Showing papers on "Job shop published in 1994"

Principles of Sequencing and Scheduling

Abstract: An up-to-date and comprehensive treatment of the fundamentals of scheduling theory, including recent advances and state-of-the-art topics Principles of Sequencing and Scheduling strikes a unique balance between theory and practice, providing an accessible introduction to the concepts, methods, and results of scheduling theory and its core topics. With real-world examples and up-to-date modeling techniques, the book equips readers with the basic knowledge needed for understanding scheduling theory and delving into its applications. The authors begin with an introduction and overview of sequencing and scheduling, including single-machine sequencing, optimization and heuristic solution methods, and models with earliness and tardiness penalties. The most current material on stochastic scheduling, including correct scheduling of safety time and the use of simulation for optimization, is then presented and integrated with deterministic models. Additional topical coverage includes: Extensions of the basic model Parallel-machine models Flow shop scheduling Scheduling groups of jobs The job shop problem Simulation models for the dynamic job shop Network methods for project scheduling Resource-constrained project scheduling Stochastic and safe scheduling Extensive end-of-chapter exercises are provided, some of which are spreadsheet-oriented, and link scheduling theory to the most popular analytic platform among today's students and practitionersthe Microsoft Office Excel spreadsheet. Extensive references direct readers to additional literature, and the book's related Web site houses material that reinforces the book's concepts, including research notes, data sets, and examples from the text. Principles of Sequencing and Scheduling is an excellent book for courses on sequencing and scheduling at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, computer science, operations research, and engineering. Kenneth R. Baker, PhD, is Nathaniel Leverone Professor of Management at Dartmouth College. A Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), Dr. Baker has published extensively in his areas of research interest, which include mathematical modeling, spreadsheet engineering, and scheduling. He is the coauthor of Management Science: The Art of Modeling with Spreadsheets, Second Edition, also published by Wiley. Dan Trietsch, PhD, is Professor of Industrial Engineering at the American University of Armenia. He has authored over thirty journal articles on topics such as network design, statistical quality control, and various aspects of scheduling.

Robustness measures and robust scheduling for job shops

Abstract: A robust schedule is defined as a schedule that is insensitive to unforeseen shop floor disturbances given an assumed control policy. In this paper, a definition of schedule robustness is developed which comprises two components: post-disturbance make-span and post-disturbance makespan variability. We have developed robustness measures and robust scheduling methods for the case where a “right-shift” control policy is used. On occurrence of a disruption, the right-shift policy maintains the scheduling sequence while delaying the unfinished jobs as much as necessary to accommodate the disruption. An exact measure of schedule robustness is derived for the case in which only a single disruption occurs within the planning horizon. A surrogate measure is developed for the more complex case in which multiple disruptions may occur. This surrogate measure is then embedded in a genetic algorithm to generate robust schedules for job-shops. Experimental results show that robust schedules significantly outper...

Improved Approximation Algorithms for Shop Scheduling Problems

Abstract: In the job shop scheduling problem, there are $m$ machines and $n$ jobs. A job consists of a sequence of operations, each of which must be processed on a specified machine, and the aim is to complete all jobs as quickly as possible. This problem is strongly ${\cal NP}$-hard even for very restrictive special cases. The authors give the first randomized and deterministic polynomial-time algorithms that yield polylogarithmic approximations to the optimal length schedule. These algorithms also extend to the more general case where a job is given not by a linear ordering of the machines on which it must be processed but by an arbitrary partial order. Comparable bounds can also be obtained when there are $m'$ types of machines, a specified number of machines of each type, and each operation must be processed on one of the machines of a specified type, as well as for the problem of scheduling unrelated parallel machines subject to chain precedence constraints.

Scheduling Theory: Multi-Stage Systems

Abstract: Preface Introduction 1: Flow Shop 1 Maximal Completion Time Two Machines 2 Maximal Completion Time Three and More Machines 3 Maximal Completion Time with No-Wait in Process 4 Maximal Lateness 5 Total Flow Time 6 Ordered Matrices of Processing Times 7 Dominant Matrices of Processing Times 8 Approximation Algorithms 9 Bibliography and Review 2: Job Shop 1 Optimal Processing of Two Jobs 2 Maximal Lateness 3 Maximal Completion Time Equal Processing Times 4 Maximal Completion Time Arbitrary Processing Times 5 Maximal Completion Time with No-Wait in Process 6 Bibliography and Review 3: Open Shop 1 Maximal Completion Time Two Machines 2 Maximal Completion Time Three and More Machines 3 Maximal Completion Time Preemption 4 Maximal Completion Time Precedence Constraints 5 Due Dates 6 Total Flow Time Equal Processing Times 7 Total Flow Time Arbitrary Processing Times 8 Bibliography and Review 4: Mixed Graph Problems 1 Network Representation of Processing Systems 2 Mixed Graphs 3 Brand-and-Bound Method 4 Optimization of Processing Systems 5 Stability of Optimal Schedules 6 Bibliography and Review References Additional References Index

Impact of sequence-dependent setup time on job shop scheduling performance

Abstract: In a production system using multi-purpose and flexible machines, reducing setup time is an important task for better shop performance. Numerous cases were reported about successful reduction of setup times by standardization of setup procedures. However, setup times have not been eliminated and remain an important element of real production problems for production systems such as commercial printing, plastics manufacturing, metal processing, etc. It is especially critical when the setup time is sequence dependent. In this situation, shop performance cannot be effectively improved without the aid of an appropriate scheduling procedure. Review of the past studies shows that there has not been a significant amount of research done on the scheduling procedure for a dynamic job shop with sequence dependent setup times. This paper investigates the job shop scheduling problems that are complicated by sequence-dependent setup times. The study classifies and tests scheduling rules by considering whether setup tim...

Scheduling products with bills of materials using an improved Lagrangian relaxation technique

Abstract: A bill of materials specifies the sequence in which parts are to be processed and assembled in order to manufacture a deliverable product. In practice, a bill of materials may be quite complex, involving hundreds of parts to be processed on a number of limited resources, making scheduling difficult. This has forced many practitioners to turn to Material Requirements Planning (MRP) and heuristic rules to perform scheduling. These methods are seldom integrated, resulting in unreliable completion times for products and, hence, low customer satisfaction. This paper addresses the issue of integrally scheduling parts that are related through a bill of materials for the purpose of improving the on-time performance of products as well as reducing work-in-process (WIP) inventory. The technique presented here is based on an existing Lagrangian relaxation (LR) approach for the scheduling of independent parts in a job shop. An auxiliary problem formulation with a modified subgradient method is adopted to improve the computation time of the existing LR approach. This improved LR approach allows the bill of material constraints to be considered directly in the problem formulation. >

An expert neural network system for dynamic job shop scheduling

Abstract: The objective of this research is to apply the neural network approach to the dynamic job shop scheduling problem. A feed-forward back propagation neural network is designed and trained to recognize the individual contributions of traditional dispatch rules. The network is incorporated into an expert system which activates the network according to the prevailing shop environment. The effectiveness of the approach is compared with the traditional dispatch rule approach as well as a composite rule expert system. Results of scheduling with a neural network show that the network is able to perform well against its component factors for job lots with varying arrival rates.

An Overview of Scheduling Problems Arising in Satellite Communications

Abstract: Satellite communications, like batches of work in a job shop, need to be scheduled in order to use their resources as efficiently as possible The most common satellite communications system in use today is known as Time Division Multiple Access (TDMA), in which data from earth stations is buffered before being transmitted to the appropriate receiver on a satellite Cycles of transmission are fixed for all stations Since the same satellite will be used for routeing data in several different ways, a schedule must be devised to use the receivers, repeaters and transmitters on board to minimize the time needed for completion of a batch of work This paper is a survey of current scheduling algorithms used for optimizing satellite communications resources Apart from telecommunications, the methods presented here could be applied to more general scheduling problems with renewable resources but without precedence constraints

Stable earliest starting schedules for periodic job shops: A linear system approach

Abstract: We consider a cyclic job shop where an identical mixture of parts of different types, called a minimal part set (MPS), is produced repetitively in the same processing order. The precedence relationships among events (start of operation) are represented by a directed graph that has a recurrent structure. Each operation starts as soon as all its preceding operations are complete (called earliest starting). There is a class of desirable schedules that has the minimum cycle time and an identical schedule pattern for every MPS. By using linear system theory on minimax algebra, we characterize the set of all possible such schedules. We develop an efficient algorithm to find one among such schedules that minimizes a performance measure related to work-in-progress inventory. We also discuss an application to a flexible manufacturing system.

Sequencing jobs on an automobile assembly line: objectives and procedures

Abstract: Here a paced automobile assembly line is considered with no buffers between stations. Setup costs are incurred each time sequence of jobs switches colour in the paint shop, and utility work costs are incurred when some amount of work on a job cannot be completed within the boundary of a trim station. Although there has been a concerted effort to reduce setup costs, the nature of the changeovers makes it difficult to reduce to zero. Thus, there is a need for procedures that systematically consider both setup and utility costs, not only to improve current operations, but also to evaluate the economic benefit of investments in setup cost reduction. First, an algorithm with a look-ahead provision is developed to determine sequences for trim stations only. Later, setup cost is included and solution approaches are developed based on the previous algorithm. Extensive empirical study is done by using real as well as random data to evaluate performances of procedures.

A simulated annealing approach to job shop scheduling using critical block transition operators

Abstract: The job shop scheduling problem is one of the most difficult NP hard combinatorial optimization problems. This research investigates finding optimal and near optimal schedules using simulated annealing and a schedule permutation procedure. New schedules are generated by permuting operations within existing schedules. Simulated annealing probabilistically chooses one of the new schedules and probabilistically accepts or rejects it, allowing importance sampling search over the job shop schedule space. The initial and (minimum) final temperatures are adaptively determined a priori, and a reintensification strategy that improves the search by resetting the current temperature and state. Experimental results show this simple and flexible method can find near optimal schedules and often outperforms previous SA approaches. >

Stochastic dynamic job shops and hierarchical production planning

Abstract: This paper presents an asymptotic analysis of hierarchical production planning in a general manufacturing system consisting of a network of unreliable machines producing a variety of products. The concept of a dynamic job shop is introduced by interpreting the system as a directed graph, and the structure of the system dynamics is characterized for its use in the asymptotic analysis. The optimal control problem for the system is a state-constrained problem, since the number of parts in any buffer between any two machines must remain nonnegative. A limiting problem is introduced in which the stochastic machine capacities are replaced by corresponding equilibrium mean capacities, as the rate of change in machine states approaches infinity. The value function of the original problem is shown to converge to that of the limiting problem, and the convergence rate is obtained. Furthermore, near-optimal controls for the original problem are constructed from near-optimal controls of the limiting problem, and an error estimate is obtained on the near optimality of the constructed controls. >

Acquiring and operationalizing worker flexibility in dual resource constrained job shops with worker transfer delays and learning losses

Abstract: Although several studies within the dual resource constrained (DRC) job shop literature have investigated issues related to worker flexibility, the costs associated with acquiring and operationalizing this flexibility have been addressed in a limited fashion. This study is aimed at understanding whether cross-training workers in DRC job shops in the presence of worker transfer delays, worker learning effects, and worker attrition improves shop performance. Two worker assignment control rules, and two rules to select the department for the transferring worker, are also tested to understand how workers should be assigned to alternate tasks in different departments. Results from our study indicate that cross-training workers in at least two different departments improves shop performance even when large transfer delays are present. Extensive cross-training is shown to increase the associated costs, especially for the centralized transfer rule. Consequently, the decentralized assignment control policy is recommended since it performs well even in the presence of large transfer delays for the cross-trained workers. Consistent with the prior literature, the rules to select the next department for the transferring worker are shown to have negligible impact on the shop performance. Worker utilization level is shown to be the key underlying variable which explains these performance trends and leads to the associated recommendations.

Evaluating variance control, order review/release and dispatching: a regression analysis

Abstract: The challenge of improving performance in the job shop can be met in a number of ways. We can rely on variance control to reduce variances created either in the planning system (through uneven or fluctuating loads) or on the shop floor (through varying processing times for released batches). We can rely on Order Review/Release (ORR). Finally, we can turn to one of the various dispatching rules that are available. Each option has been previously examined. However, this study examines all of these options simultaneously within the context of a simulated simple job shop. Based on a regression analysis of the data generated, several interesting results are uncovered. First, the results show that the presence of variance control at both the planning and shop floor levels can greatly enhance the effectivess of ORR. This result partially helps resolve the controversy surrounding ORR. Second, the results show that Shortest Processing Time (SPT), while extremely effective in an uncontrolled environment, r...

A game-theoretic control approach for job shops in the presence of disruptions

Abstract: A methodology inspired by a game-theoretic view of the on-line control problem for job-shops is developed which allows the use of static off-line schedules in uncertain environments, and the explicit incorporation of deterministic and stochastic information concerning future disturbances. A discrete event dynamic system representation is used to formulate the control problem. The control objectives are to minimize expected makespan and deviations from an off-line schedule. Computational tractability is achieved through a graph-theoretic decomposition of the job-shop scheduling problem, the development of fast rescheduling heuristics, and efficient sampling of future events. A heuristic search algorithm is developed for problem resolution. Experimental results show that the methodology significantly outperforms existing control methods such as ‘total rescheduling’ and ‘right-shift.’ Most importantly, the control methodology demonstrates consistent performance and small CPU time requirements throughout the ...

Impact of family-based scheduling on transfer batches in a job shop manufacturing cell

Abstract: Past research has shown that the performance of manufacturing cells can be improved if family orientated scheduling heuristics are used, or if jobs are split into smaller transfer batches for processing and material handling purposes. The research has also shown that large amounts of lot splitting results in increased setup frequency due to the reduction in transfer batch size. This can offset any gains in performance. This study examines the combined effect of lot splitting in a manufacturing cell that utilizes family-based scheduling heuristics. The results show that family-based scheduling is an effective means of reducing the negative impact of lot splitting on flow time. Lot splitting, however, has little benefit for due date performance even when scheduling rules that reduce setup frequency are used.