Showing papers on "Job shop scheduling published in 1996"

A Fast Taboo Search Algorithm for the Job Shop Problem

Abstract: A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented. The algorithm is based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions. Computational experiments up to 2,000 operations show that the algorithm not only finds shorter makespans than the best approximation approaches but also runs in shorter time. It solves the well-known 10 × 10 hard benchmark problem within 30 seconds on a personal computer.

Scheduling Computer and Manufacturing Processes

Abstract: Written in a clear and concise manner this book provides a theoretical and application oriented analysis of deterministic scheduling problems arising in computer and manufacturing environments. Various scheduling problems are discussed where different problem parameters such as task processing times, urgency weights, arrival times, deadlines, precedence constraints, and processor speed factor are involved. Polynomial and exponential time optimization algorithms as well as approximation and heuristic approaches are presented and discussed. Moreover, resource-constrained, imprecise computation, flexible flow shop and dynamic job shop scheduling, as well as flexible manufacturing systems, are considered. An excellent analysis based on real-world applications with plenty of examples.

Cycle-static dataflow

Abstract: We present cycle-static dataflow (CSDF), which is a new model for the specification and implementation of digital signal processing algorithms. The CSDF paradigm is an extension of synchronous dataflow that still allows for static scheduling and, thus, a very efficient implementation of an application. In comparison with synchronous dataflow, it is more versatile because it also supports algorithms with a cyclically changing, but predefined, behavior. Our examples show that this capability results in a higher degree of parallelism and, hence, a higher throughput, shorter delays, and less buffer memory. Moreover, they indicate that CSDF is essential for modelling prescheduled components, like application-specific integrated circuits. Besides introducing the CSDF paradigm, we also derive necessary and sufficient conditions for the schedulability of a CSDF graph. We present and compare two methods for checking the liveness of a graph. The first one checks the liveness of loops, and the second one constructs a single-processor schedule for one iteration of the graph. Once the schedulability is tested, a makespan optimal schedule on a multiprocessor can be constructed. We also introduce the heuristic scheduling method of our graphical rapid prototyping environment (GRAPE).

Simulated annealing metaheuristics for the vehicle routing problem with time windows

Abstract: This paper develops simulated annealing metaheuristics for the vehicle routing and scheduling problem with time window constraints. Two different neighborhood structures, the λ-interchange mechanism of Osman and thek-node interchange process of Christofides and Beasley, are implemented. The enhancement of the annealing process with a short-term memory function via a tabu list is examined as a basis for improving the metaheuristic approach. Computational results on test problems from the literature as well as large-scale real-world problem are reported. The metaheuristics achieve solutions that compare favorably with previously reported results.

Stochastic optimization of unit commitment: a new decomposition framework

Abstract: This paper presents a new stochastic decomposition method well-suited to deal with large-scale unit commitment problems. In this approach, random disturbances are modeled as scenario trees. Optimization consists in minimizing the average generation cost over this "tree-shaped future". An augmented Lagrangian technique is applied to this problem. At each iteration, nonseparable terms introduced by the augmentation are linearized so as to obtain a decomposition algorithm. This algorithm may be considered as a generalization of price decomposition methods, which are now classical in this field, to the stochastic framework. At each iteration, for each unit, a stochastic dynamic subproblem has to be solved. Prices attached to nodes of the scenario trees are updated by the coordination level. This method has been applied to a daily generation scheduling problem. The use of an augmented Lagrangian technique, provides satisfactory convergence properties to the decomposition algorithm. Moreover, numerical simulations show that compared to a classical deterministic optimization with reserve constraints, this new approach achieves substantial savings.

Decomposition Methods for Complex Factory Scheduling Problems

Abstract: Preface. 1. Introduction. 2. Industrial Context and Motivation for Decomposition Methods. 3. Review of Decomposition Methods for Factory Scheduling Problems. 4. Modelling Interactions Between Subproblems: The Disjunctive Graph Representation and Extensions. 5. Workcenter-Based Decomposition Procedures for the Classical Job Shop Environment. 6. A Generic Decomposition Procedure for Semiconductor Testing Facilities. 7. Time-Based Decomposition Procedures for Single-Machine Subproblems with Sequence-Dependent Setup Times. 8. Time-Based Decomposition Procedures for Parallel Machine Subproblems with Sequence-Dependent Setup Times. 9. Naive Rolling Horizon Procedures for Job Shop Scheduling. 10. Tailored Decomposition Procedures for Semiconductor Testing Facilities. 11. Computational Results for Job Shops with Single and Parallel Machine Workcenters. 12. The Effects of Subproblem Solution Procedures and Control Structures. 13. Conclusions and Future Directions. Author Index.

On Permutation Representations for Scheduling Problems

Abstract: In this paper we concentrate on job shop scheduling as a representative of constrained combinatorial problems. We introduce a new permutation representation for this problem. Three crossover operators, different in tending to preserve the relative order, the absolute order, and the position in the permutation, are defined. By experiment we observe the strongest phenotypical correlation between parents and offspring when respecting the absolute order. It is shown that a genetic algorithm using an operator which preserves the absolute order also obtains a superior solution quality.

Task clustering and scheduling for distributed memory parallel architectures

Abstract: This paper addresses the problem of scheduling parallel programs represented as directed acyclic task graphs for execution on distributed memory parallel architectures. Because of the high communication overhead in existing parallel machines, a crucial step in scheduling is task clustering, the process of coalescing fine grain tasks into single coarser ones so that the overall execution time is minimized. The task clustering problem is NP-hard, even when the number of processors is unbounded and task duplication is allowed. A simple greedy algorithm is presented for this problem which, for a task graph with arbitrary granularity, produces a schedule whose makespan is at most twice optimal. Indeed, the quality of the schedule improves as the granularity of the task graph becomes larger. For example, if the granularity is at least 1/2, the makespan of the schedule is at most 5/3 times optimal. For a task graph with n tasks and e inter-task communication constraints, the algorithm runs in O(n(n lg n+e)) time, which is n times faster than the currently best known algorithm for this problem. Similar algorithms are developed that produce: (1) optimal schedules for coarse grain graphs; (2) 2-optimal schedules for trees with no task duplication; and (3) optimal schedules for coarse grain trees with no task duplication.

Evolutionary Search and the Job Shop

Abstract: 1. Introduction.- 2. Job Shop Scheduling.- 3. Local Search Techniques.- 4. Evolutionary Algorithms.- 5. Perspectives on Adaptive Scheduling..- 6. Population Flow in Adaptive Scheduling.- 7. Adaptation of Structured Populations.- 8. A Computational Study.- 9. Conclusions and Outlook.- References.

Job Shop Scheduling by Local Search

Abstract: We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their effectiveness and efficiency on a standard set of problem instances.

A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem

Abstract: From a computational point of view, the job-shop scheduling problem is one of the most notoriously intractable NP-hard optimization problems. In spite of a great deal of substantive research, there are instances of even quite modest size for which it is beyond our current understanding to solve to optimality. We propose several new lower bounding procedures for this problem, and show how to incorporate them into a branch-and-bound procedure. Unlike almost all of the work done on this problem in the past thirty years, our enumerative procedure is not based on the disjunctive graph formulation, but is rather a time-oriented branching scheme. We show that our approach can solve most of the standard benchmark instances, and obtains the best known lower bounds on each.

Energy minimization using multiple supply voltages

Abstract: We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data flow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.

Overview of Scheduling and Planning of Batch Process Operations

Abstract: Scheduling of batch operations is an important area of batch process systems engineering which has been receiving increasing attention in the last decade, especially in its role within computer integrated process operations. In this paper, we review the basic issues which scheduling methodology seeks to address and outline some of the reasons for the growth of interest in this field. The components of the scheduling problem are described and the main threads of the available recent solution methodology are reviewed.

Minimum inventory variability schedule with applications in semiconductor fabrication

Abstract: A typical semiconductor wafer fab contains many different products and processes, some with small quantities, competing for resources. Each product row can contain hundreds of processing steps demanding production time of the same resource many times during the row. When this re-entry requirement is compounded with multiple product flows, short interval scheduling becomes important. Scheduling to reduce variations and to balance the whole wafer production line becomes a very complex issue. We investigate in this paper a new scheduling policy called minimum inventory variability scheduling (MIVS). This scheduling policy can significantly reduce the mean and variance of cycle-time in semiconductor fabs. The conclusions are based on the real world implementation in two major semiconductor fabs since 1990, and a simulation study of a much simplified hypothetical re-entrant network to capture the nature of semiconductor manufacturing. A discrete event simulation model was used to compare MIVS with five different popular dispatching policies (FIFO, SNQ, LNQ, RAN, and CYC) practised in wafer fabrication environments. The results gained on two factory floors and the simulation model indicate that dispatching policies have a significant impact on performance. The simulation results show that the MIVS dispatching policy demonstrated a percentage improvement over all other tested dispatching policies.

Evolutionary Search and the Job Shop: Investigations on Genetic Algorithms for Production Scheduling

Abstract: 1. Introduction.- 2. Job Shop Scheduling.- 3. Local Search Techniques.- 4. Evolutionary Algorithms.- 5. Perspectives on Adaptive Scheduling..- 6. Population Flow in Adaptive Scheduling.- 7. Adaptation of Structured Populations.- 8. A Computational Study.- 9. Conclusions and Outlook.- References.

Duality and linear programs for stability and performance analysis of queuing networks and scheduling policies

Abstract: We consider the problems of performance analysis and stability/instability determination of queuing networks and scheduling policies. We exhibit a strong duality relationship between the performance of a system and its stability analysis via mean drift. We obtain a variety of linear programs (LPs) to conduct such stability and performance analyses. A certain LP, called the performance LP, bounds the performance of all stationary nonidling scheduling policies. If it is bounded, then its dual, called the drift LP, has a feasible solution which is a copositive matrix. The quadratic form associated with this copositive matrix has a negative drift, showing that all stationary nonidling scheduling policies result in a geometrically converging exponential moment. These results carry over to fluid models, allowing the study of networks with nonexponential distributions. If a modification of the performance LP, called the monotone LP, is bounded, then the system is stable. Finally, there is a another modification of the performance LP, called the finite time LP. It provides transient bounds on the performance of the system from any initial condition.

Primary production scheduling at steelmaking industries

Abstract: This paper describes primary production scheduling in the steel industry—the problem and the approaches to the solution. The scheduling problem in steel plants is known to be among the most difficult of several industrial scheduling problems. We first describe the main steelmaking processes and show how scheduling affects the effectiveness of plant operations. We characterize the problems associated with scheduling steelmaking activities to achieve business objectives of delivering quality steel on time to customers, while minimizing operating costs. We then describe several different approaches for computerized scheduling solutions. They include application of techniques in operations research, artificial intelligence, and a hybrid of these two. We conclude by describing advanced techniques for integrated scheduling of steel plants.

A branch & bound method for the general-shop problem with sequence dependent setup-times

Abstract: A branch & bound algorithm is presented for a very general scheduling problem withn jobs andm machines. Each job consists of a set of operations. Each operation has to be processed on a dedicated machine. There may be arbitrary precedence relations between the operations. The set of all operations is partitioned into groups. If on a machine an operation belonging to groupG g is processed immediately after an operation belonging to groupG f there is a setup ofs fg time units. We assume thats fg=0 iff=g and that thes fg satisfy the triangle inequality. Computational results for this general problem as well as for special cases like the job-shop problem and the open-shop problem are reported.