SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> Abstract This paper considers the gap between scheduling theory and scheduling practice. The development and the main results of classical scheduling theory are reviewed and presented in an easily accessible way. Recent trends in scheduling research which attempt to make it more relevant and applicable are described. The nature of the gap between theory and practice is discussed. The failure of classical scheduling theory to address the total environment within which the scheduling function operates is noted. However, scheduling research in operations management and manufacturing systems tends to ignore the rich vein of methods, techniques and results in the classical theory. The need for an integrated scheduling research effort, containing elements of both approaches, is stressed. <s> BIB001 </s> SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> This book on scheduling covers theoretical models as well as scheduling problems in the real world. Author Michael Pinedo also includes a CD that contains slide-shows from industry and movies dealing with implementations of scheduling systems. The book consists of three parts. The first part focuses on deterministic scheduling with the associated combinatorial problems. The second part covers probabilistic scheduling models. In this part it is assumed that processing times and other problem data are not known in advance. The third part deals with scheduling in practice. It covers heuristics that are popular with practitioners and discusses system design and development issues. Each chapter contains a series of computational and theoretical exercises. This book is of interest to theoreticians and practitioners alike. Graduate students in operations management, operations research, industrial engineering and computer science will find the book to be an accessible and invaluable resource. Scheduling will serve as an essential reference for professionals working on scheduling problems in manufacturing and computing environments. Michael Pinedo is the Julius Schlesinger Professor of Operations Management at New York University. <s> BIB002 </s> SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> Flexible manufacturing systems (FMS) have been developed with the hope that they would provide a means to tackle a threefold challenge — better quality, lower cost and shorter lead times — by integrating machine tools, robots, material handling and storage systems, and computers. Control of the integrated system presented a new set of problems as well as challenges, which have been receiving considerable attention from the academic community as well as from industrial system users. Intelligent control, which involves using computers to assist in decision making at various stages of the control process, has been advocated by many researchers as a possible avenue to reach a solution to these problems. This paper provides a review of the state of the art in intelligent control of FMS, in an attempt to supplement earlier general reviews via a more focused perspective. The principles of several techniques, namely simulation, knowledge based, example based, petri nets, and hybrid approaches are briefly introduced, and publications are reviewed, followed by discussions ontheir potential. Suggestions for further research and development are also enumerated. <s> BIB003 </s> SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> Abstract Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. © 1999 Elsevier Science B.V. All rights reserved. <s> BIB004 </s> SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> In research on generating a predictive schedule, the scheduling problem is often viewed as a deterministic problem. However, the real-life job shop environment is stochastic in that information on job attributes and shop floor status is not precisely known in advance. In this situation, in order to increase the effectiveness of a predictive schedule in practice, the focus should be on creating a robust schedule. The purpose of this paper is to investigate the robustness of a number of scheduling rules in a dynamic and stochastic environment using the rolling time horizon approach. A cost-based performance measure is used to evaluate the scheduling rules. The simulation results, under various conditions in a balanced and unbalanced shop, are presented and the effects of the rescheduling interval and operational factors including shop load conditions and a bottleneck on the robustness of the schedule are studied. From the results the key factors that influence the robustness of a scheduling system are ident... <s> BIB005 </s> SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS <s> INTRODUCTION <s> Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice. <s> BIB006
Scheduling is defined as the allocation of resources to jobs over time. It is a decision-making process with the goal of optimising one or more objectives BIB002 . The objectives can be the minimisation of the completion time of jobs, mean flow time, lateness of jobs, processing cost, etc. Scheduling plays an important role in many manufacturing and production systems. Scheduling problems, which are concerned with searching for optimal (or near-optimal) predictive schedules subject to a number of constraints, are mostly NP-hard. So far, research has primarily been focused on finding optimal (or nearoptimal) solutions for static models with respect to various measures, i.e. shortest total processing time, minimal production cost, etc. These approaches mostly have used the implicit assumption of static environments without any kind of failures. Extensive literature reviews on static deterministic scheduling can be found in  BIB004 BIB002 BIB002 2 part of production systems planning for many reasons BIB005 BIB006 . A predictive schedule serves as an overall plan upon which many other shop activities are based. Examples of such activities include short-term labour planning, ordering and preparation of raw material, and planning for tooling, set-up activities, etc. It can enable better coordination by properly planning the timing of shop floor activities to increase productivity and minimise operating costs. A predictive schedule can identify resource conflicts, control the release of jobs to the shop, and ensure that required raw materials are ordered in time. It gives shop floor personnel an explicit statement of what should be done so that managers can measure their performance. Unfortunately, most manufacturing systems operate in dynamic environments where usually inevitable unpredictable real-time events may cause a change in the scheduled plans, and the optimal or nearoptimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. Examples of such real-time events include machine failures, arrival of urgent jobs, due date change, etc. BIB001 addressed the nature of the gap between the scheduling theory and scheduling practice, the failure of classical scheduling theory to respond to the needs of practical environments, and recent trends in scheduling research which attempt to make it more relevant and applicable. BIB003 , in their comprehensive survey on intelligent real-time control in flexible manufacturing systems, stated that comparison of theory and scheduling practice showed very little correspondence between the two. Cowling and Johanson (2002) addressed an important gap between scheduling theory and practice, and stated that scheduling models and algorithms are unable to make use of real-time information. Until very recently the problem of scheduling in the presence of real-time events, termed dynamic scheduling, has been largely neglected and not many surveys have been done in this area. In this paper, we focus on a number of issues that have come up in recent years on dynamic scheduling in manufacturing systems. We are primarily concerned with the issue of how to handle the occurrence of real-time events during the execution of a given schedule on the shop floor. The paper is organised as follows. Section 2 defines the categories of real-time events. Next, Section 3 defines the dynamic scheduling approaches. Section 4 describes discusses the rescheduling policies and rescheduling strategies. Section5 gives a review on previous research work on techniques used to solve the problem of dynamic scheduling, such as dispatching rules, heuristics, meta-heuristics, knowledge-based systems, fuzzy logic, neural networks, hybrid techniques, and multi-agent based systems. Section 6 presents a comparative study of the different techniques. Finally, summary and conclusions conclude the paper.