsang, 2013;Zuashkiani and Jardine, 2013;Wijnia et al., 2007;Scarf et al., 2007;Oien, 1998), maintenance outsourcing (Cruz and Rinco, 2012;Wang, 2010;Martin, 1997;Buczkowski et al., 2005); maintenance process planning, task and task interval selection (Ghosh and Roy, 2010;Mckone and Weiss, 2002;Khan and Haddara, 2003); planning, scheduling and information management (Satyanarayana and Prasad, 1996;Nagarur and Kaewplang, 1999;Tsang et al., 2006). ...
A review of the concept of engineering asset management (EAM) and its role in integrity management within the context of energy pipelines has been conducted. The EAM system is shown to be concerned with pipeline integrity assurance at any point of the asset life. The effectiveness of a holistic EAM approach to assuring pipeline integrity is explored through case studies for pipelines that transport high pressure natural gas or liquid petroleum. The research examines the EAM system activities, data available and information flow and decision mechanisms utilised in industry and their effectiveness in incorporating the management or control of coating degradation and external corrosion into pipeline integrity management. The objective is to provide a holistic approach to defining the status of the EAM system that is in current use in energy pipeline organisations and to examine the role of EAM in integrity management of these pipelines.
... They then proposed a maintenance planning diagram to identify the adequate maintenance strategy for each device based on the total intensity score of the multi-criteria analysis and the risk priority index. For a related work but not specifically focusing on the medical equipment maintenance we refer to Ghosh et al. (2010). To the best of our knowledge, there is no procedure in the literature to find the criticality thresholds according to which the maintenance strategy of equipment switches from one strategy to another. ...
The maintenance department in a hospital is responsible for ensuring the safety of medical equipment and their availability while keeping the operation costs minimal. The selection of the best maintenance strategy is a key decision to reduce the equipment downtime, increase the availability, and bring down the maintenance costs. In this paper, we use an integrated approach that includes several tools from the literature, namely, the Analytical Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the mathematical optimization (especially mixed integer problems MILP) to provide the decision maker of the maintenance department with an entire solution to the problem at hand. These three tools are introduced to 1) determine the criticality of medical equipment based on a multi-criteria analysis, 2) rank the different maintenance strategies based on their (benefits) importance to the hospital and 3) select the optimal maintenance strategy for each device while keeping the total maintenance costs within a predetermined budget. We applied our approach to a case study at the Hospital of " Habib Bourguiba " in Tunisia, and the numerical results show the efficiency of our approach to improve the availability and the reliability of high risk medical devices.
... Frameworks used in railway maintenance decision making often include Corrective Maintenance (CM), Time-Based Preventive Maintenance (TBPM), and Condition-Based Predictive Maintenance (CBPM) using simulation and Goal Programming (GP)-fuzzy analytic hierarchy process (AHP). These three techniques support the decision making process by improving overall equipment availability and cost effectiveness (Ghosh & Roy, 2009). Other frameworks consider the errors in decision making methods by using Statistical Process Control, the Hidden Markov Model, and the Proportional Hazard Model. ...
... According to Sawhney et al. (2009), maintenance management has found new vigour and purpose to optimise equipment capacity and capability in the increasing competitive environment of industries, and tremendous efforts have been made to develop different types of maintenance strategies for enhancing the performance of equipment. Ghosh and Roy (2010) presented a multiple-criteria decision-making methodology for selecting the optimal mix of maintenance approaches -corrective maintenance, timebased preventive maintenance (PM) and condition-based predictive maintenance -for different equipment in a typical process plant. According to Rosmaini et al. (2011), PM strategies have been as well addressed in many studies. ...
This chapter describes the relevant steps for the design of a preventive maintenance program in an oil refinery plant and its application. The method was developed during a period of 3 years in one of the main Italian refinery.
... The maintenance process represents a key factor for the production in industrial processes (Ghosh and Roy, 2010;Pintelon and Gelders, 1992). Therefore, a powerful tool as the one proposed in this paper, allowing the evaluation of the maintenance performance, and helping decision makers, represents a strong advance on this field of industrial engineering. ...
In order to present a new methodology to help maintenance practitioners, owners and managers to develop and implement strategies for the management of their maintenance activities, based on the Gupta's Six Sigma Business Scorecard (SSBS) this paper proposes the theoretical principles of the Six Sigma Maintenance Scorecard (SSMS) as well as a case study. The proposed methodology, SSMS, represents a very efficient tool to evaluate the performance of maintenance activities and to adopt methodologies for continuous improvement. Moreover, by creating both performance and evaluation standards, as proved in the case study presented, the SSMS also allows the comparison between maintenance services.
... According to Sawhney et al. (2009), maintenance management has found new vigour and purpose to optimise equipment capacity and capability in the increasing competitive environment of industries, and tremendous efforts have been made to develop different types of maintenance strategies for enhancing the performance of equipment. Ghosh and Roy (2010) presented a multiple-criteria decision-making methodology for selecting the optimal mix of maintenance approaches -corrective maintenance, timebased preventive maintenance (PM) and condition-based predictive maintenance -for different equipment in a typical process plant. According to Rosmaini et al. (2011), PM strategies have been as well addressed in many studies. ...
This paper aims at describing the methodologies and the techniques used to manage an oil refinery scheduled maintenance (turnaround). In particular it will be discussed a risk based approach to define the equipment to be included in the turnaround process. Oil refinery turnaround main goal is to respect the maintenance schedule in terms of times and costs as defined by the management. Owing to the heavy impact of plants shutdown for controlling and repairing oil refinery equipments both in terms of production loss and of major maintenance costs it is of vital importance to clearly assess which maintenance task have to be included in the schedule. The application of the procedure to the planned turnaround has proved that a risk-based techniques decision support system leads to an improvement in maintenance efficiency and efficacy.
... In the maintenance environment, AHP has been applied in Labib (1998), Ramadhan et al. (1999), Bevilacqua and Braglia (2000), Kodali and Chandra (2001), Emblemsvag and Tonning (2003), Carnero (2005Carnero ( , 2007, Bertolini and Bevilacqua (2006), and Pramod et al. (2007). Fuzzy AHP has been applied for selecting the optimal mix of maintenance policies in Ghosh and Roy (2009) In a decision-making problem, n criteria C i (i = 1, 2, 3, …, n) and k alternatives A k (k = 1, 2, 3, …, k) are considered. To determine the relative importance of the alternatives with regard to each of the criteria or between two criteria, linguistic terms are used that include the judgements of the decision-maker. ...
Overall Equipment Effectiveness is a widely used and customary indicator for evaluating industrial performance. Overall Equipment Effectiveness (OEE) of a system is a metric that integrates availability, performance, and quality into a single dimension to assess how effectively a production process is functioning and what variables are holding it from getting the maximum efficiency. All control factors, i.e., availability rate, performance rate, and quality rate, are given an equal weight in the measurement of Overall Equipment Effectiveness. This assumption, however, may not be appropriate for each and every company. Analytic Hierarchy Process (AHP) methodology is employed in this study, to obtain the normalized OEE, which is significant for the company, by evaluating the relative significance of each performance index.KeywordsOverall equipment effectivenessTPMSix big lossAHP
The aim of this chapter is to select the most suitable combination of maintenance policies in the different systems that make up an operating theatre: air conditioning, sterile water, power supply, medicinal gases, and operating theatre lighting. To do so, a multicriteria model will be developed using the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) approach considering multiple decision centres. The model uses functional, safety, and technical-economic criteria, amongst which is availability. Mean availability for repairable systems has been measured to assess this criterion, using Markov chains from the data obtained over three years from the subsystems of a hospital operating theatre. The alternatives considered are corrective maintenance; preventive maintenance together with corrective maintenance by means of daily, weekly, monthly, and yearly programmes; periodical predictive maintenance together with corrective maintenance; and corrective together with preventive and predictive maintenance.
Decision-making regarding maintenance planning has become increasingly critical. In view of the need for more assertive decisions, methods, and tools based on failure analysis, performance indicators, and risk analysis have obtained great visibility. One of these methods, the Variation and Mode Effect Analysis (VMEA), is a statistically based method that analyses the effect of different sources of variations on a system. One great advantage of VMEA is to facilitate the understanding of these variations and to highlight the system areas in which improvement efforts should be directed. However, like many knowledge-based methods, the inherent epistemic uncertainty can be propagated to its result, influencing following decisions. To minimize this issue, this work proposes the novel combination of VMEA with Paraconsistent Annotated Logic (PAL), a technique that withdraws the principle of noncontradiction, allowing better decision-making when contradictory opinions are present. To demonstrate the method applicability, a case study analyzing a hydrogenerator components is presented. Results show how the proposed method is capable of indicating which are the failure modes that most affect the analyzed system, as well as which variables must be monitored so that the symptoms related to each failure mode can be observed, helping in decision-making regarding maintenance planning.
Nondestructive testing (NDT) is a vital tool in maintenance. Each NDT technique has some benefits and hindrances; therefore, the selection is crucial. Generally, the selection of a technique relies on operating personnel experience, and very few research papers shows uses of the decision-making (DM) approach. It was highlighted by various researchers that if a proper DM approach is used, it will save time and increase fault detection reliability. By keeping this fact in mind, this chapter is an attempt to provide a detailed review of research work from the year 2000-2018 that covered the role of DM techniques while making combinations of NDT for effective condition monitoring. It observed from the literature that very few researchers effectively utilized the power of DM tool. The researcher can use the outcome of this work as a beacon and improve it further.
Purpose
The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators.
Design/methodology/approach
First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined.
Findings
The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively.
Research limitations/implications
While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve.
Originality/value
While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.
Sustainable energy supply defined as uninterrupted, reliable, efficient, economic and environmentally friendly electricity generation is the main goal of power plants. Carrying out the proper maintenance processes has critical importance in terms of prolonging the effective operational lifetime of power plants and thus improving the sustainable power generation of the system. Since it serves for such an important purpose, maintenance is a crucial process that must be managed and selection of the most appropriate maintenance strategy is the first and unignorably stage of maintenance management in power plants as in other manufacturing facilities. Within this scope, this study focuses on the maintenance strategy selection problem in hydroelectric power plants have great importance for world and Turkey energy mix. As hydroelectric power plants comprise thousands of equipment with different characteristics, nine equipment which have similar effects and the most important ones for power plant are determined by The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under nine evaluation criteria weighted by the Analytic Hierarchy Process (AHP) for a big scale hydroelectric power plant in Turkey. Maintenance strategy combinations are obtained for each selected equipment via proposed goal programming (GP) model which uses the criteria weights and alternative priorities calculated with AHP and reflects the realities of power plant. Finally, it is determined that there is an improvement about 77% in downtimes arise from carrying out the improper maintenance strategy on selected critical equipment compared to the period when the model is not used.
The need of making appropriate decision in less time is arises day by day due to increasing competition, need of bottleneck production, high quality standards, awareness in customer and globalization. A small mistake may result in hefty production losses and reputation. Apart from this, generally a limited budget allocated to maintenance department.
Therefore, it is necessary to use decision making techniques to ensure right decision at right time instead of relying merely on experience. The objective of present work is to identify Critical Component of Compressor using improved preference ranking organization method for enrichment evaluations (PROMETHEE) to ensure maximum equipment availability for production in a heavy industry.
Maintenance is carried out to prevent the occurrence of events that lead to malfunction and interruption of the production process or the operation of the concerned equipment. One of the main approaches in maintenance is to identify the risk of equipment failure mode. Whereas by reducing the high risk of failure mode the reliability and availability of equipment enhanced and the cost of shutdown reduced, identifying the risk of failure is important. In this paper, a fuzzy hybrid approach, including failure mode and effective analysis (FMEA), decision-making trial and evaluation laboratory technique (DEMATEL), and analytic network process (ANP), is presented to select an appropriate maintenance policy through identifying the risk of failures. The authors aim to develop a risk-based method for selecting a proper maintenance strategy to have available and reliable tamping equipment in railway of Iran. Because of considering the same weight of risk factors, failure occurrence, failure severity, and failure detection ability in FMEA, the traditional FMEA method cannot correctly predict the system’s behavior. So, in this paper, the risk factors as fuzzy variables are proposed and evaluated using fuzzy linguistic terms and fuzzy ratings. In a case study, the present approach is utilized for evaluation and determination of the risk of failure modes for a railway company. At first, fuzzy FMEA is used to identify the main risk and sub-risk of failure modes. Second, fuzzy DEMATEL method is used in order to put forward the interrelationship among the main risks which are determined through fuzzy FMEA. Then, the weights of the sub-risks are calculated by fuzzy ANP approach on the basis of cause–effect relationships that are exposed through DEMATEL method. Finally, the weights of sub-risks are determined by multiplying the weights which have been obtained from fuzzy FMEA to the weights of ANP supermatrix. The weights of sub-risks were determined on the basis of these examined dependencies, and these weights were used for a tamping machine to determine maintenance policy. Finally, some strategies and suggestions have been drawn concerning the needs to reduce the risks and improve the equipment’s availability.
The aim of this article is to select the most suitable combination of maintenance policies in the different subsystems that make up an operating theatre. To do so a multicriteria model will be developed using the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) technique considering multiple decision centres: The Hospital's technical services, environment and occupational risk prevention managers, healthcare managers (operating theatres and health activity programming) healthcare staff, technicians, purchasing service managers and Hospital executives. The model uses functional, safety and technical-economical criteria, amongst which is availability. Mean availability for repairable systems has been measured to assess this criterion, using Markov chains from the data obtained over three years from the subsystems of a Hospital operating theatre. All this is aimed at increasing the operating theatre's availability and, consequently, increasing physical safety in patient operations and reducing the number of delayed operations due to technical malfunctions.
Among the activities managed by an organization, the maintenance of the resources it uses considerably affects sustainable performance. In this paper, we propose research on decision support for controlling sustainable performance induced by maintenance processes based on the core principles of decision systems. We discuss their application in maintenance, and underline the weaknesses of current practices in this domain. As we are particularly interested in key performance indicators, dashboards and prognosis approaches, we have reviewed the work on these subjects conducted by different scientific communities. This study allows us to propose a set of founding elements to conduct research on dashboards for sustainable performance in maintenance. Among these elements, we define sustainable value, sustainable signature, and sustainable state of the equipment. We suggest implementing such dashboards in Sustainable Condition-Based Maintenance (SCBM) based on Remaining Sustainable Life (RSL), and we propose a framework to conduct this research using a systemic approach according to the process of dashboard building.
This paper deals with a condition-based maintenance policy for single-unit production systems which generate damage to environment as they degrade and get older. The system is submitted to periodic inspections to assess the generated environmental damage. In case an inspection reveals that the environmental degradation level has exceeded a critical level, the system is considered in a failed state and will have generated significant environmental damage. It is stopped during a certain time necessary to perform a corrective maintenance (CM) action and to eventually clean the environment. In order to prevent such an undesirable situation, a lower threshold level is considered to trigger a preventive maintenance (PM) action which takes less time than the corrective one. A mathematical model and a numerical procedure are developed to determine simultaneously the PM threshold level and the inspection period which maximize the stationary availability of the system. Numerical calculations are performed to illustrate the proposed model.
Hydropower reservoirs are artificial water systems and comprise a small proportion of the Earth's continental territory. However, they play an important role in the aquatic biogeochemistry and may affect the environment negatively. Since the 90s, as a result of research on organic matter decay in manmade flooded areas, some reports have associated greenhouse gas emissions with dam construction. Pioneering work carried out in the early period challenged the view that hydroelectric plants generate completely clean energy. Those estimates suggested that GHG emissions into the atmosphere from some hydroelectric dams may be significant when measured per unit of energy generated and should be compared to GHG emissions from fossil fuels used for power generation. The contribution to global warming of greenhouse gases emitted by hydropower reservoirs is currently the subject of various international discussions and debates. One of the most controversial issues is the extrapolation of data from different sites. In this study, the extrapolation from a site sample where measurements were made to the complete set of 251 reservoirs in Brazil, comprising a total flooded area of 32 485 square kilometers, was derived from the theory of self-organized criticality. We employed a power law for its statistical representation. The present article reviews the data generated at that time in order to demonstrate how, with the help of mathematical tools, we can extrapolate values from one reservoir to another without compromising the reliability of the results.
Recession, render down economy and fold up of giant corporate houses are very frequent phenomenon now, having affect on society directly. These effects change the society badly in the form of sociological, economical and physiological changes. For the welfare of common people and self, industrialists have to espouse the newer and effectual technologies to minimise the risk of loosing the business. In industries, every equipment plays an imperative function and its malfunction leads to heavy cost, fearing of loosing the business forever. The present study to resolute the expenditure inference conjures up of maintenance of boilers at Sugar Plant. For this, a preemptive Goal Programming conjures up by considering foremost influencing factors. Any alteration in these factors, maintenance time also gets ostentatious. However, these factors are sublevelled more for an accurate conjecture of optimal maintenance time. Finally, the conjure up is worked out within 3% variation from the benchmark jobs involved.
Purpose
– Maintenance strategy selection (MSS) is considered as a complex multiple-criteria decision-making (MCDM) problem. The purpose of this paper is to present a comprehensive review on the use and application of MCDM approach and its associated case studies in the field of MSS.
Design/methodology/approach
– The paper systematically classifies the published literature of both researchers and practitioners and then analyzes and reviews it methodically.
Findings
– This paper outlines the important issues relevant to the subject, including the techniques used for data collection, the quantitative and qualitative criteria taken into account in decision making, the maintenance strategies considered for evaluation, the methods applied to find the solution, and the type of industries being studied. In each category, the gaps are identified along with recommendations for the future research work.
Practical implications
– Literature on classification of the MCDM models used to select the most appropriate maintenance strategy is very limited. The proposed classification scheme not only will be useful to researchers, but also assists maintenance professionals to find the models that fit their specific needs.
Originality/value
– The paper provides many references in the field, including the articles published in academic journals, conference papers, master and doctoral dissertations, text books, and industrial reports, and suggests a classification scheme according to various attributes.
The maintenance strategies optimization can play a key role in the industrial systems, in particular to reduce the related risks and the maintenance costs, improve the availability, and the reliability. Spare part demands are usually generated by the need of maintenance. It is often dependent on the maintenance strategies, and a better practice is to deal with these problems simultaneously. This article presents a stochastic dynamic programming maintenance model considering multi-failure states and spare part inventory. First, a probabilistic maintenance model called hidden semi-Markov model with aging factor is used to classify the multi-failure states and obtain transition probabilities among multi-failure states. Then, spare parts inventory cost is integrated into the maintenance model for different failure states. Finally, a double-layer dynamic programming maintenance model is proposed to obtain the optimal spare parts inventory and the optimal maintenance strategy through which the minimum total cost can be achieved. A case study is used to demonstrate the implementation and potential applications of the proposed methods.
Purpose
– The purpose of this paper is to primarily focus on labor in maintenance areas, addressing human rights issues, labor standards and safety standards. The main issue is to investigate how these factors are considered to drive the prioritization of maintenance interventions within maintenance plans. In particular, a method for criticality analysis of production equipment is proposed considering specific labor issues like age and gender, which can be useful to steer maintenance plans toward a more social perspective.
Design/methodology/approach
– The authors focus on the two main social issues of SA 8000 norms, age and gender, exploring how these issues may drive the selection of maintenance policies and the relative maintenance plans. The research is conducted through fuzzy analytical hierarchy process (AHP) implemented within a failure mode effects analysis (FMEA).
Findings
– The research is conducted through fuzzy AHP implemented within a FMEA. The maintenance plans resulting from the FMEA driven by social issues are evaluated by a benchmark of three different scenarios. The results obtained allowed the firm to evaluate maintenance plans, considering the impact on workers’ health and safety, the environment, social issues like gender and age.
Research limitations/implications
– One of the main limitation of this research is that it should also encompass maintenance costs under social and safety perspective. The method developed should be extended by further study of maintenance planning decisions subject to budget constraints. Moreover, it would be worth evaluating the effect of adopting more proactive maintenance policies aimed at improving plant maintainability in view of what emerged during the test case in the presence of an aged workforce and the subsequent need to prevent and/or protect people from hidden risks.
Practical implications
– With reference to the results obtained from the two models of this scenario, the authors observed an increase of equipment criticality, from B class to the A class, and similarly from C class to B class. No equipment has reduced its criticality. This depends on the particular context and the relative weights of drivers indicated in its AHP matrixes.
Social implications
– The paper addressed the main social implication as well as other social issues represented by age and gender factors, which are normally neglected. The Action Research (AR) proved the effects resulted from considering either gender factor or gender and age factors at the same time for maintenance policy selection. All in all, an increase of criticality is evident even if “people” is a driver with less importance than “environment” and “structures.”
Originality/value
– The present work focussed on a new definition of a criticality ranking model to assign a maintenance policy to each component based on workers’ know-how and on their status. The approach is conceived by the application of a fuzzy logic structure and AHP to overcome uncertainties, which can rise during a decision process when there is a need to evaluate many criteria, ranging from economic to environmental and social dimensions.
Acquiring assets that can be serviced cost effectively is a fundamental goal during large acquisition projects at NS, the largest railway company in the Netherlands. Buying passenger trains and providing their required services requires important strategic decisions involving both the trains and their technical service system. This thesis studies how serviceability is considered during acquisition projects in practice, and explores means to support decisions that intend to improve serviceability during such projects.
During acquisition of passenger trains, managers must specify requirements and make design decisions for both the trains and their service system. Trains are expensive, they are bought in large quantities, and have long life cycles. The service system requires investments in facilities, equipment and people. Design of passenger trains determines the service system needed. Design of the service system in turn determines the operational performance of the train. For a company such as NedTrain, the maintenance service provider of NS, it is the relationship with the supplier that creates success. Collaboration and partnerships are more important than the predictability of performance to produce a successful acquisition project. Before contracting, a dialog with the technical system owner and the system integrator, including the subsystem suppliers, helps NedTrain to mitigate risks and uncertainties. After contracting, close cooperation and communication are fundamental for the successful completion of a project.
This PhD research developed two approaches to support practice. Firstly, we developed and tested The Logistic Support Game to improve the service design process, and its associated decisions. This serious game is a tool that supports exploration of the design space of technical services at an early stage in the acquisition process. During early stages of acquisitions, more effort is needed for service concept development. Secondly, we developed a model to support experts in the definition of line replaceable units (LRUs). We identified this important problem and called it the LRU-definition problem. This is the problem of selecting which items to replace upon failure within the indenture structure of the asset. Our model leads to better LRU-definitions, and can lead to important cost savings when compared to heuristics found in practice.
Numerous of maintenance policies were developed due to the change in the manufacturing environment and the growing of technologies in the past few decades. Due to fluctuation (oscillation, instability) phenomena of the manufacturing industry, it is difficult to identify an optimal maintenance policy that actually suit for a manufacturing system. Thus, a lot of efforts have been done in order to assist manufacturing industry in finding an optimal maintenance policy. This paper attempts to review past and current research on optimal maintenance policy selection issues associated with methods used as well as the applications. Published literatures were systematically classified based on certainty theory in operation management classification model in term of certainty, uncertainty, and risk. Furthermore, a sub family had been classified based on the approaches used in determining the optimal maintenance policy. The possible gap occurred between academic research and industrial application in maintenance policy optimization is also discussed in detail, and several possible ideas are put forward to reduce the gap. More importantly, the paper is intended to provide a different view on classifying these models and give useful references for personnel working in industrial as well as researchers.
Advances in decision theory have allowed it to make an effective
contribution to the modelling of the decision-making process. Research
work on maintenance decision making using decision theory, however, has
received little emphasis to date. For this reason, very little has been
done in utilizing two very important decision theory topics, namely
utility theory and multi-attribute utility theory. Investigates possible
contributions from decision theory to the maintenance area and develops
a framework to solve maintenance decision problems. This framework
includes elicitation of both utility functions and prior probability
distributions, optimization and sensitivity analysis modules. Details
this framework and applies it to a real-life maintenance problem.
This paper reviews the main theories and methods used for multiple attribute decision making in a fuzzy environment. Fuzzy multiple attribute decisions involve two processes, the rating and the ranking of alternatives. If the rating results are crisp then the ranking procedure becomes straightforward; hence, the emphasis of this paper is on obtaining crisp ratings for alternatives. In order to aid the decision maker to express his/her attribute preferences, new elicitation techniques to determine attributes importance are proposed. These techniques range from statistical to scaling methods based on linguistic variables, and so enable a more versatile elicitation procedure as well as providing crisp preferences.
This paper presents a ‘Lexicographic’ Goal Programming (LGP) approach to define the best strategies for the maintenance of critical centrifugal pumps in an oil refinery.For each pump failure mode, the model allows to take into account the maintenance policy burden in terms of inspection or repair and in terms of the manpower involved, linking them to efficiency-risk aspects quantified as in FMECA methodology through the use of the classic parameters occurrence (O), severity (S) and detectability (D), evaluated through an adequate application of the Analytic Hierarchy Process (AHP) technique.An extended presentation of the data and results of the case analysed is proposed in order to show the characteristics and performance of this approach.
Maintenance policy optimization is concerned with determining when and to what extent major plant equipment items are to be taken off production for preventive maintenance. This paper considers the problem of determining optimal preventive maintenance policy parameters (e.g. effectiveness, frequency) for different items of equipment in multipurpose plants. Since preventive maintenance activities tend to be infrequent, a long-term view of the operation of the process must be taken. The procedure followed involves the formulation of a long-term combined production/maintenance aggregate planning problem, where the effect of failures on plant profitability are taken into account using the measure of expected resource availability as developed for continuous plants and modifying its use for applicability to the multipurpose mode of production. A variety of ways of formulating the problem as mixed integer non-linear or mixed integer linear programs are explored, and two examples are used to illustrate the ideas developed.
Applying an effective maintenance policy to a stochastically failing system is extremely important for reducing unwanted system failures and maintenance costs. This paper proposes a method of selecting a maintenance policy for a critical single-unit item in each workstation in a flexible manufacturing system (FMS) from among breakdown maintenance (BM), time-based maintenance (TBM) and condition-based maintenance (CBM). Each workstation in an FMS examined in this paper is assumed to have a critical single-unit item to maintain; therefore, we have multiple maintained items, which causes interactions among the items. The developed method can select the most appropriate maintenance policy for each individual maintained item.The selected policy may not always be the same for all of the items because the overall criticality of each maintained item in a workstation, which is dependent on the system configuration, maintenance parameters and demand, is different for each one. The proposed method uses efficient maintenance policy assignment procedures, in which makespan, opportunity loss and maintenance cost minimisation are systematically taken into consideration.Numerical simulation results are presented with varying system parameters of an examinedFMS.
This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper.
Continued pressure on companies to reduce costs and improve customer satisfaction has resulted in increasingly detailed examinations of maintenance practices and strategies. The justification of any given maintenance strategy or practice within an organisation must consider multiple criteria. It should also be based on the overall objectives of the organisation, many of which are ‘intangible’ or ‘non-monetary’. A fuzzy linguistic approach to achieve the inclusion of somewhat subjective assessments of maintenance strategies and practices in an objective manner is outlined in this paper. This approach is also demonstrated with two examples. Implementation of this approach will assist decision makers in the evaluation and selection of maintenance strategies and particular condition-monitoring techniques.
We assess the most popular maintenance approaches, i.e. strategies, policies, or philosophies, using a fuzzy multiple criteria decision making (MCDM) evaluation methodology. We illustrate with two examples how the suggested evaluation methodology identifies the most informative approach. Using the fuzzy MCDM, it would be possible to select in advance, the most informative (efficient) maintenance approach. Consequently, this leads to less planned replacements, and failures would be reduced to approximately zero and higher utilization of component life can be achieved. Thus, the maintenance department could contribute more to the business objectives throughout participating effectively in adding value to the production activities.
This paper aims to evaluate different maintenance strategies (such as corrective maintenance, time-based preventive maintenance, condition-based maintenance, and predictive maintenance) for different equipment. An optimal maintenance strategy mix is necessary for increasing availability and reliability levels of production facilities without a great increasing of investment. The selection of maintenance strategies is a typical multiple criteria decision-making (MCDM) problem. To deal with the uncertain judgment of decision makers, a fuzzy modification of the analytic hierarchy process (AHP) method is applied as an evaluation tool, where uncertain and imprecise judgments of decision makers are translated into fuzzy numbers. In order to avoid the fuzzy priority calculation and fuzzy ranking procedures in the traditional fuzzy AHP methods, a new fuzzy prioritization method is proposed. This fuzzy prioritization method can derive crisp priorities from a consistent or inconsistent fuzzy judgment matrix by solving an optimization problem with non-linear constraints. A specific example of selection of maintenance strategies in a power plant with the application of the proposed fuzzy AHP method is given, showing that the predictive maintenance strategy is the most suitable for boilers. As demonstrated by this case study, the fuzzy AHP method proposed in this paper is a simple and effective tool for tackling the uncertainty and imprecision associated with MCDM problems, which might prove beneficial for plant maintenance managers to define the optimum maintenance strategy for each piece of equipment.
In today's process industry environment, it is becoming more and more important for companies to manage the risks associated with their plants. Amongst others, some reasons for this are that 1) Process Safety is featuring high on the agenda of Trade Unions; 2) that Management is coming under increased pressure to provide a safe workplace; 3) that Companies are trying to survive in the current competitive environment by adopting the “zero accidents” ideal; 4) because the effects of accidents that do occur are becoming more devastating due to increased inventories and the exotic nature of products and 5) the fact that overseas companies are looking at safety and conservation track records before choosing business partners or considering trade agreements.
We present a modification of the fuzzy multi-criteria method proposed by Van Laarhoven and Pedrycz (1983). First the weights of the decision criteria are calculated by the minimization of a logarithmic regression function. Next the weights of the decision alternatives are calculated for each criterion separately. Lastly, the fuzzy final scores of the alternatives are determined by an appropriate aggregationm of the calculated weights. We will show that our modified method, in contrast with the original one, yields weights which are optimal with respect to the logarithmic regression function.We also present a version of our method with geometric ratio scales, rather than Saaty's equi-distant scale, to quantify the gradations of human comparative judgement. This approach also enables us to develop an efficient procedure for investigating the scale sensitivity of the calculated weights and the final scores of the alternatives.
This paper proposes a new approach for tackling the uncertainty and imprecision of the service evaluation process. Identifying suitable service offers, evaluating the offers and choosing the best alternatives are activities that set the scene for the consequent stages in negotiations and influence in a unique manner the following deliberations. The pre-negotiation problem in negotiations over services is regarded as decision-making under uncertainty, based on multiple criteria of quantitative and qualitative nature, where the imprecise decision-maker’s judgements are represented as fuzzy numbers. A new fuzzy modification of the analytic hierarchy process is applied as an evaluation technique. The proposed fuzzy prioritisation method uses fuzzy pairwise comparison judgements rather than exact numerical values of the comparison ratios and transforms the initial fuzzy prioritisation problem into a non-linear program. Unlike the known fuzzy prioritisation techniques, the proposed method derives crisp weights from consistent and inconsistent fuzzy comparison matrices, which eliminates the need of additional aggregation and ranking procedures. A detailed numerical example, illustrating the application of our approach to service evaluation is given.
This paper describes an application of the Analytic Hierarchy Process (AHP) for selecting the best maintenance strategy for an important Italian oil refinery (an Integrated Gasification and Combined Cycle plant). Five possible alternatives are considered: preventive, predictive, condition-based, corrective and opportunistic maintenance. The best maintenance policy must be selected for each facility of the plant (about 200 in total). The machines are clustered in three homogeneous groups after a criticality analysis based on internal procedures of the oil refinery. With AHP technique, several aspects, which characterise each of the above-mentioned maintenance strategies, are arranged in a hierarchic structure and evaluated using only a series of pairwise judgements. To improve the effectiveness of the methodology AHP is coupled with a sensitivity analysis.
A new approach for deriving priorities from fuzzy pairwise comparison judgements is proposed, based on α-cuts decomposition of the fuzzy judgements into a series of interval comparisons. The assessment of the priorities from the pairwise comparison intervals is formulated as an optimisation problem, maximising the decision-maker's satisfaction with a specific crisp priority vector. A fuzzy preference programming method, which transforms the interval prioritisation task into a fuzzy linear programming problem is applied to derive optimal crisp priorities. Aggregating the optimal priorities, which correspond to different α-cut levels enables overall crisp scores of the prioritisation elements to be obtained.A modification of the linear fuzzy preference programming method is also proposed to derive priorities directly from fuzzy judgements, without applying α-cut transformations. The formulation of the prioritisation problem as an optimisation task is similar to the previous approach, but it requires the solution of a non-linear optimisation program. The second approach also derives crisp priorities and has the advantage that it does not need additional aggregation and ranking procedures.Both proposed methods are illustrated by numerical examples and compared to some of the existing fuzzy prioritisation methods.
In this paper we give an overview of applications of maintenance optimization models published so far. We analyze the role of these models in maintenance and discuss the factors which may have hampered applications. Finally, we discuss future prospects.
Chemical process reliability has become more recognized both in terms of its impact on economics, and for providing academically challenging problems. In this work, we give an overview of some of the major challenges in formulating and optimizing preventive maintenance. As a result, we propose a general framework for preventive maintenance optimization that combines Monte Carlo simulation with a genetic algorithm.This proposed approach has distinct advantages. When applied to opportunistic maintenance problems, the method developed overcomes demonstrated shortcomings with analytic or Markov techniques in terms of solution accuracy, versatility, and tractability. The framework is easily integrable with general process planning and scheduling, and it provides sensitivity analysis. Furthermore, a genetic algorithm combines well with Monte Carlo simulation to optimize a non-deterministic objective function.