Article

A simulation-Optimization Based Heuristic for the Online assignment of Multi-Skilled Workers Affected by Fatigue in Manufacturing Systems

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Abstract

Manufacturing systems are often characterized by a stochastic and uncertain behavior in which frequent changes and unpredictable events may occur over time. Moreover, the customers’ demands can sometime evolve drastically along time. In order to cope with such changes in the manufacturing system state, and to optimize given performance criteria, the assignment of multi-skilled workers to the machines in the system can be decided online, in a dynamic manner, whenever workers become available and need to be assigned. Indeed, the starting and completion times of jobs in such systems cannot be predicted, so that static optimization approaches turn out not to be relevant. Several studies, in the ergonomics literature, have outlined that the operators' performances often decline because of their fatigue in work. In particular, in manufacturing contexts, fatigue can increase the processing times of jobs. Several online heuristic have been published, but to the best of our knowledge, they do not cope with this consequence of fatigue. We propose to solve this dynamic multi-skilled workers assignment problem using a new methodology, which aims to provide an adaptable dynamic assignment heuristic, which is used online. Our approach takes the impact of fatigue into consideration, in order to minimize the mean flowtime of jobs in the system. We suggest computing more realistic task durations, in accordance with the worker's fatigue. The heuristic uses a multi-criteria analysis, in order to find a compromise that favors short processing times and avoids congestions. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select the machine where to assign the worker. Since in our case no expertise is available, an offline adaptation process, based on simulation optimization, is used to identify the weights needed by TOPSIS, so as to better fit with the system specificities. A Job-Shop systems are simulated to illustrate the proposed approach. The performance of the suggested heuristic is assessed and compared to two other workers assignment rules, which are widely used in the scientific literature because of their efficiency on the mean flowtime: SPT and LNQ. The comparisons are made under different conditions (staffing level, operators’ flexibility). A sensitivity analysis is also performed to analyze the impact of the way how fatigue affects the task duration. Our experimental results show that our heuristic provides better results in every case studied. Several important research directions are finally pointed out.

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... D'après l'étude bibliographique que nous avons menée sur les problèmes d'affectation dynamiques d'opérateurs (Ferjani et al. (2017)), nous avons constaté que les règles d'affectation de type "When" et "Where" sont les plus utilisées dans la littérature (Ammar et al. (2013)). Les règle d'affectation de type "when" sont utilisés pour gérer la fréquence de déplacements et la durée d'affectation d'un opérateur alors que les règles de type "where" permettent de choisir la machine à laquelle l'opérateur libre va être affecté (Xu et al. (2011)). ...
... En revanche, dès que l'effet de la fatigue devient important, nous remarquons que la règle LNQ devient significativement plus performante.Contrairement aux résultats obtenus dans(Salum et Araz (2009)), où les auteurs ont montré à travers des comparainsons de règles d'affectation que SPT est la règle la plus performante sur le MFT, nous avons pu constater que, particulièrement dans notre exemple, lorsque l'effet de la fatigue devient sévère, le critère de la règle LNQ (celui de la quantité de charge en attente sur les machines) devient plus important que les autres critères considérés. Ces résultats confirment également les résultats que nous avons obtenus dans un autre travail réalisé hors du cadre de cette thèse(Ferjani et al. (2017)).Nous avons également pu constater que, particulièrement dans cet exemple, il semble que la règle SWT est la moins efficace sur le MFT. En effet, nous avons remarqué que, quel que soit la valeur de δ, l'application de la règle SWT donne toujours le performance le moins faible. ...
... Modélisation, Quantification et Évaluation de la Fatigue et de ses Impacts dans les Systèmes de ProductionBien que les chercheurs se sont concentrés principalement sur le développement des modèles mathématiques pour l'estimation du MET, il est important de noter que ces modèles sont limités dans le sens où ils prédisent la "fatigue maximale"sous une charge f M V C donnée, mais ne fournissent aucune indication sur la forme de la fonction d'accumulation de fatigue ou revèlent l'état de fatigue au cours de l'exécution de la tâche(Jaber et al. (2013);Ferjani et al. (2017)).Figure 2.1 -Liste des modèles de MET dans la revue de la littérature réalisée par Imbeau et al. (2006). En outre, les modèles de MET existants sont souvent concentrés sur le travail statique où les travailleurs répètent les même tâches durant leur travail. ...
Thesis
Le développement rapide des technologies de l’information dans l’usine du future nécessite de repenser l’organisation des systèmes de production. Si l’importance des opérateurs est bien reconnue dans l’industrie 4.0, les facteurs humains restent cependant insuffisamment pris en considération dans les méthodes d’organisation. La simulation est parmi les méthodes largement utilisées pour étudier les systèmes de production et évaluer leurs performances. Toutefois, les approches de simulation existantes et les logiciels disponibles sont centrés sur les dimensions techniques de ces systèmes (machines, transport, règles de priorité, etc.). Malheureusement, les facteurs humains, particulièrement la fatigue des opérateurs, semble être très insuffisamment pris en compte dans les modèles de simulation. Dans ce contexte, la présente thèse vise à étudier dans quelle mesure la fatigue pourrait être intégrée dans les modèles de simulation des ateliers de production. Un cadre conceptuel combinant différents paradigmes de modélisation est ainsi proposé pour aider à concevoir de tels modèles. Le paradigme de simulation à événements discrets est utilisé pour décrire la dynamique de fonctionnement des ateliers. Le paradigme d’agent intelligent est utilisé pour modéliser les opérateurs et intégrer leur comportement de fatigue que nous avons modélisé en utilisant la simulation continue. Nous avons également utilisé la logique floue pour caractériser les facteurs de pénibilité, connus de façon imprécise, et leur impact sur l’évolution de la fatigue. Nous avons montré comment le cadre conceptuel proposé peut être mis en œuvre de façon assez générique avec le logiciel Anylogic. L’approche proposée a ensuite été utilisée pour aborder deux différents problèmes de production: le premier concerne l’affectation dynamique des opérateurs sur les machines et le deuxième est relatif à l’agencement de machines lors de la phase de conception des ateliers. Ceci nous a permis d’illustrer l’utilité de l’approche propose et montrer sa pertinence. L’analyse des résultats de simulation permet d’apporter des connaissances à la fois sur les performances des systèmes et sur les tendances d’évolution de la fatigue des opérateurs durant le travail. De tels résultats aident à réfléchir sur la façon dont laquelle nous pourrions améliorer les méthodes d’organisation et de gestion des ateliers pour contribuer à l’amélioration de leurs performances et au bien-être de leurs opérateurs.
... The advancements in automation, computation, information, sensing, and expert systems are changing the landscape of jobs and workplaces at unprecedented speeds ( National Science Foun- ( Ferjani, Ammar, Pierreval, & Elkosantini, 2017 ) who can capitalize on the technological revolution. The industry is moving towards a future that will be defined by how it optimizes its three main resources ( Daugherty & Wilson, 2018;Kong, Luo, Huang, & Yang, 2018;Pacaux-Lemoine, Berdal, Enjalbert, & Trentesaux, 2018 ): human workers, machines , and supporting technologies (e.g., artificial intelligence, high performance computing, and expert systems). ...
... Furthermore, we have chosen to focus on advanced manufacturing tasks since: (a) changes in work performance is task/field dependent; and (b) advanced manufacturing jobs are highly fatiguing despite the increased prevalence of automation ( Kajimoto, 2008;Loriol, 2017;Lu et al., 2017;Yung, 2016 ). The high prevalence of fatigue at manufacturing workplaces can be explained by the transformations in labor roles, where the following changes have been observed: (a) a reduction in mundane tasks ( Yakowicz, 2016 ), (b) an increased dependency on highly-trained workers ( Ferjani et al., 2017 ), (c) an increase in worker's autonomy and responsibility ( Waldeck, 2014 ), and (d) the introduction of new job duties ( Waldeck, 2014 ). ...
... A framework is proposed instead of a model to allow for the detection/diagnosis of multiple fatigue modes. The main premise is that advanced manufacturing firms require specialized labor ( Ferjani et al., 2017;Lu et al., 2017 ). Thus, the jobs can then be grouped by the type of activities. ...
Article
The use of expert systems in optimizing and transforming human performance has been limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue accumulation, which can vary based on both the worker and the workplace conditions. As a first step toward realizing the human-centered approach to artificial intelligence and expert systems, this paper lays the foundation for a data analytic approach to managing fatigue in physically-demanding workplaces. The proposed framework capitalizes on continuously collected human performance data from wearable sensor technologies, and is centered around four distinct phases of fatigue: (a) detection, where machine learning methodologies are deployed to detect the occurrence of fatigue; (b) identification, where key features relating to the fatigue occurrence is to be identified; (c) diagnosis, where the fatigue mode is identified based on the knowledge generated in the previous two phases; and (d) recovery, where a suitable intervention is applied to return the worker to mitigate the detrimental effects of fatigue on the worker. Moreover, the framework establishes criteria for feature and machine learning algorithm selection for fatigue management. Two specific application cases of the framework, for two types of manufacturing-related tasks, are presented. Based on the proposed framework and a large number of test sets used in the two case studies, we have shown that: (i) only one wearable sensor is needed for fatigue detection with an average accuracy of ≥ 0.850 and a random forest model comprised of < 7 features; and (ii) the selected features are task-dependent, and thus capturing different modes of fatigue. Therefore, this research presents an important foundation for future expert systems that attempt to quantify/predict changes in workers’ performance as an input to prescriptive rest-break scheduling, job-rotation, and task assignment models. To encourage future work in this important area, we provide links to our data and code as Supplementary Materials.
... The fatigue model has been proposed in some studies. Ferjani et al. proposed the human fatigue model to solve the problem of worker assignment and improve the performance of the entire system [24]. In the literature [25], the human fatigue-recovery model is integrated into the optimization model, which gives some spare boxes and determines the size of the boxes to minimize the cost. ...
... In the disassembly process, the disassembly efficiency of robots is constant, while the disassembly efficiency of the human worker inevitably decreases with the disassembly time due to the fatigue accumulation. It has been proven in the literature that human processing time is a logarithmic function of fatigue [24,[28][29]. In this paper, Eq. (6) is used to describe the relationship between processing time and human fatigue level. ...
... Assume that the time for manual-related disassembly operations is T 0 when the human fatigue level is 0, λ and F (t) are the same with those in Eq. (5). δ represents the effect of human fatigue level on processing time [24] which is related to the intensity of manual work. ...
Article
Full-text available
Disassembly, which plays an essential role in remanufacturing, is the first step to extend the service life of end-of-life (EOL) products. Traditional disassembly is always accomplished by either humans or robots. Manual disassembly is a time-consuming process, and the high labour intensity will also pose a threat to human health, while robotic disassembly is difficult to flexibly handle complex parts. Continuous manual work leads to the accumulation of fatigue, which decreases the efficiency of manual work. In this paper, sequence planning considering human fatigue for human-robot collaboration in disassembly (HRCD) is proposed. This method involves assigning disassembly task to human and robot according to their respective characteristics, models for HRCD considering human fatigue is established. In the case of disassembling batches products with the same type, discrete Bees algorithm is used to obtain the optimal disassembly sequence to minimize the total disassembly time. Case studies based on gear pumps show that the proposed algorithm outperforms the other two optimization algorithms in solution quality.
... Besides learning and forgetting, also fatigue and recovery significantly impact processing times (Jaber and Neumann 2010; Givi 2014, 1;Ferjani et al. 2017). Astonishingly, within operations management models, fatigue and recovery are very seldom considered ( Givi 2014, 19). ...
... Astonishingly, within operations management models, fatigue and recovery are very seldom considered ( Givi 2014, 19). Fatigue is the classical antagonist of learning: grouping jobs stimulate learning, but the inherent repetitiveness also creates fatigue which leads to deterioration effects (Jaber and Neumann 2010; Givi 2014, 30;Ferjani et al. 2017). Fatigue increases with time ( Givi 2014, 10), but can be completely removed by resting from work (Givi, Jaber, and Neumann 2015). ...
... As explanatory factors we use the aforementioned human effects of learning, forgetting, fatigue and recovery. Given that fatigue is influenced by time (Ferjani et al. 2017) and learning by the number of products already produced (Biskup 2008), a combination of auxiliary variables is required. Learning is captured by the cumulative volume (Biskup 1999), forgetting by time as it is mainly influenced by the interruption duration (Sikström, Jaber, and Neumann 2016, 164) and both fatigue and recovery by time (Schlick, Bruder, and Luczak 2010, 195). ...
Article
There is an increasing awareness in scheduling research that human behaviour needs to be considered explicitly in scheduling models. Although most scheduling literature ignores human behaviour, especially sequence-dependent processing times form a good basis for explicit consideration. Hence, a processing time function is derived that considers the effects of learning, forgetting, fatigue and recovery. The necessity for explicit human consideration can be regarded as most urgent for unpaced highly-manual mixed-model assembly lines. Based on real data a simulation study is conducted to determine the effect of explicit human consideration while also taking into account the effects of different idealised schedule types and the product mix. The results strongly indicate that the product mix has a consistently high impact on scheduling objectives, the schedule type affects lower-level objectives like starving and blocking times to a greater extent than higher-level objectives like makespan and flow time, and that for certain objectives the height of the objective values and the relative favourability of schedule types depends on human consideration.
... A group of research (e.g. Ferjani, Ammar, Pierreval, & Elkosantini, 2017;Ji, Lan, & Looney, 2006;Landau, Rohmert, & Brauchler, 1998) considers that fatigue increases/decreases exponentially over time, while others (Jaber & Neumann, 2010;Soo, Nishino, Sugi, Yokoi, & Ota, 2009) assume it increases/decreases linearly. Nevertheless, to date, no empirical or experimental study has confirmed the result. ...
... gender, age, physical capacity, health condition) (Kent Braun, Ng, Doyle, & Towse, 2002). Recent research by Ferjani et al. (2017) consider the maneuverability of machines as a major factor contributing to fatigue, and employ a penalty coefficient associated with machines to model the fatigue-recovery behavior. However, the physiological aspect is seldom addressed in previous work. ...
... greater value means higher speed of fatigue accumulation and recovery alleviation. Ferjani et al. (2017) considers the machine as a major factor contributing to fatigue accumulation. However, the physical condition of workers (such as age, health) also has a great impact on the speed of fatigue accumulation (Kent Braun et al., 2002). ...
Article
Dual resource constrained flexible job shop scheduling problem (DRCFJSP) becomes a hot research topic in recent years. However, worker fatigue is barely considered in DRCFJSP, which will result in unsuitable task assignments for workers and bring about negative effects such as muscle fatigue and cognitive confusion. To this end, a fatigue-conscious DRCFJSP is proposed in this paper, aiming at simultaneously relieving fatigue and promoting productivity by joint scheduling of machines and workers. A multi-objective optimization model is constructed to minimize the maximum worker fatigue and makespan. Furthermore, an enhanced NSGA-II (ENSGA) is developed in this paper to cope with the proposed problem. In the ENSGA, four dispatch rules are designed to generate high-quality solutions at the initialization stage. Two neighborhood structures based on a novel method of defining the critical path are designed to strengthen the ability of local search. Finally, with the comprehensive experiments on extensive test instances and a case study from real-world casting workshop, the effectiveness of proposed ENSGA for solving the fatigue-conscious DRCFJSP is verified, and the significance of the proposed joint scheduling approach is elucidated. This work renders the decision-maker practical and effective scheduling with fatigue-conscious for the machine and worker constrained flexible manufacturing system.
... The manufacturing capacity and performance depend on the number of employees and their skills. Unlike machinery, human labor is more uncertain and fluctuates more, making the manufacturing systems generally more unpredictable (Ferjani, Ammar, Pierreval, & Elkosantini, 2017). Second, SMEs often achieve their competitiveness by offering craftsmanship, flexibility, and better service to the market (Bär, Herbert-Hansen, & Khalid, 2018). ...
... The simulation-optimization approach's value as a means for the extensive use of modeling and decision support tool is broadly recognized, especially for highly complex systems, such as those discovered in automotive, aerospace, electronics, and semiconductor industries (Mourtzis, 2020). Manufacturing and supply chain systems are often characterized by stochastic and uncertain behaviors (Ferjani et al., 2017). The characteristics of these large, complex systems are NP-Hard in their nature (Danilovic & Ilic, 2019;Motlagh, Azimi, Amiri, & Madraki, 2019). ...
Article
Manufacturing capacity planning is one of the critical processes in every manufacturing company, and, with increasing exploitation of data and information technology, has necessarily become more efficient than before. However, the power to harness data and information for planning requires specific knowledge and resources, mostly limited to large enterprises. Small and medium-sized enterprises (SMEs) generally do not have sufficient resources to collect large amounts of data or the know-how to process and exploit data. Moreover, SMEs often fail to implement advanced techniques and tools (e.g., optimization tools or enterprise resource planning (ERP) software), owing to the cost and a lack of specific knowledge and personnel. This paper proposes a solution for reducing the burden on SMEs in collecting and utilizing data for the planning of manufacturing capacity. A simulation-optimization approach is adopted because of the complex nature of labor-intensive manufacturing in SMEs. The approach includes an artificial neural network for model simulation and data relationship recognition, combined with a genetic algorithm for optimizing manufacturing resource configuration. The proposed method can facilitate the process of planning manufacturing capacity for different yield targets, as tested in a case study of a pastry company and providing the means for the company to exploit both empirical and observational data for the purpose.
... The main purpose of scheduling is to organize the manufacturing process in the best way that a performance function is optimized and existing constraints of the manufacturing environment are respected (Pinedo, 2005). Such performance indicators can be expressed in different ways such as flow time, work in progress and throughput (Digiesi et al., 2009), but also makespan, total weighted completion time, maximum lateness, and the total number of tardy jobs (Ferjani et al., 2017). However, most of them are profit/cost oriented. ...
... The literature analysis shows that except the heuristic developed in Ferjani et al. (2017) and used to minimize the flowtime, no scheduling procedure takes into consideration the fact that operators' performances often decline because of the fatigue generated during task execution. ...
Article
Human factors are often ignored in scheduling algorithms despite the fact that the majority of manufacturing systems still employ human operators. In particular, ergonomic studies shown that human fatigue has an important impact on worker performance and as a consequence it should be taken into account in the modelling of the system performance. This study investigates the problem of the integration of accumulated human fatigue into scheduling algorithms. A new optimization problem is defined and several constructive heuristics are developed to solve it. Their performances are evaluated through a numerical experiment. The conclusions of this analysis and future research directions are discussed.
... The products could exceed their development deadline, and the company faces losses in overhead costs. Since workers get tired when they process jobs, their performance declines over time because of their fatigue, so that they can become slower and spend more time than expected in handling their assigned tasks [24]. Furthermore, all the devices needed for the assembly must be portable, as the fixed-position layout design operation was carried out. ...
... Manufacturing systems usually characterised by a stochastic and uncertain behaviour in which frequent changes and unpredictable events may occur over time [24]. The ultimate basis for all job creation is and must be the manufacturing industry. ...
Article
Full-text available
Efficiencies and the productivity of the assembly line are crucial in the manufacturing sector. It is very unusual opportunity to visualise and analyse the production system, which used in defence manufacturing sector. This research study focuses on the performance of an existing production line for Malaysia's automotive defence manufacturing industry. The main issues that arise are first, the delivery is always behind the schedule and second, the human factor that contributes to the increase of rejected parts and slow down the production line. WITNESS simulation will be utilised to analyse the dynamic issues associated with the whole performance of the manufacturing system. A methodology for production layout improvement will bring into notice. DELMIA simulation can improve employee's working condition, which is to optimise the production line efficiency. The assembly line can be better in many ways, for example, the arrangement of working layout, the summit of the workplace and massive machines handling method by the worker. All of these are imperative to increase the efficiency of the employees. Continuous improvement of the proposed methodology includes progress in model design, training of operators, follow-up of implementing changes and investigations in the measurement of manufacturing line efficiencies.
... Assignment problems consist to find a correspondence one-to-one, for example, a set of tasks to a set of agents, goods to vehicles, etc. According to Ferjani [6], the resolution of these industrial problems allows the company to improve their performance. From the literature review, we observe two categories of this problem: static and dynamic allocation. ...
... The constraint (5) ensures that each command is assigned to a single record in a single installation. The constraint (6) ensures that the lockers are only reserved for the storage period. In other words, no locker is reserved for a given parcel, during the periods preceding his arrival and those which follow his departure. ...
... Assignment problems consist to find a correspondence one-to-one, for example, a set of tasks to a set of agents, goods to vehicles, etc. According to Ferjani [6], the resolution of these industrial problems allows the company to improve their performance. From the literature review, we observe two categories of this problem: static and dynamic allocation. ...
... The constraint (5) ensures that each command is assigned to a single record in a single installation. The constraint (6) ensures that the lockers are only reserved for the storage period. In other words, no locker is reserved for a given parcel, during the periods preceding his arrival and those which follow his departure. ...
... In addition, some scholars pointed out that fatigue can degrade workers performance on tasks in production reflected by the change of task duration [48][49][50][51][52]. Digiesi et al. showed that workers spend more time to process jobs when they become tired and slower [49]. ...
... In addition, some scholars pointed out that fatigue can degrade workers performance on tasks in production reflected by the change of task duration [48][49][50][51][52]. Digiesi et al. showed that workers spend more time to process jobs when they become tired and slower [49]. Ferjani et al. used a new methodology about workers fatigue, an adaptable dynamic assignment heuristic, to solve multiskilled workers assignment problem in the manufacturing system state [52]. In particular, the authors indicated that fatigue can increase the processing times of jobs; namely, workers may need more time than expected to process their tasks. ...
Article
Full-text available
Aiming at production environment and operation design in manual materials handling which often overlook workers’ physiological factors and cause fatigue, even work-related musculoskeletal disorders, we construct a biobjective model based on economics and ergonomics. In the model, two objectives include functions about handling time and energy consumption. Based on the openness of IGRIP/ERGO simulation software combined with MATLAB, we design and develop the interactive simulation platform, where program language can be automatically generated. Then, we analyze the case about handling operations in an automobile brake pad manufacturing company, and the number of input materials and process scheduling are taken as research objects. Finally, the results show that the win-win optimal solution can be usually obtained between productivity and ergonomics for decision makers according to the proposed biobjective model. Moreover, the case study demonstrates that the interactive simulation platform can be devoted to providing the solution for modern production operation directly and conveniently, which can make the production environment and operation design in accordance with ergonomics.
... Many former studies in this area dealt with solving the CF problem. A complete review of the works related to CF can be found in [2][3][4][5][6] . Moreover, the two problems of GL and GS are investigated through the provided CMS framework in order to reach a real-world optimal manufacturing system. ...
... His algorithm considered several important cell design factors like inter-intra cell material handling, machine and worker assignments, outsourcing, and workload balancing according to operational time and parts operation sequences. Ferjani et al. [3] proposed a new methodology aiming at providing an adaptable, dynamic assignment heuristic which has been used online to solve the dynamic multi-skilled workers' assignment problem. Moreover, they investigated the impact of fatigue in the optimality of the model. ...
Article
One of the most critical issues in manufacturing systems is the operator management. In this paper, the operator assignment problem is studied within a cellular manufacturing system. The most important novelty of this research is the consideration of operator learning and forgetting effects simultaneously. The skill level of an operator can be increased/decreased based on the time spent on a machine. Moreover, the issues related to operators like hiring, firing, and salaries are considered in the proposed model. The parameters are considered to be uncertain in this model, and a robust optimization approach is developed to handle it. Using this approach, the model solution remains feasible (or even optimal) for different levels of parameter uncertainty. To verify and validate the proposed model, some numerical instances are randomly generated and solved using GAMS. A statistical analysis is also conducted on the results of the objective function values of linear and nonlinear models, followed by some managerial insights. Download link until May 10, 2022: https://authors.elsevier.com/a/1enKq,703qAdJw
... lights out manufacturing facilities); however, advanced manufacturing aims to integrate workers into the cyber-physical infrastructure to maximize the impact of their skills (Gorecky et al., 2014). Second, it is well documented that automation can lead to: (a) reducing repetitive, mundane and dangerous work (see e.g., Kelly, 2012;Thompson, 2014;Yakowicz, 2016); (b) increasing the dependency on multi-skilled workers who can simultaneously work multiple workstations, which originated with the creation of U-shaped cells in lean manufacturing (Black and Phillips, 2013) and became more prominent with automation (Ferjani et al., 2017); and (c) broadening the workers' autonomy and responsibility as well as requiring new job duties (Waldeck, 2014). Third, the advancements in computation and sensing technologies is leading to smart factories, where workers will respond to mass-customized products (Hu, 2013) and have to be able to process and act upon large amounts of information. ...
... There is an increasing amount of literature suggesting that the increased workloads, which result in a higher prevalence of fatigue, continue to be a factor in advanced manufacturing settings. For examples, we refer the reader to: Brocal and Sebasti an (2015), Romero et al. (2016), Ferjani et al. (2017), and Gust et al. (2017). In our estimation, these examples and the changing nature of jobs require a holistic analysis of the workers' states, from an occupational health and safety perspective and in the emerging era of advanced manufacturing. ...
Article
Advanced manufacturing has resulted in significant changes on the shop-floor, influencing work demands and the working environment. The corresponding safety-related effects, including fatigue, have not been captured on an industry-wide scale. This paper presents results of a survey of U.S. manufacturing workers for the: prevalence of fatigue, its root causes and significant factors, and adopted individual fatigue coping methods. The responses from 451 manufacturing employees were analyzed using descriptive data analysis, bivariate analysis and Market Basket Analysis. 57.9% of respondents indicated that they were somewhat fatigued during the past week. They reported the ankles/feet, lower back and eyes were frequently affected body parts and a lack of sleep, work stress and shift schedule were top selected root causes for fatigue. In order to respond to fatigue when it is present, respondents reported coping by drinking caffeinated drinks, stretching/doing exercises and talking with coworkers. Frequent combinations of fatigue causes and individual coping methods were identified. These results may inform the design of fatigue monitoring and mitigation strategies and future research related to fatigue development.
... The manufacturing capacity and performance depend on the number of employees and their skills. Unlike machinery, human labor is more uncertain and fluctuates more, making the manufacturing systems generally more unpredictable (Ferjani, Ammar, Pierreval, & Elkosantini, 2017). Second, SMEs often achieve their competitiveness by offering craftsmanship, flexibility, and better service to the market (Bär, Herbert-Hansen, & Khalid, 2018). ...
... The simulation-optimization approach's value as a means for the extensive use of modeling and decision support tool is broadly recognized, especially for highly complex systems, such as those discovered in automotive, aerospace, electronics, and semiconductor industries (Mourtzis, 2020). Manufacturing and supply chain systems are often characterized by stochastic and uncertain behaviors (Ferjani et al., 2017). The characteristics of these large, complex systems are NP-Hard in their nature (Danilovic & Ilic, 2019;Motlagh, Azimi, Amiri, & Madraki, 2019). ...
Article
Manufacturing capacity planning is one of the critical processes in every manufacturing company, and, with increasing exploitation of data and information technology, has necessarily become more efficient than before. However, the power to harness data and information for planning requires specific knowledge and resources, mostly limited to large enterprises. Small and medium-sized enterprises (SMEs) generally do not have sufficient resources to collect large amounts of data or the know-how to process and exploit data. Moreover, SMEs often fail to implement advanced techniques and tools (e.g., optimization tools or enterprise resource planning (ERP) software), owing to the cost and a lack of specific knowledge and personnel. This paper proposes a solution for reducing the burden on SMEs in collecting and utilizing data for the planning of manufacturing capacity. A simulation-optimization approach is adopted because of the complex nature of labor-intensive manufacturing in SMEs. The approach includes an artificial neural network for model simulation and data relationship recognition, combined with a genetic algorithm for optimizing manufacturing resource configuration. The proposed method can facilitate the process of planning manufacturing capacity for different yield targets, as tested in a case study of a pastry company and providing the means for the company to exploit both empirical and observational data for the purpose.
... In addition, each agent can work with other agents according to the specific rules, which are modifiable when the manufacturing system is facing changes in production requirements. Ferjani et al. [24] have used the hybrid modeling method to construct the simulation model of the demand fluctuation manufacturing system and have integrated the fatigue model in operator agents. Then the effects of the number of operators and the number of operators' skills on production efficiency and operator fatigue levels have been studied. ...
Article
Full-text available
In the multi-variety and small-quantity manufacturing environment, changeover operation occurs frequently, and cooperative changeover method is often used as a way to shorten the changeover time and balance the workload. However, more workers and tasks will be affected by cooperative changeover. As such, the effectiveness of the cooperative changeover is dependent on other factors, such as the scope of cooperation and the proportion of newly introduced products. For this reason, this paper proposes a hybrid modeling method to support the simulation study of the production team's cooperative changeover strategies under various environments. Firstly, a hybrid simulation modeling method consisting of multi-agent systems and discrete events is introduced. Secondly, according to the scope of cooperation, this paper puts forward four kinds of cooperative changeover strategies. This paper also describes the cooperative line-changing behavior of operators. Finally, based on the changeover strategies, the proposed simulation method is applied to a production cell. Four production scenarios are considered according to the proportion of newly introduced part. The performance of various cooperative strategies in different production scenarios is simulated, and the statistical test results show that the optimal or satisfactory strategy can be determined in each production scenario. Additionally, the effectiveness and practicability of the proposed modeling method are verified.
... In the study, in which the effect of the fatigue on the task duration was investigated, an intuitive analysis method with multiple criteria was used and a more dynamic machine operator assignment was attempted to perform. The results of Ferjani et al. showed that fatigue had an effect on the mainstream time and that the intuitive approach gave better results [6]. In the study which was carried out by Stefanie Brilon in 2010 and which was about assigning of the tasks requiring different skills to the operators whose skill levels were unknown, it was focused primarily on the performance of the operators for the task assignments to be done. ...
... Any change in the problem objectives, including the optimization of the production duration (Uzun Araz and Salum, 2010), the system configuration, the operator's availability and efficiency in processing different tasks (Costa et al., 2013), the transfer costs between stations (Malhotra and Kher, 1994) will impact the choice of optimal rules to be implemented. Support systems, such as simulation-based adaptive control schemes, are used to identify the combination of rules that work best in given states of the system but the solution found can quickly become obsolete due to variations in the system and a new assessment is required (Ferjani et al., 2017;Uzun Araz and Salum, 2010). For this reason, simulation meta-models, such as artificial intelligence models, are an efficient alternative that can be used to speed up the decision process and select rules at each assignment point (Can and Heavey, 2012;Uzun Araz and Salum, 2010). ...
Article
In manufacturing systems where productivity is constrained by operators’ availability, cross-training strategies can be used to enable dynamic assignment of operators to workstations. However, finding an assignment approach that efficiently works under various system conditions is not trivial as, among other factors, the level of cross-training, the production duration and the initial conditions of the system can influence the assignment approach’s performance. To overcome this issue, in the case study presented in this paper, operator assignments have been modeled using a simulation-based optimization approach, with an “outer” optimizer that selects assignment-related parameters to simulate based on the system conditions, and an “inner” optimizer integrated with a simulation model that generates optimal assignments. For the case described here, which is modeled using a deterministic simulation model, the “outer” optimizer is an Ant Colony Optimizer (ACO) and the “inner” optimizer is a Binary Integer Programming (BIP) model. The ACO will select weights for the assignment objectives of the BIP multi-objective function so that throughput is maximized. The BIP is called by the system simulation model at fixed intervals or when a system status changes to assign operators to workstations. Results show that the simulation-based optimization approach generates higher throughput performance than static WIP-base assignment, especially when longer production duration are considered. The effects of cross-training and production duration on production throughput are also investigated. The simulation-optimization approach used can be abstracted to a framework where the “outer” and “inner” optimizers may be applied to different domains than the case study addressed here and can also be applied to stochastic simulation models.
... The operation time of the workstations was found to be dependent on a skill factor related to the workers' skill and age. A heuristic algorithm for multi-skilled workforce scheduling in a job-shop system has been presented by Ferjani et al. (2017). The purpose of the study was to assign workers to machines minimizing the mean time to complete a job and considering the impact of fatigue on the workers' processing time. ...
Article
In the last few decades, studies have demonstrated the correlation between worker well-being and the performance of production systems. This paper addresses the problem of assigning workers to tasks in a workshop system. In this context, recent researches have focused on the ergonomics assessment, often neglecting the evaluation of the workers’ performance. This study aims to formulate a mixed integer linear programming model to solve the workforce scheduling problem and improve the performance of the system integrating ergonomics and human skills. To overcome the complexity of the combinatorial problem, a constructive heuristic procedure is developed. Moreover, a novel approach is proposed to determine the workers’ skills. Human performance is modelled in terms of the time required to perform consecutive tasks, considering different sequences of tasks. In addition, the model was applied to a real case study to verify its feasibility. Different scenarios are tested, considering different levels of exposure to different risk factors. The results indicate that a limited increase in the makespan enables decreasing the risk level and the achievement of an excellent workload balance among workers in terms of time spent in performing tasks. Moreover, the heuristic procedure has demonstrated to perform well on instances of realistic size, and it could be adapted to many manufacturing systems to solve the problem in real industrial contexts.
... A FJSP considering different labor wages under dynamic electricity pricing is solved by the non-dominated sorting genetic algorithm-III (NSGA-III) (Gong et al. 2019b). Ferjani et al. (2017) studied the dynamic multi-skilled workers assignment problem and considered the impact of fatigue. ...
Article
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Green low carbon flexible job shop problems have been extensively studied in recent decades, while most of them ignore the influence of workers. In this paper, we take workers into account and consider the effects of their learning abilities on the processing time and energy consumption. And then a new low carbon flexible job shop scheduling problem considering worker learning (LFJSP-WL) is investigated. To reduce carbon emission (CE), a novel CE assessment of machines is presented which combines the production scheduling strategies based on worker learning. A memetic algorithm (MA) is tailored to solve the LFJSP-WL with objectives of minimizing the makespan, total CE and total cost of workers. In LFJSP-WL, a three-layer chromosome encoding method is adopted and several approaches considering the problem characteristics are designed in population initialization, crossover and mutation. Besides, four effective neighborhood structures are developed to enhance the exploitation and exploration capacities, and the elite pool strategy is presented to reserve elite solutions along each iteration. The Taguchi method of DOE is used to obtain the best combination of the key parameters used in MA. Computational experiments conducted show that the MA is able to easily obtain better solutions for most of the tested 22 challenging problem instances compared to two other well-known algorithms, demonstrating its superior performance for the proposed LFJSP-WL.
... For large scale instances of the problem, ant colony optimization and genetic algorithms were developed. The performance measure of this research was followed by Ferjani et al. (2017) for solving dynamic multi-skilled workers assignment problem. They devised adaptable heuristics taking the impact of fatigue in to consideration in their online methodology. ...
Article
Multi-skilling provides an organization with the ability to arrange its workers to meet the certain needs for several skills. This paper reviews the literature on scheduling problems under the multi-skilled and flexible resources which is the case for a wide range of disciplines including construction industry, IT projects, healthcare, process systems, and so on. The main purpose of this review is that it helps researchers and scholars entering the multi-skilling experience an all-encompassing overview of existing models and methods and to identify new research directions. To structure the emerging literature in this area, we review and classify 160 articles published from 2000 to middle 2020 based on characteristics of the objective functions, the mathematical formulations, the solving methodologies, and the potential applications. This review outsets with a general framework for multi-skilling and accomplishes a comprehensive taxonomy for the literature of multi-skilling in scheduling problems. The results show that the main focus of the existing research in this field has been devoted to project scheduling problems (53.12%), mixed integer programming models (54.2%) and metaheuristics (28.7%) as solving method, cost (39.4%) as single objective function (68.6%), and deterministic environment for parameters (85.5%) on the top. It also turned out that 68.8% of research considered a single objective, whereas 13.8% and 17.6% of the research papers have developed models with two and more objectives, respectively. With the goal of providing a vivid roadmap for researchers, through meta-narrative analysis of the collected papers and a rigorous analysis, promising future lines of research are outlined.
... On the one hand, fatigue declines some agent capabilities. For instance, the increase of fatigue leads, according to Ferjani et al. (2015 and2017), to an increase of the task durations. Based on the human behavioral model proposed by Elkosantini and Gien (2007), fatigue causes also a decrease in the worker satisfaction. ...
Conference Paper
In manufacturing systems, workers are often subjected to arduous working conditions, such as heavy loads and discomfort postures, which induce fatigue. Because of the effect of fatigue on workers' well-being, as well as on their performances, managers would need to understand the evolution of operators’ fatigue during their work, in order to make relevant decisions (e.g. work schedule, facility layout decisions, and rest periods). In this context, we present a simulation modeling framework to evaluate manufacturing systems, which takes the workers’ fatigue into account. The suggested framework combines several worldviews: Discrete Event modeling, multi-agent and System Dynamics. Discrete Event concepts are used to describe the manufacturing system dynamic behavior and agents are used to model workers. One important characteristic of agents’ behavior on which emphasis is put is fatigue, which is modeled using System Dynamics concepts. The proposed approach is implemented using the Anylogic simulation software.
... Battini et al. 2017;Finco et al. 2020), on the impact of fatigue on task duration (i.e. Ferjani et al. 2017;Calzavara et al. 2019) or on the modelling of the human fatigue level according to the work time and workload increase (i.e. Hu and Chen 2017). ...
Article
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The effects of workforce differences on manufacturing systems have attracted the attention of a wide range of researchers in recent years. The differences between workers in terms of skills, age, gender and anthropometric measures have a large impact on production system performance. In this study, the workforce differences factors in production system design and modelling were investigated, with the aim of understanding how the differences between workers could influence a production system and how they had been considered in previous studies. The papers selected from the Scopus database were categorised based on whether how human factors are incorporated into manufacturing system optimisation and design approaches is discussed therein or is not. To find relevant papers, two sets of keywords were defined: (1) keywords relating to the differences between workers and (2) keywords relating to the kind of problem under study. Furthermore, the investigated papers helped highlight the strengths and weaknesses of the existing literature and derive a discussion on the possible future research steps. ARTICLE HISTORY
... Les problèmes d'affectation consistent à trouver une correspondance un à un, par exemple, un ensemble de tâches à un ensemble d'agents, de marchandises à des véhicules, etc. Selon Ferjani [6], la résolution de ces problèmes industriels permet à l'entreprise d'améliorer sa performance. D'après la revue de littérature, nous constatons qu'il existe deux catégories de ce problème : l'affectation statique et l'affectation dynamique. ...
Conference Paper
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Several factors strengthened urban issues, namely: the development of electronic commerce, changes in purchasing methods and consumption habits. In this context, parcel lockers are now presented as alternatives to last-mile delivery. These lockers reduce the impacts caused by the transport of parcels in the urban area and also provide a response to the cited challenges. Customer satisfaction is becoming increasingly difficult to manage given the considerable increase in e-commerce and the number of parcels delivered through lockers. In this work, we propose a mathematical approach to the dynamic allocation problem of parcels in parcel lockers. The objective is to determine the optimal location of each parcel while satisfying the customer's demands and taking into account the real constraints.
... Meanwhile, the scheduled operations remain partially manual including but not limited to: material handling, or carrying and processing jobs as stressed by (Napolitano, 2012). The assignment of operators to operations must include personal skills, training and experience in order to match the competences and/or functionalities required by the operations to be performed (Ferjani et al., 2017;Grosse et al., 2015). Reconfigurability is the capacity of a set of machines to be reconfigured in a period of time and both reconfigurable machine tools (RMT) and computer numerically controlled (CNC) machines are the core components of any reconfigurable manufacturing system. ...
Conference Paper
Reconfigurable Manufacturing Systems have been introduced in the mid 1990s as an alternative to classical dedicated or flexibles production systems. They are supposed to be more reactive and capable of evolving depending on unpredictable and high-frequency market changes induced by global market competition. While this concept has received a lot of attention in the literature, mainly at the design and conception phase of the production system, only few works are addressing the operational management of such production systems. One of the key features of reconfigurable manufacturing system is the possibility to use different configurations. The objective is to schedule operations efficiently while considering the different configurations of the system that are available. Switching from one configuration to another requires setup times. However, contrary to classical setup times that can be found in literature on scheduling problems, switching from a configuration i to j may require that some machines are stopped, and then reconfiguration goes beyond classical setups. This paper intends to formalise such a problem in the context of Flow-shop and Job-shop production systems. First results on small case instances are introduced.
... The goals of Industry 4.0 are to maximize the impact of a worker's skills by integrating him/her as as an integral component of the cyber-physical infrastructure; however, the end goal of CIM was to achieve a worker-less manufacturing environment (Gorecky et al, 2014). Additionally, recent publications from the ergonomics and manufacturing systems literature are showing that the transition to advanced manufacturing is increasing the workload on skilled labor (Brocal and Sebastián, 2015;Romero et al, 2016;Ferjani et al, 2017) and consequently, increasing fatigue levels. ...
Chapter
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With the continued technological advancements in mobile computing, sensors, and artificial intelligence methodologies, computer acquisition of human and physical data, often called cyber-physical convergence, is becoming more pervasive. Consequently, personal device data can be used as a proxy for human operators, creating a digital signature of their typical usage. Examples of such data sources include: wearable sensors, motion capture devices, and sensors embedded in work stations. Our motivation behind this paper is to encourage the quality community to investigate relevant research problems that pertain to human operators. To frame our discussion, we examine three application areas (with distinct data sources and characteristics) for human performance modeling: (a) identification of physical human fatigue using wearable sensors/accelerometers; (b) capturing changes in a driver’s safety performance based on fusing on-board sensor data with online API data; and (c) human authentication for cybersecurity applications. Through three case studies, we identify opportunities for applying industrial statistics methodologies and present directions for future work. To encourage future examination by the quality community, we host our data, Code, and analysis on an online repository.
... Several studies deal with the examination of the production performance of workers. Ferjani et al. (2017) developed a heuristic algorithm for associating workers and jobs with different abilities. In their proposed model, the processing times of operations depend on the degree of worker fatigue. ...
Article
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The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study.
... The company's potential results from the structure, quality and ability to create optimal combinations of resources (Laslo and Golberg, 2008). The condition and quality of all the organization's resources are very important, however, in the subject literature it is emphasized that human resources play crucial role today (Čižiūnienė et al., 2016;Ferjani et al., 2017;Jasiulewicz-Kaczmarek and Saniuk, 2015;Roszyk-Kowalska, 2016). Making the right decisions is very difficult due to the dynamic nature of the company and its environment (Łatuszyńska and Lemk, 2013). ...
Article
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The optimal use of resources available in the enterprise is important regardless of the size of the company and the industry in which it operates. Enterprises are therefore forced constantly to make alternative choices related to the allocation of available resources and to optimize these choices. The article addresses the problem of the approach to optimizing the use of human resources particularly, the use of extra employee qualifications e.g., manual skills, pressure resistance, work precision, the ability to read schematic diagrams, etc. in the context of technical requirements for a given task. This is extremely important in the situation when subsequent works are individual and the conditions in which they will be performed, cannot be predicted in 100%, they may differ from those that have been implemented so far, and at the same time numerous orders of various nature are being implemented. In this situation, an accurate prediction of the requirements posed by new tasks and the appropriate selection of teams executing them can have an impact on the effectiveness of the task completion process. In the article, this problem is presented on the example of a medium-sized service enterprise operating in the industry-related sector operating basically on tender procedures and tender contests. The works are carried out on the customer's premises, often with new customers or in new field conditions. Thus, the success of the undertaking depends mainly on the optimal selection of employees with appropriate qualifications and competences. The example of an investment task is used to show a method of identifying characteristics relevant to the task as well as selection of employees in order to use the capabilities of human teams better. Technical aspects of task implementation and an employees team selection with regard to the absolutely required technical qualifications as well as the behavioral and physical skills necessary for its implementation are taken into account. The described method can be used for future tasks regardless of the changing conditions of their implementation. The intention of the authors is to develop a tool supporting the decision-making process in this area, so that it can also be used by managers with lower technical competences.
... [14] proposed a multi-objective simulation optimization approach that is based on Nondominated Sorting Genetic Algorithm ΙΙ (NSGA-ΙΙ) to address the random nature of a team of workers when they were assigned at a job shop production system. In another paper, [15] proposed an approach that can address the increase in the processing times of jobs when taking fatigue of workers into consideration. Although the above-mentioned related research was targeting the implementation of Seru, and the other parts talked about optimization of processing times under some uncertainties in the environment, however, none of them has addressed the problem of assigning workers of different multiple skills when considering the stochastic nature of task times into account. ...
Conference Paper
In this study, we investigated the impact of uncertainty in task processing times of workers on production output rate in a SERU manufacturing system and compared it with traditional assembly line. SERUs have been an innovative way manufacturing system design and workforce allocation for labor-intensive manufacturing processes where workers are expected to carry out multiple or the entire set of tasks to make/assemble the product. The literature has shown that SERUs could create significant advantages compared to assembly lines in terms of capacity utilization, production rate, and work-in process inventory. However, the uncertainty in task times could have a significant impact on throughput in labor intensive manufacturing systems compared to machine-intensive ones due to the potential influence of worker's skill level. Therefore, we proposed a stochastic mixed integer linear programming approach to model the uncertainty in task processing times and optimize the workforce allocation. We also investigated the impact of worker skill level on the throughput in both systems and discussed the superiority of each system based on the worker and team skill levels.
... Indeed, the load and repetitiveness of operations in assembly lines can lead to muscle fatigue ), which has been shown to reduce the performance and the product quality and leads to MSDs (Elmaraghy, Nada, and ElMaraghy 2008;Kolus, Wells, and Neumann 2018). Most of the time, in assembly lines, a high effort, and intensity lead to the fatigue of workers (Givi, Jaber, and Neumann 2015;Ferjani et al. 2017). Besides, workers can initiate several cycles on several products without the benefit of sufficient rest time to recover and reduce fatigue. ...
Article
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Assembly lines are production lines used to manufacture products, ranging from mass-production products to mass-customisation with low unit products. Assembly lines consume the largest parts of investment funds and involve the largest proportion of companies' labour force. However, workers in assembly lines are exposed to work-related musculoskeletal disorders (MSDs) and ergonomics problems. Poor distribution of workloads reduces the performance of assembly lines and causes workers MSDs and injuries, largely affecting the economics of production systems and resulting in high workers' compensation and absenteeism costs. Furthermore, ergonomics problems and MSDs impact product quality and decrease productivity. We propose a methodology for taking physical ergonomics into account as early as in the design phase of assembly lines. This methodology is based on Integer Linear Programming for the assembly line balancing problem with consideration of ergonomics with a quantitative fatigue and recovery criterion. As solving approach, we develop a dedicated exact algorithm, denoted Iterative Dichotomic Search, to solve low and medium-size instances of the problem. We validate our approach by proposing numerical experiments and analysis on instances from the literature.
... Bi-criteria and multi-criteria algorithms are also used to optimize employees' structure, like the allocation problem of cross-trained workers from the point of view of operational and human resource aspects [13]. Simulation-based methods support the optimization of online assignment of multi-skilled workers in manufacturing systems, where unpredictable events over time can be taken into consideration [14]. Fuzzy models are used to optimize the transportation of relief materials and human resources from distribution centers to delivery points, where total cost and time window of operations are taken into consideration as an objective function of the bi-criteria transportation problem [15]. ...
Article
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Manufacturing and service processes are composed of several elements: Technical, financial, logistics, information and human resources. Staff deployment and staffing is an essential problem in the human resource management domain because the structure of employees would be continuously in an optimal relationship to the jobs to be performed. This paper proposes a conceptual model for the analysis of human resource deployment processes. After a systematic literature review, it was found that algorithms are important tools for the design and control of human resource problems since a wide range of models determines an optimization problem. According to that, the main focus of this research is the modelling and analysis of human resource deployment processes of manufacturing companies using Markov-chain mathematics, also taking into account the absorbing phenomena of employees’ promotion. The main contribution of this article includes the model framework of Markov-chain simulation of a human resource deployment problem; the mathematical description of different human resource deployment strategies with subdiagonal and superdiagonal promotion matrices; the computational results of the described model with different datasets and scenarios. In the case of a given human resource strategy, the Markovian human resource deployment process of a company was analyzed. The analyzed model was the HR deployment of assembly line operators in a multinational company, including six levels of promotion. The results of the scenario analysis show that promotion and recruitment rates have a great impact on the future employees’ structure.
... random arrival of orders) and where frequent changes occur, for example due to fluctuation in the customers' demands. For such types of systems, the starting time and completion times of jobs can be unpredictable (Ferjani et al., 2017). ...
Article
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This study focused solely on a paint manufacturing industry in Iran that will help managers to effectively manage their enterprise. The goal of this paper is to integrate simulation modeling along with response surface methodology (RSM) and design of experiments (DOE) in order to analyze and improve the productivity in a selected continuous paint manufacturing industry. Computer simulation is developed to propose different scenarios as the inputs of DOE. Based on the final results, the optimum productivity was achieved at the point of 93.5, that is relevant to the number of labor (B) = 26 and failure time of lifter (C) = 56.01 min. Moreover, the other two factors, A (service rate of delpak mixer) and D (number of permil) should be located at a low level. Quality and production managers, engineers as well as academicians can implement the results of the current study in other case studies. This approach can be generalized to other manufacturing systems to improve their productivity in a timely and cost-effective manner.
... On the other hand, a semiautomatic production line is the combination between manpower and machines to perform specific task [5][6][7]. In a semiautomatic production line, the factors that relate to manpower performance are stress [8][9][10] and fatigue [10][11][12][13][14]. However, it is found that the past studies appear to have more emphasis on fully automatic production lines, while little attention has been given to the semiautomatic production line that is associated with manpower factor as found in [15][16][17][18]. ...
Article
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Completion time in a manufacturing sector is the time required to complete a product in sequence process during production operation. In a semiautomatic production line, manpower factors such as fatigue and pressure are two significant influences on completion time. However, it is found that previous studies lack the concern to include manpower factor in completion time. Hence, this paper develops a causal loop diagram and stock flow diagram to simulate the influence of manpower factor on the completion time in a semiautomatic production line. In this research, a well-known audio speaker manufacturer is selected as a case company. As a result, it is found that the preparation time for materials has a great impact on fatigue and pressure as it contributes the highest percentage of deviation from the completion time base run with 72.22%. Finally, a policy regarding completion time improvement is recommended to the management to enhance their production performance.
... Although a human is in a working state, it is subject to dynamically changing and unobservable factors such as fatigue and situation awareness. In this work, we consider fatigue accumulation-one of the major unobservable factors affecting the task performances of human operators [3,4,25,29,30]. There is no universally agreed model for describing the relationship between fatigue and task performance. ...
Article
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Catering for human operators is a critical aspect in the sustainability of a manufacturing sector. This paper presents a task allocation problem in human–machine manufacturing systems. A key aspect of this problem is to carefully consider the characteristics of human operators having different task preferences and capabilities. However, the characteristics of human operators are usually implicit, which makes the mathematical formulation of the problem difficult. In addition, variability in manufacturing systems such as job completion and machine breakdowns are prevalent. To address these challenges, this paper proposes a deep reinforcement learning-based approach to accommodate the unobservable characteristics of human operators and the stochastic nature of manufacturing systems. Historical data accumulated in the process of job assignment are exploited to allocate tasks to either humans or machines. We demonstrate that the proposed model accommodates task competence and fatigue levels of individual human operators into job assignments, thereby improving scheduling-related performance measures compared to classical dispatching rules.
Article
Flexibility is becoming more and more important for manufacturers facing various changes. As an effective way to increase flexibility in short time with small investment, multi-skilled workers have received much attention in recent years. In this paper, multi-skilled worker assignment problem is solved in the context of seru production system, in which differences in workers’ skill sets and proficiency levels are taken into consideration. Worker grouping, cell loading and task assignment are solved concurrently in the problem. A mathematical model with objectives of improving inter-seru and inter-worker workload balance is proposed to solve the problem. In order to validate the proposed model, a numerical example is presented and solved by the sum weighted method, with a linearized formulation. With respect to the NP-hard nature of the model, a meta-heuristic algorithm based on NSGA-II is developed. The algorithm is tested by several numerical examples and the impact of differences in workers’ competency on workload balance is analyzed based on the computational results.
Article
Modern manufacturing systems are characterized by waste elimination, cycle time control, and high work specifications. Workers, although being an integral part of manufacturing, are usually neglected or severely simplified in operational research of these systems. Through the years, the need for control over the job in manual manufacturing has been identified as crucial for both system performances and operators’ health. The aim of this research is to integrate time margins, as the mean of control, and human factors under uncertainty into scheduling problem of a multi-product manufacturing system while maintaining performance and workers’ well-being. The proposed method is polynomial and simulation-based, developed in two stages using agent-based methodology. The first stage provides a global schedule with makespan as the objective function and with time margin allocation strategy under uncertainty. The second stage enables rescheduling depending on the human error probability and fatigue level. Experiments and comparisons with the similar literature problem have indicated decrease of human error probability and fatigue. Extended experiments for flow shop system justify the use of this unique approach. The developed tool enables system designers to enhance performance by observing human effects through its factors and different time margins allocation strategies.
Article
In the past few decades, more and more studies have begun to consider the impact of human factors on manufacturing systems. This paper studies a hybrid flow shop scheduling problem considering multi-skilled workers and fatigue factors. An agent-based simulation system is established to cope with the uncertainties in the worker fatigue model. Furthermore, this paper proposes a novel simulation-based optimization (SBO) framework, which combines genetic algorithm (GA) and reinforcement learning (RL) to address the hybrid flow shop scheduling problem. Numerical experiments are conducted on several instances with different production configurations. In particular, a pharmaceutical production facility is modeled as a hybrid flow shop to demonstrate the feasibility and effectiveness of the proposed SBO method.
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Many man-machine systems experience machine degradation and human errors, which may be mutually dependent and have detrimental effects on system reliability. On the one hand, machine degradation will increase fatigue inducing conditions and result in more human errors. On the other hand, human errors usually cause shock loads on a machine and accelerate its degradation. Therefore, machine degradation and human errors aggravate each other. To model the mutual dependence, we develop a Piecewise-deterministic Markov process modeling framework, which can incorporate machine degradation and human errors to evaluate the system reliability. In the framework, the machine degradation is described by a multi-state model with a Semi-Markov process, where the times of transitions due to the mutual dependence are time-varying random variables; a mathematical model is developed to evaluate the human error rate under the effect of fatigue-recovery, where human errors occur according to a nonhomogeneous Poisson process. A Monte Carlo simulation algorithm is implemented to compute the reliability. The turret of a lathe operated by a worker is presented to illustrate the effectiveness of the reliability model.
Conference Paper
It is known that the workers’ fatigue can greatly affect both industrial performances and the workers’ wellbeing at work. Consequently, when designing a manufacturing system, managers are interested in facility layouts that favor performance objectives, while avoiding excessive fatigue. Unfortunately, most existing studies related to layout design focus on technical aspects of the considered system (e.g. flow costs, distance between machines, etc.) so that human factors, in particular fatigue, are insufficiently taken into consideration. Therefore, we are interested in how the workers’ fatigue can be taken into account when evaluating possible layout designs. We analyze the factors that induce fatigue, which are mainly concerned with the work arduousness, and depend on the layout. We explain how they can be considered in order to compare possible solutions of a layout problem. In such a context, emphasis is put on the role of simulation. We illustrate our purpose and highlight the importance of taking fatigue into consideration through a comparison, using simulation, of two different layouts of a jobshop system. The comparison is based both on the mean flowtime of jobs and how the workers’ fatigue evolves over time.
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Work-related fatigue is a multidimensional phenomenon with significant effects on operational performance. Our work focuses on how the literature of operational research measures and models fatigue and its effects on operational performance, and on how it mitigates those effects. We position the literature of fatigue relative to that of work-rest scheduling, shift scheduling, multitasking, ergonomics, deterioration scheduling, and occupational health and safety. We classify the literature of fatigue across multiple dimensions: the methods by which it is identified and measured; the operational research methodology applied for fatigue prevention or mitigation; the flexibility allowed in work-rest scheduling and in shift scheduling; applications within manufacturing, construction, transportation, hospitals, and services; and the extent to which real data is used and results are implemented. Our work shows that operational research has contributed numerous effective algorithms and heuristic solution procedures to fatigue mitigation. We also identify several important research directions for operational research, to promote its broader and more effective use to identify and mitigate the effects of fatigue on operational performance.
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Human-Robot Collaboration, whereby human worker and robot perform tasks jointly, is becoming the new frontier in industry production. Unlike robots, continuous work leads to an accumulation of human fatigue, which is the main cause of decreased efficiency and deterioration of health. Characteristic differences between human and robot bring challenges to collaboration task scheduling. In this paper, we studied the task scheduling of a human-robot collaboration assembly cell to achieve a trade-off between job cycle and human fatigue. A task scheduling model integrated with micro-breaks inside job cycles was proposed to avoid human fatigue accumulation by taking advantage of the human-robot collaboration characteristics. Furthermore, the optimisation of task scheduling by taking the job cycle as the objective function and maximum human fatigue as a constraint was solved. The developed method is studied on a cable assembly inspired by an industry case. The results of the case study are presented to indicate the validity and practicability of the proposed model. It suggests that compared with the model of placing rest breaks between job cycles, the proposed model outperforms in job-cycle performance in most cases. Finally, there are some insights on the HRCAC which are obtained from the results of the case study.
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This study considers the characteristics of a worker performing a sequence of tasks in a mission by developing a Bayesian Network model to predict reliability and mission completion time, the two measures of overall performance. The mission is broken down into tasks of different types, some of which may not be repeated back-to-back. Worker's initial task performance, learning, fatigue, and stress are the factors that affect the overall performance, and they vary by ‘worker’ and ‘task type'. Those characteristics and the task sequence plan are incorporated into a Bayesian Network to measure the performance of each task and, subsequently, the mission. Taking the task sequence plan into account adds a new dimension to the Bayesian Network as it counts the number of repetitions performed for each type of task. This distinctive feature allows linking the performance, learning, fatigue, and stress levels of a preceding task to a succeeding one. The developed model is general and can be applied to different real-life settings that are stressful and labour intensive. A numerical analysis is conducted to study how a worker's characteristics affect her/his reliability and the mission duration. The results are discussed, and managerial insights are presented.
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Job shop scheduling problem (JSSP) is a thriving area of scheduling research, which has been concerned and studied widely by scholars in engineering and academic fields. This paper provides a comprehensive review on the types and models of JSSP, to the best of our knowledge, there has not been a review paper on this aspect up to now. The main purpose of this review is to help researchers and scholars outlining an overview of existing JSSP models and exploring more valuable research directions. We first analyze and classify the entities and their attributes, assumptions, basic subtypes, and measures of performance of JSSP based on the researches from mid-1960s to 2020s. The general representation and overview of JSSP models are also presented. Then, some extensive statistics and analysis are conducted on 297 published papers in 72 journals ranging between 2016 and early 2021. Finally, some hot research aspects of JSSP models are reviewed in detail and some promising research directions are provided.
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Human fatigue, which is one of the main causes of efficiency decline and health damage, cannot be overlooked in the task scheduling of HRC that is becoming one of the latest frontiers of manufacturing. In this letter, we proposed a task scheduling model that integrated micro-breaks within job-cycles to provide recovery from fatigue accumulation by taking advantage of the characteristic of HRC. Furthermore, an improved Chemical Reaction Optimization (CRO) algorithm is developed to solve the trade-off between cycle time and human fatigue. Greedy strategy is applied in the global and local search reaction of CRO to solve the multi-objective problem which includes two conflicting goals of cycle time and fatigue minimization. The proposed model and algorithm are demonstrated on assembly cases inspired by an industry application. The results from the case study suggest that the proposed model outperforms the comparison model. Finally, computational results have demonstrated that the superiority of the proposed algorithm is proved by comparing with genetic algorithm (GA), imperialist competitive algorithm (ICA), and standard CRO.
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For several decades, simulation has been used as a descriptive tool by the operations research community in the modeling and analysis of a wide variety of complex real systems. With recent developments in simulation optimization and advances in computing technology, it now becomes feasible to use simulation as a prescriptive tool in decision support systems. In this paper, we present a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments. We classify the existing techniques according to problem characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). We discuss the major advantages and possible drawbacks of the different techniques. A comprehensive bibliography and future research directions are also provided in the paper.
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Fatigue must be reduced so that the workers can maintain/increase productivity andhave optimal stress. Time at work can be divided into working time and resting (recovery) time. The purpose of resting time is to overcome fatigue. Fatigue can occur in different parts of the body—general body fatigue (cardiovascular system) (physiological), muscular fatigue (muscles) (physiological), and mental fatigue (brain) (psychological). A key consideration of a rest is its recovery value. The recovery value of a rest is a function of how fatigued the muscle (cardiovascular system, brain) is when the rest begins, length of the rest, and the status of the muscle (cardiovascular system, brain) during the rest. Fatigue increases exponentially with time. On the other hand, the value of a rest declines exponentially with time. Rest is more beneficial if it occurs before the muscle (cardiovascular system, brain) has too much fatigue. Thus, machine-paced or standardized rests are probably less effective than rests under operator control.
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This study evaluates operating policies for offering a near-perfect delivery performance for vital customers in dual resource-constrained (DRC) job shop environments. Prior studies have considered this problem in machine-limited settings, and shown that dispatching rules that help realise a near-perfect delivery performance for vital customers necessarily deteriorate delivery performance for other customers served by the shop. This study extends prior work, and considers additional tools that can be used by managers in DRC shops such as labour flexibility, and assignment rules that incorporate customer-based information to deploy workers to departments containing high-priority jobs. Results show that labour flexibility in conjunction with appropriate decision rules allows for enhanced delivery performance for both vital and normal priority customers. These results hold even under conditions where 80% of the shop's workload is compromised of high-priority orders.
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Manufacturing systems are uncertain and dynamic systems, hence, they require real-time scheduling to adapt to changing manufacturing conditions. Current real-time scheduling approaches have been devised mainly for machine-only constrained systems, in which the shop capacity is constrained only by machine capacity, rather than for dual resource constrained (DRC) systems, in which the shop capacity is constrained by machine and worker capacity. In particular, there is no study on DRC system scheduling in which the ‘where’ and ‘when’ worker assignment rules, basic features of DRC systems, are altered in real-time (dynamically selected) to respond to new manufacturing conditions. Besides, multi-criteria DRC system scheduling has not yet been addressed extensively. Also, there has been little research on the interactions of dynamically selected job dispatching, worker assignment and job routing rules, which have a significant impact on DRC system performance. This paper proposes a multi-criteria real-time scheduling methodology for DRC systems to address these issues, and investigates these interactions. The methodology employs artificial neural networks as meta-models to reduce computational complexity and a fuzzy inference system to cope with multiple performance criteria. Various simulation experiments demonstrate that the methodology provides satisfactory results for real-time DRC systems scheduling.
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Service operations that utilize cross-trained employees face complex workforce staffing decisions that have important implications for both cost and productivity. These decisions are further complicated when cross-trained employees have different productivity levels in multiple work activity categories. A method for policy analysis in such environments can be beneficial in determining low-cost staffing plans with appropriate cross-training configurations. In this paper, we present an integer linear programming model for evaluating cross-training configurations at the policy level. The objective of the model is to minimize workforce staffing costs subject to the satisfaction of minimum labor requirements across a planning horizon of a single work shift. The model was used to evaluate eight cross-training structures (consisting of 36 unique cross-training configurations) across 512 labor requirement patterns. These structures, as well as the labor requirement patterns, were established based on data collected from maintenance operations at a large paper mill in the United States. The results indicate that asymmetric cross-training structures that permit chaining of employee skill classes across work activity categories are particularly useful.
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This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.
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This research addresses a system of flexible worker assignments in a setting where there are more workers than machines. When organized using this system, a production line balances itself by shifting the workloads continuously and automatically in response to changes in the state of the system. The system is, in effect, buffering itself against variation by altering the work assignments on the fly. This allows the system to operate with very low levels of work-in-process inventory (WIP). In this paper, the workers (rather than machines) are the factor that limits the rate of output. We also assume that the line has a “U” shape, but many of the results do not depend on this topology. An industrial example is described. The system has some interesting and counter-intuitive properties which we demonstrate under a variety of circumstances through an exploratory approach that uses both Markovian and simulation models. Several different policies are compared under conditions of processing time uncertainty. We demonstrate that a flexible assignment system can outperform fixed assignments in a variety of circumstances. Of particular interest is the near absence of balance delay, even when the tasks cannot be divided equally among the workers.