Showing papers on "Flow shop scheduling published in 1999"

A review of scheduling research involving setup considerations

Abstract: The majority of scheduling research assumes setup as negligible or part of the processing time. While this assumption simplifies the analysis and/or reflects certain applications, it adversely affects the solution quality for many applications which require explicit treatment of setup. Such applications, coupled with the emergence of production concepts like time-based competition and group technology, have motivated increasing interest to include setup considerations in scheduling problems. This paper provides a comprehensive review of the literature on scheduling problems involving setup times (costs). It classifies scheduling problems into batch and non-batch, sequence-independent and sequence-dependent setup, and categorizes the literature according to the shop environments of single machine, parallel machines, flowshops, and job shops. The suggested classification scheme organizes the scheduling literature involving setup considerations, summarizes the current research results for different problem types, and finally provides guidelines for future research.

SHOP: Simple Hierarchical Ordered Planner

Abstract: SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system with the following characteristics. • SHOP plans for tasks in the same order that they will later be executed. This avoids some goal-interaction issues that arise in other HTN planners, so that the planning algorithm is relatively simple. • Since SHOP knows the complete world-state at each step of the planning process, it can use highly expressive domain representations. For example, it can do planning problems that require complex numeric computations. • In our tests, SHOP was several orders of magnitude faster man Blackbox and several times faster than TLpian, even though SHOP is coded in Lisp and the other planners are coded in C.

Scheduling with time dependent processing times: Review and extensions

Abstract: In classical scheduling theory job processing times are constant However, there are many situations where processing time of a job depends on the starting time of the job in the queue This paper reviews the rapidly growing literature on single machine scheduling models with time dependent processing times Attention is focused on linear, piecewise linear and non-linear processing time functions for jobs We survey known results and introduce new solvable cases Finally, we identify the areas and give directions where further research is needed

Hybrid flow shop scheduling: a survey

Abstract: Extensive work has been done in hybrid flow shop scheduling. This paper reviews the state of art and discusses in details their contributions. The review is concluded with suggestions for future research directions.

Project scheduling : recent models, algorithms, and applications

Abstract: Part I: Classical Models - Classification, Exact Algorithms. 1. A Classification Scheme for Project Scheduling W. Herroelen, et al. 2. Solving Large-Sized Resource-Constrained Project Scheduling Problems P. Brucker, S. Knust. 3. Lower Bounds in Different Problem Classes of Project Schedules with Resource Constraints E. Pesch. 4. Algorithms for Scheduling Projects with Generalized Precedence Relations B. de Reyck, et al. 5. An Exact Solution Procedure for Maximizing the Net Present Value of Cash Flows in a Network S. Baroum, J. Patterson. 6. Solving a Preemptive Project Scheduling Problem with Coloring Techniques L. Bianco, et al. Part II: Classical Models - Heuristics, Benchmark Instances, Software Evolution. 7. Heuristic Algorithms for the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis R. Kolisch, S. Hartman. 8. A Heuristic Procedure for the Multi-Mode Project Scheduling Problem Based on Benders' Decomposition V. Maniezzo, A. Mingozzi. 9. Benchmark Instances for Project Scheduling Problems R. Kolisch, et al. 10. A Survey of Interval Capacity Consistency Tests for Time- and Resource-Constrained Scheduling U. Dorndorf, et al. 11. The Evolution of Software Quality in Project Scheduling C. Maroto, et al. Part III: New Models. 12. Methods for Resource-Constrained Project Scheduling with Regular and Nonregular Objective Functions and Schedule-Dependent Time Windows K. Neumann, J. Zimmerman. 13. Project Scheduling under Discrete and Continuous Resources J. Jozefowska, et al. 14. Scheduling of Projects with Stochastic Evolution Structure K. Neumann. 15. Project Scheduling with Stochastic Activity Interruptions V. Valls, et al. 16. Fuzzy Multi-Mode Resource-Constrained Project Scheduling with Multiple Objectives M. Hapke, et al. 17. Knowledge-Based Multiobjective Project Scheduling Problems J. Nabrzyski, J. W glarz. Part IV: Extensions and Applications. 18. New Modelling Concepts and Their Impact on Resource-Constrained Project Scheduling A. Drexl, et al. 19. Integrating Quality as a Measure of Performance in Resource-Constrained Project Scheduling Problems S.S. Erenguc, O.I. Tukel. 20. Cognitive Science and Project Scheduling: More Realistic Representation P. Baptiste, O. Grunder. 21. On Payment Scheduling in Client-Contractor Negotiations in Projects: An Overview of the Problem and Research Issues N. Dayanand, R. Padman. 22. Project Management in Audit Staff Scheduling B. Dodin.

Production scheduling and rescheduling with genetic algorithms

Abstract: A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

Multiobjective Scheduling by Genetic Algorithms

Abstract: From the Publisher: Multiobjective Scheduling By Genetic Algorithms describes methods for developing multiobjective solutions to common production scheduling situations modeling in the literature as flowshops, job shops and open shops The methodology is metaheuristic, one inspired by how nature has evolved a multitude of coexisting species of living beings on earth "Thus this book is intended for students of industrial engineering, operations research, operations management and computer science, as well as practitioners It may also assist in the development of efficient shop management software tools for schedulers and production planners who face multiple planning and operating objectives as a matter of course

Operations scheduling with applications in manufacturing and services

Abstract: This text provides coverage of scheduling for operations, both manufacturing and services. It includes: reservations systems; systems design; flexible system scheduling; workforce scheduling; and future scheduling issues such as Web-based systems.

Genetic scheduling for parallel processor systems: comparative studies and performance issues

Abstract: Task scheduling is essential for the proper functioning of parallel processor systems. Scheduling of tasks onto networks of parallel processors is an interesting problem that is well-defined and documented in the literature. However, most of the available techniques are based on heuristics that solve certain instances of the scheduling problem very efficiently and in reasonable amounts of time. This paper investigates an alternative paradigm, based on genetic algorithms, to efficiently solve the scheduling problem without the need to apply any restricted assumptions that are problem-specific, such is the case when using heuristics. Genetic algorithms are powerful search techniques based on the principles of evolution and natural selection. The performance of the genetic approach will be compared to the well-known list scheduling heuristics. The conditions under which a genetic algorithm performs best will also be highlighted. This will be accompanied by a number of examples and case studies.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Abstract: Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In ihis study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.

A genetic algorithm approach to a general category project scheduling problem

Abstract: A genetic algorithm (GA) approach is proposed for the general resource-constrained project scheduling model, in which activities may be executed in more than one operating mode, and renewable as well as nonrenewable resource constraints exist. Each activity's operation mode has a different duration and requires different amounts of renewable and nonrenewable resources. The objective is the minimization of the project duration or makespan. The problem under consideration is known to be one of the most difficult scheduling problems, and it is hard to find a feasible solution for such a problem, let alone the optimal one. The GA approach described in this paper incorporates problem-specific scheduling knowledge by an indirect chromosome encoding that consists of selected activity operating modes and an ordered set of scheduling rules. The scheduling rules in the chromosome are used in an iterative scheduling algorithm that constructs the schedule resulting from the chromosome. The proposed GA is denoted a hybrid GA (HGA) approach, since it is integrated with traditional scheduling tools and expertise specifically developed for the general resource-constrained project scheduling problem. The results demonstrate that HGA approach produces near-optimal solutions within a reasonable amount of computation time.

A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop

Abstract: We present a shifting bottleneck heuristic for minimizing the total weighted tardi- ness in a job shop. The method decomposes the job shop into a number of single-machine subproblems that are solved one after another. Each machine is scheduled according to the solution of its corresponding subproblem. The order in which the single machine subproblems are solved has a significant impact on the quality of the overall solution and on the time required to obtain this solution. We therefore test a number of different orders for solving the subprob- lems. Computational results on 66 instances with ten jobs and ten machines show that our heuristic yields solutions that are close to optimal, and it clearly outperforms a well-known dispatching rule enhanced with backtracking mechanisms. © 1999 John Wiley & Sons, Inc. Naval

Survey of Job Shop Scheduling Techniques

Abstract: The sections in this article are 1 Introduction 2 Mathematical Techniques 3 Dispatching Rules 4 Artificial Intelligence (AI) Techniques 5 Artificial Neural Networks 6 Neighborhood Search Methods 7 Fuzzy Logic 8 Swarm 9 Reactive Scheduling 10 Learning in Scheduling 11 Theory of Constraints 12 Summary and Conclusions

Scheduling of Resource-Constrained Projects

Abstract: Notations. Preface. Part I: Project Management: Basics and Scheduling Problems. 1. The Project Management Process. 2. Project Planning and Control. 3. Resource-Constrained Scheduling Problems. Part II: Resource-Constrained Project Scheduling: Solution Methods. 4. Lower Bound Methods. 5. Heuristic Procedures. 6. Exact Procedures. 7. Computational Expirements. 8. Summary and Conclusions. References. Index.

Predictable scheduling of a single machine subject to breakdowns

Abstract: The predictive production schedule has two important functions; allocating shop resources to the different jobs to optimize some measure of shop performance and serving as a basis for planning activities such as material procurement, preventive maintenance and delivery of orders to external or internal customers. This schedule is modified during execution on the occurrence of disruptions such as machine breakdowns. The schedule modification process may delay or render infeasible the execution of activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules which can absorb disruptions without affecting planned activities while maintaining high shop performance. A predictable scheduling approach is presented, where the predictive schedule is built with such objectives. The effects of disruptions on planned activities are measured by the difference between planned and realized job completion times. The specific scheduling model considered is minimizing ma...

System for scheduling and monitoring computer processes

Abstract: A job scheduling device providing a consistent set of application programming interfaces (APIs) (240) compiled and linked into an individual or suite of programs to provide scheduling services on a single computer or across multiple computing platforms, includes a GUI API for retrieving and validated job parameters, a job scheduling API for allocating jobs based on the job parameters, and an enterprise scheduling agent hosted on one or more nodes of the computer platforms. An enterprise communication agent sends messages (200) containing jobs from a computer executing a program utilizing the job scheduling device to the enterprise scheduling agent on a selected node where the job is to execute. Then, the enterprise scheduling agent retrieves job parameters and launches the job on the selected node. The enterprise scheduling agent maintains a local job repository (180) containing job information for each job run on its corresponding node and sends messages to a job data management API (230) to maintain a central job repository (190) containing information on jobs executed on all nodes.

Predictable scheduling of a single machine with breakdowns and sensitive jobs

Abstract: Production schedules released to the shop floor have two important functions: allocating shop resources to different jobs to optimize some measure of shop performance and serving as a basis for planning external activities such as material procurement, preventive maintenance and delivery of orders to customers. Schedule modification may delay or render infeasible the execution of external activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules that can absorb disruptions without affecting planned external activities while maintaining high shop performance. We present a predictable scheduling approach, that inserts additional idle time into the schedule to absorb the impacts of breakdowns. The effects of disruptions on planned support activities are measured by the deviations of job completion times in the realized schedule from those in the predictive schedule. We apply our approach to minimizing total tardiness on a single machine with stochastic...

A Graph-Theoretic Decomposition of the Job Shop Scheduling Problem to Achieve Scheduling Robustness

Abstract: In this paper we study the weighted tardiness job-shop scheduling problem, taking into consideration the presence of random shop disturbances. A basic thesis of the paper is that global scheduling performance is determined primarily by a subset of the scheduling decisions to be made. By making these decisions in an a priori static fashion, which maintains a global perspective, overall performance efficiency can be achieved. Further, by allowing the remaining decisions to be made dynamically, flexibility can be retained in the schedule to compensate for unforeseen system disturbances. We develop a decomposition method that partitions job operations into an ordered sequence of subsets. This decomposition identifies and resolves a "crucial subset" of scheduling decisions through the use of a branch-and-bound algorithm. We conduct computational experiments that demonstrate the performance of the approach under deterministic cases, and the robustness of the approach under a wide range of processing time perturbations. We show that the performance of the method is superior, particularly for low to medium levels of disturbances.

Satisfiability tests and time-bound adjustments for cumulative scheduling problems

Abstract: This paper presents a set of satisfiability tests and time‐bound adjustmentalgorithms that can be applied to cumulative scheduling problems. An instance of thecumulative scheduling problem (CuSP) consists of (1) one resource witha given capacity, and (2) a set of activities, each having a release date, adeadline, a processing time and a resource capacityrequirement. The problem is to decide whether there exists a start time assignment to allactivities such that at no point in time the capacity of the resource is exceeded and alltiming constraints are satisfied. The cumulative scheduling problem can be seen as a relaxationof the decision variant of the resource‐constrained project scheduling problem.We present three necessary conditions for the existence of a feasible schedule. Two ofthem are obtained by polynomial relaxations of the CuSP. The third is based on energeticreasoning. We show that the second condition is closely related to the subset bound, awell‐known lower bound of the m‐machine problem. We also present three algorithms,based on the previously mentioned necessary conditions, to adjust release dates anddeadlines of activities. These algorithms extend the time‐bound adjustment techniquesdeveloped for the one‐machine problem. They have been incorporated in a branch andbound procedure to solve the resource‐constrained project scheduling problem.Computational results are reported.

Overview of a performance evaluation system for global computing scheduling algorithms

Abstract: While there have been several proposals of high-performance global computing systems, scheduling schemes for the systems have not been well investigated. The reason is difficulties of evaluation by large-scale benchmarks with reproducible results. Our Bricks performance evaluation system allows the analysis and comparison of various scheduling schemes in a typical high-performance global computing setting. Bricks can simulate various behaviors of global computing systems, especially the behavior of networks and resource scheduling algorithms. Moreover, Bricks is partitioned into components such that not only can its constituents be replaced to simulate various different system algorithms, but it also allows the incorporation of existing global computing components via its foreign interface. To test the validity of the latter characteristics, we incorporated the NWS (Network Weather Service) system, which monitors and forecasts global computing systems behavior. Experiments were conducted by running NWS under a real environment versus a Bricks-simulated environment, given the observed parameters of the real environment. We observed that Bricks behaved in the same manner as the real environment, and NWS also behaved similarly, making quite comparative forecasts under both environments.