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Improving Production Scheduling:Integrating Organizational Decision-Making and Problem-Solving Perspectives
Abstract
Abstract Production scheduling activities are common,but complex. This leads to many,different views and perspectives of production scheduling. Each perspective has a particular scope, its own set of assumptions, and a different approach toimproving,production ,scheduling. This ,paper covers three ,important ,perspectives (the problem-solving perspective, the decision-making perspective, and the organizational perspective) and discusses the methodologies that these perspectives use. Finally, this paper presents an integrative strategy that can be used to select, in a particular setting, an approach for improving production scheduling. KEYWORDS: Production scheduling, decision-making,optimization
... Off-the-shelf integrated IT scheduling DSS have been made available to human schedulers since the 1960s (Herrmann, 2006;O'Brien, 1969). Their functionality typically addresses generic cases of scheduling processes, although their operation can be parameterised to a certain extent. ...
... The intuition behind the development of the i-DESME framework has its origins in the works of Bourgeois et al. (1993), Gachet and Sprague (2005), Herrmann (2006), Higgins (2001), Jackson, Wilson, and MacCarthy (2004), McKay and Black (2007), Riezebos et al. (2011) and Wiers (1997). Gachet and Sprague (2005) suggested a set of steps which can be followed during the development of a general-purpose DSS, in order to address the problems faced by traditional SDLC methodologies during the design of such systems. ...
This paper introduces i-nterdisciplinary DEsign for SMEs (i-DESME), a structured interdisciplinary framework for the design of IT scheduling decision support systems, with a focus on small-sized Small and Medium Enterprises (SME) industrial environments. The proposed framework initially models the interdisciplinary characteristics of the current (‘as-is’) implementation of scheduling processes within a particular SME industrial environment. This information provides the basis for the implementation of function allocation and algorithm selection studies on the scheduling processes considered. As a result, an interdisciplinary specification of the information technology (IT) decision support system which will support the future (‘to-be’) implementation of the scheduling processes within the industrial environment is produced, and subsequent software lifecycle phases are implemented. A summary of case study results from the application of the i-DESME framework within the environment of a typical micro-sized SME company is provided.
... According to Herrmann (2006), the production scheduling problem can be considered from many different views and perspectives. The most important perspectives are the following: ...
... There are three main approaches in planning and scheduling to respond to disturbances raised by exceptions and to absorb or at least to mitigate their negative effects (Bruccoleria et al., 2006;Herrmann, 2006): predictive, reactive, and predictive-reactive. ...
The paper proposes a novel predictive–reactive planning and scheduling framework in which both approaches are combined in order to complement each other in a reasonably balanced way. It proposes neither any original scheduling algorithms nor techniques. It also not aims to invent some new mechanisms or to propose some cardinally new ideas. The aim is to chose, adapt and test ideas, mechanisms and algorithms already proposed by other researchers. The focus of research is set to make-to-order production environments. The proposed approach aims not only to absorb disruptions in shop floor level schedules but also to mitigate the impacts of potential exceptions disrupting mid-term level production plans. It is based on application of risk mitigation techniques and combines various simulation techniques expanded by optimization procedures. The proposed approach is indented to be applied by an Advanced Planning and Scheduling system implemented as add-on for an Enterprise Resources Planning system. To make it easier to understand the focus of the paper, we clarify at the beginning the position from which we start.
... It can determine whether delivery promises can be met and identify time periods available for preventive maintenance. Also it gives shop floor personnel an explicit statement of what should be done so that supervisors and managers can measure their performance [7]. ...
In according to competitive markets in most of commodities, supply chain actors must do better than before by decreasing response time of demands as well as decreasing their costs. On the other hand, coordination of supply chain's operation can incredibly effect on reduction time and cost. In this paper, we consider a two stage supply chain consist of a manufacturing and a distributer. The problem is determination when and how each demand must be response. According to variant of demands, we present a nonlinear formulation of production scheduling problem in manufacturing level and vehicle routing problem in distributor level simultaneously and minimize total requirement time to satisfy demands. To consider real world condition, we also assume time window and split delivery in formulation. This paper will end by conclusion and future research.
... Huge interest has been demonstrated in investigating the effect of DSSs on user performance (e.g., Ferris et al., 2010). DSSs could be useful for managers and operators in improving ontime delivery, increasing their responsivity (Herrmann, 2006) by providing information that may release users' cognitive demands (Lee and Seong, 2009;Onnasch and Hösterey, 2019). For instance, when using DSSs, individuals might understand and identify potential mistakes more easily (Lee and Seong, 2009). ...
For many years, manufacturers have focused on improving their productivity. Production scheduling operations are critical for this objective. However, in modern manufacturing systems, the original schedule must be regularly updated as it takes places in a dynamic and uncertain environment. The modern manufacturing environment is therefore very stressful for the managers in charge of the production process because they have to cope with many disruptions and uncertainties. To help them in their decision-making process, several decision support systems (DSSs) have been developed. A recent and enormous challenge is the implementation of DSSs to efficiently manage the aforementioned issues. Nowadays, these DSSs are assumed to reduce the users' stress and workload because they automatically (re)schedule the production by applying algorithms. However, to the best of our knowledge, the reciprocal influence of users' mental state (i.e., cognitive and affective states) and the use of these DSSs have received limited attention in the literature. Particularly, the influence of users' unrelated emotions has received even less attention. However, these influences are of particular interest because they can account for explaining the efficiency of DSSs, especially in modulating DSS feedback processing. As a result, we assumed that investigating the reciprocal influences of DSSs and users' mental states could provide useful avenues of investigation. The intention of this article is then to provide recommendations for future research on scheduling and rescheduling operations by suggesting the investigation of users' mental state and encouraging to conduct such research within the neuroergonomic approach.
... Each perspective has a particular scope, set of assumptions, and a different approach to improving production scheduling. Jeffrey W. Herrmann covers three important perspectives (the problem-solving perspective, the decision-making perspective, and the organizational perspective) and discusses the methodologies that these perspectives are using [7]. ...
Scheduling is the proper allocation of resources over a period for performing a set of tasks with the objective of optimizing one or more performance measures. The actual assignment of starting and completion times of operations on jobs, if the manufacturing order is to be completed on time is known as Production scheduling. The Job Shop Scheduling Problem (JSSP) is one of the most difficult scheduling problems. Since JSSP is NP-complete, that is, the selection of the best scheduling solution is not polynomially bounded, heuristic approaches are often considered. This is an important practical problem in the field of production management and combinatorial optimization. Inspired by the decision-making capability of bee swarms in the nature, this paper proposes an efficient scheduling method based on Artificial Bee Colony (ABC) for solving the JSSP. Most of the researchers in production scheduling are concerned with the optimization of a single criterion. However, the performance of a schedule often involves more than one aspect and, therefore requires a multi-objective treatment. Minimization of makespan and total tardiness are the two performance measures considered in this paper. The Artificial Bee Colony algorithm was coded in MATLAB 2009. A parameter analysis was done to fix the control parameters of Artificial Bee Colony algorithm. The performance of the algorithm was analyzed on the benchmark problems provided by E. Taillard.
... Off-the-shelf integrated IT scheduling DSS have been made available to human schedulers since the 1960s (Herrmann, 2006; O'Brien, 1969). Their functionality typically addresses generic cases of scheduling processes, although their operation can be parameterised to a certain extent. ...
This paper introduces i-DESME, an interdisciplinary framework for the design of IT scheduling Decision Support Systems in small-sized SME industrial environments. The proposed framework adopts a structured software engineering design approach, which has been suitably modified in order to explicitly identify and model the interdisciplinary characteristics that dictate the implementation of scheduling processes within an SME industrial environment. The framework aims to help practitioners design support systems which are not only effective, but are also being trusted and adopted for use by human schedulers. An overview of the framework's application within the environment of a typical micro-sized food manufacturing company is provided. © IFIP International Federation for Information Processing 2014.
... In [22], Herrmann describes production scheduling from a logistics point of view, and with a hierarchical view of the organizational, decision-making, and problemsolving perspectives of production scheduling. In this view, the problem of finding optimal schedules equals production scheduling from the problem-solving perspective. ...
Volvo Aero has invested in a complex production cell containing a set of multipurpose machines. The problem of finding optimal schedules for this multitask cell is a complex combinatorial optimization problem which is recognized as a flexible job shop problem.
This thesis proposes an approach to find such schedules using mathematical optimization. The mathematical models developed so far are presented together with a study of their interrelations, both from a computational and from a modelling perspective. One result of the study is that one of these models, a time-indexed model
with nail variables, outperforms all the others presented. To our knowledge, this model is the first for the flexible job shop problem using this type of variables.
In order to reduce the number of variables in the time-indexed models, a heuristic has been developed which finds an upper bound on the optimal value of the makespan.
Computational results are presented for several variants of the time-indexed model and the engineer’s model, the latter belonging to a family of models widely used in job shop scheduling. The objective employed for the computations is the minimization of a weighted sum of the total tardiness and the sum of job completion
times.
A comparison is made between optimal schedules emanating from the time-indexed model and schedules resulting from the use of three well-known dispatching rules, using the data from 21 real production scenarios. The tardiness and the sum of job completion times are on average 6–22% larger in the schedules resulting from the use of the dispatching rules compared to those obtained in the optimal schedules. The first appended paper provides the results from a similar comparison for a number of scenarios, constructed by using data emanating from the multitask cell.
The computational complexity of the problem of scheduling the multitask cell is also investigated. The second appended paper contains a proof of a complexity result for a related scheduling problem, namely flow-shop scheduling with deteriorating jobs.
... Because of the many kinds of organizational settings in which scheduling is important, it can take many different forms. Figure 1a shows an example that is typical of manufacturing organizations (Herrmann, 2006;Vollmann, Berry, Whybark, & Jacobs, 2005). Orders are received by order managers, who operate under the supervision of the sales manager. ...
In this article, the authors focus on scheduling situations. Because of their unstructured nature and hard combinatorial complexity, scheduling situations have always been a predominant application area for decision support systems (DSSes). After setting out the generic characteristics of a DSS, the authors summarize some of their known limitations in scheduling situations. They argue that scheduling situations focus excessively on performance and effectiveness and neglect the potential of human schedulers. The authors then review field studies to understand the way in which humans perform scheduling, demonstrating the importance of cognitive readiness if schedulers are to schedule efficiently. On the basis of design issues and field studies, the authors go on to refine three factors that influence cognitive readiness that therefore need to be taken into account when designing a DSS, namely, adaptability, flexibility, and acceptability. Finally, the authors show how these concepts can be used to assess DSS user interfaces and algorithms from a cognitive readiness perspective.
... Meanwhile, Herrmann (2003) defined production schedules as plans that state when certain controllable activities (processing of jobs by resources) should take place. He stated that production scheduling coordinate activities to increase productivity and minimize operating cost. ...
Manufacturing firms are expected to adopt best production practices accommodating production scheduling in the environment of demand uncertainty. Given that there is the gap between theory and practice, this study examined the extent production scheduling practices are involved in the operations of Nigerian manufacturing firms; and the relationship between production scheduling and manufacturing performance under demand uncertainty. The study involved the use of questionnaire to collect data from 29 manufacturing firms using random sampling from the population of 33 manufacturing firms registered with the Manufacturers' Association of Nigeria (MAN) in Rivers State, Nigeria. Relevant research questions and research hypotheses of the study variables; master production scheduling, operations scheduling and order scheduling as techniques of production scheduling on firm performance measures of productivity, market share and product quality were stated. Results of the study showed that master production scheduling, operations scheduling and order scheduling have above 75% influence on productivity and market share as compared to less than 40% influence on product quality. Concluding, that production scheduling has significant influence on performance of manufacturing firms in Rivers State, Nigeria, and that demand uncertainty affects the relationship between production scheduling and manufacturing firms' performance. It is recommended that manufacturing firms in Nigeria should effectively adopt production scheduling techniques in enhancing improved levels of manufacturing performance; and that the concept of demand as an input in production scheduling for a production plan period should accommodate unsatisfied demand from the immediate preceding plan period plus the forecast demand for that period.
... The future research will be in the direction of implementation of this or similar simple mathematical models into simulation with two or more different products processed on the group of work centers on the same shop floor. In manufacturing systems with a wide variety of products, processes, and production levels, production schedules can enable better coordination to increase productivity and minimize operating costs [5]. ...
Performance of manufacturing organization depends on production scheduling which is an essential part of the management of production systems. Effective scheduling can lead to performance which results in reducing work in process inventories [1]. According to the lean production principles, excessive production and high inventory level are the biggest waste in production process [2]. Inventory level higher then it is necessary for fulfilling the costumer demands, leads to additional costs in warehouse. Objectives of production control are graphically represented over time in diagrams: inventory, lead time, input orders and output of production process. In this paper, the fact that the input and output amount of work coming from released orders of one product are not equal in every period, results with a technique to balance input against output continuously, to establish a control loop.
Rational decision making requires executing an appropriate decision-making process to select the best alternative. This can be challenging when information is uncertain or when time is limited. This article describes three important perspectives on decision making: (i) the problem-solving perspective, (ii) the decision-making process perspective, and (iii) the decision-making system perspective. This article describes important concepts from these three perspectives, including rationality, multicriteria decision making, group decision making, decision making under uncertainty, game theory, contexts for decision making, decision-making processes, and techniques for modeling and improving decision-making systems. Understanding and applying these concepts can improve decision making. The article will first consider the challenge of selecting the best alternative, which is the problem-solving perspective. Then, the article will discuss the decision-making process perspective: how people make decisions. Finally, the article will describe decisions from the decision-making system perspective by considering the decision-making behaviors and information flow within organizations and how to improve those decision-making systems. This article also provides numerous references to sources that provide additional details about rational decision making.
This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop
and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article
begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial
immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes
of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This
article also, summarizes the direction of current research and suggests areas that might most profitably be given further
scholarly attention.
Product development includes many different types of decision-making by engineers and managers. Design decisions determine the product form and specify the manufacturing processes to be used. Development decisions control the progress of product development projects by specifying which activities should happen, their sequence, and who should perform them. This paper introduces the concept of a decision production system to describe a product development organization as a system of decision-makers who use and create information to develop a product. This perspective does not advocate any particular type of product development process. Instead, it looks at the organization in which the product development process exists and considers the decision-makers as a manufacturing system that can be viewed separately from the organization structure.
Production planning, scheduling and control (PSC) activities are critical to manufacturing businesses competing through responsive order fulfilment. With the current drive for businesses and supply chains to be responsive, a large scale three year empirical study was conducted with five cross-sectoral industrial partners to investigate the reality of PSC practice and to establish ways to improve it. From this in-depth analysis, PSC design rules have been established and used to develop a Toolkit to assist businesses in re-designing their PSC organisation and PSC processes. This paper describes the research and Toolkit development stages and content. It also evaluates the Toolkit's performance in its first full application to a business. The Toolkit is extensive, it is therefore intended that the paper should describe its focus and provide an overview of its applicability and content.
In this chapter, we will review the research conducted on the human factor in planning and scheduling. Specifically, the positive
and negative aspects of the human factor will be discussed. We will also discuss the consequences when these aspects are ignored
or overlooked by the formal or systemized solutions.
Planners in the steel industry design a set of steel slabs to satisfy the order book subject to constraints on (1) achieving a total designed weight for each order using multiples of an order-specific production size range, (2) minimum and maximum sizes for each slab, and (3) feasible assignments of multiple orders to the same slab. We developed a heuristic solution based on matchings and bin packing that a large steel plant uses daily in mill operations.
The 1980s and 1990s have seen a growing interest in research and practice in the use of methodologies within problem contexts characterised by a primary focus on technology, human issues, or power. During the last five to ten years, this has given rise to challenges regarding the ability of a single methodology to address all such contexts, and the consequent development of approaches which aim to mix methodologies within a single problem situation. This has been particularly so where the situation has called for a mix of technological (the so-called 'hard') and human centred (so-called 'soft') methods. The approach developed has been termed mixed-mode modelling. The area of mixed-mode modelling is relatively new, with the phrase being coined approximately four years ago by Brian Lehaney in a keynote paper published at the 1996 Annual Conference of the UK Operational Research Society. Mixed-mode modelling, as suggested above, is a new way of considering problem situations faced by organisations. Traditional technological approaches used in management science have suffered criticisms relating to their adequacy in the past few decades, and these hard approaches have been replaced by soft methods, which consider process more relevant than outcome. However, the sole use of human centred approaches to organisational problems has also proved to be inadequate. Mixed-mode modelling accepts the importance of both process and outcome, and provides enabling mechanisms for hard and soft investigation to be undertaken.
This paper gives an overview on the concepts, models, methods and commercial software packages for production management systems (PMS). We start by presenting the concepts and explaining what is production management, its position and role in CIM, and the structure of PMS. Then we will talk about some concepts relating to MRP, kanban and JIT. The economic aspects and performance indicators of PMS are not ignored. Next we will present the state-of-the-art in the field of PMS models (cam-i, icam, nbs, mmcs, pac, etc.), as well as simulation models. Then we give a state-of-the-art on PMS design methods, in particular the sadt, ssad, merise, grai, ecograi and gim methods. We also present what we consider the three most promising approaches for the PMS design in the future: CIM-OSA, impacs and FOF, which are currently developed within the European ESPRIT programme. Finally a brief summary on the packages used in France for production management is given at the end of the paper.
The hierarchical production planning (HPP) paradigm that is used to control production in many industries has been developed and refined during the past century. In this paper, the basic concepts underlying the HPP paradigm will be briefly reviewed and an enhanced paradigm introduced for rapidly changing industries. The proposed enhancements view the decision making as a dynamic information system in which the role of information is dramatically different from the traditional approaches. The enhancements are at three levels: structural topology of the decision layers, adaptive framework for the internal structure of an enhanced layer, and a special class of heuristics for managing capacity when faced by change.
Although often studied as an isolated optimization problem, production scheduling in practice is a complex flow of information and decision-making. This paper discusses this perspective and presents ways to represent production scheduling systems. The paper uses a case study of a manufacturing facility to illustrate the concepts.
Systems Thinking is a new paradigm set to revolutionize management practice in the 21st century. 'Systems Approaches to Management' is the most comprehensive guide available to the application of this new paradigm in the field of management. The volume:
- traces the emergence of holistic thinking in disciplines such as biology, control engineering, sociology, and the natural sciences
-details and provides a critique, based upon social theory, of the range of systems approaches, methodologies, models and methods
- offers numerous case studies to illustrate systems thinking applied to management
- introduces 'critical systems thinking' as a coherent framework that brings unity to the diversity of different systems approaches and advises managers how best to use them
- provides an accessible source of inspiration for managers, management consultants, scholars, and students
The author covers chaos and complexity theory, the learning organization, system dynamics, living systems theory, soft systems methodology, interactive management, interactive planning, total systems intervention, autopoiesis, management cybernetics, the viable system model, operations research (hard and soft), systems analysis, systems engineering, general system theory, socio-technical systems thinking. the fifth discipline, social systems design, team syntegrity, postmodern systems thinking, critical systems thinking, and much more.
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