Showing papers on "Flow shop scheduling published in 1998"
TL;DR: A reduction of the set of possible neighbours to a subset for which it can be proved that it always contains the neighbour with the lowest makespan and an efficient approach to compute such a subset of feasible neighbours is presented.
Abstract: The flexible job shop problem is an extension of the classical job shop scheduling problem which allows an operation to be performed by one machine out of a set of machines. The problem is to assign each operation to a machine (routing problem) and to order the operations on the machines (sequencing problem), such that the maximal completion time (makespan) of all operations is minimized. To solve the flexible job shop problem approximately, we use local search techniques and present two neighbourhood functions (Nopt1, Nopt2). Nopt2 is proved to be optimum connected. Nopt1 does not distinguish between routing or sequencing an operation. In both cases, a neighbour of a solution is obtained by moving an operation which affects the makespan. Our main contribution is a reduction of the set of possible neighbours to a subset for which can be proved that it always contains the neighbour with the lowest makespan. An efficient approach to compute such a subset of feasible neighbours is presented. A tabu search procedure is proposed and an extensive computational study is provided. We show that our procedure outperforms previous approaches. Copyright © 2000 John Wiley & Sons, Ltd.
486 citations
01 Jan 1998
TL;DR: This work focuses on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.
Abstract: The scheduling of computer and manufacturing systems has been the subject of extensive research for over forty years. In addition to computers and manufacturing, scheduling theory can be applied to many areas including agriculture, hospitals and transport. The main focus is on the efficient allocation of one or more resources to activities over time. Adopting manufacturing terminology, a job consists of one or more activities, and a machine is a resource that can perform at most one activity at a time. We concentrate on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.
336 citations
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TL;DR: On-line scheduling illustrates many general aspects of competitive analysis, especially in the setting and numerical results, but also in the techniques used.
Abstract: We have seen a variety of on-line scheduling problems. Many of them are understood satisfactorily, but there are also many interesting open problems. Studied scheduling problems differ not only in the setting and numerical results, but also in the techniques used. In this way on-line scheduling illustrates many general aspects of competitive analysis.
253 citations
01 Jun 1998
TL;DR: The approach to minimizing maximum lateness in a job shop environment with random machine breakdowns is applied, and it provides high predictability with minor sacrifices in shop performance.
Abstract: 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 which can absorb disruptions without affecting planned external activities, while maintaining high shop performance. We present a predictable scheduling approach where the predictive schedule is built with such objectives. The procedure inserts additional idle time into the schedule to absorb the impacts of breakdowns. The amount and location of the additional idle time is determined from the breakdown and repair distributions as well as the structure of the predictive schedule. 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 maximum lateness in a job shop environment with random machine breakdowns, and show that it provides high predictability with minor sacrifices in shop performance.
248 citations
30 Mar 1998
TL;DR: A more conservative approach is shown, in which small jobs move ahead only if they do not delay any job in the queue, which produces essentially the same benefits in terms of utilization as the EASY scheduler.
Abstract: Scheduling jobs on the IBM SP2 system is usually done by giving each job a partition of the machine for its exclusive use. Allocating such partitions in the order that the jobs arrive (FCFS scheduling) is fair and predictable, but suffers from severe fragmentation, leading to low utilization. An alternative is to use the EASY scheduler, which uses aggressive backfilling: small jobs are moved ahead to fill in holes in the schedule, provided they do not delay the first job in the queue. The authors show that a more conservative approach, in which small jobs move ahead only if they do not delay any job in the queue, produces essentially the same benefits in terms of utilization. The conservative scheme has the added advantage that queueing times can be predicted in advance, whereas in EASY the queueing time is unbounded.
239 citations
TL;DR: This paper presents extensive sets of randomly generated test problems for the problems of minimizing makespan (Cmax) and maximum lateness (Lmax) in flow shops and job shops.
226 citations
01 Jan 1998
TL;DR: ACO is a new algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems.
Abstract: In this article we present an Ant Colony Optimization approach to the Flow Shop Problem. ACO is a new algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. Artificial ants are used to construct solutions for Flow Shop Problems that subsequently are improved by a local search procedure. Comparisons with other heuristics for the Flow Shop Scheduling problem show that with our approach very promising results are obtained.
212 citations
TL;DR: A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a flow shop with parallel machines and a special advanced method of implementation improves the local search significantly and increases the speed of the algorithm.
168 citations
29 Mar 1998
TL;DR: A parameterized algorithm is introduced that provides good performance across all of these criteria and can be tuned to emphasize either average or worst case waiting time and makes scheduling decisions based on the current queue state.
Abstract: Advances in telecommunications have enabled the deployment of broadcast-based wide-area information services that provide on-demand data access to very large client populations. In order to effectively utilize a broadcast medium for such a service, it is necessary to have efficient, on-line scheduling algorithms that can balance individual and overall performance and can scale in terms of data set sizes, client populations, and broadcast bandwidth. In this study we introduce a parameterized algorithm that provides good performance across all of these criteria and can be tuned to emphasize either average or worst case waiting time. Unlike previous work on low overhead scheduling, the algorithm is not based on estimates of the access probabilities of items, but rather, it makes scheduling decisions based on the current queue state, allowing it to easily adapt to changes in the intensity and distribution of the workload. We examine the performance of the algorithm using a simulation model.
166 citations
TL;DR: The proposed implementation of the tabu search approach suggests simple techniques for generating neighborhoods of a given sequence and a combined scheme for intensification and diversification that has not been considered before that results in an implementation that improves upon previoustabu search implementations that use mechanisms of comparable simplicity.
TL;DR: Computational experiments indicate that HFC performs as well as NEH which is the currently best available constructive heuristic on problems where a permutation schedule is expected to be optimal, however, HFC outperforms NEH on problemsWhere a non-permutation schedule may be optimal.
TL;DR: A systematic procedure to find appropriate values for parameters quickly without much human intervention by using a nonlinear optimization method, the simplex method for nonlinear programming is suggested.
Journal Article•
TL;DR: A Hybrid Genetic Algorithm, combining genetic algorithm with neural network, for Job shop scheduling problem is described and it is shown that this method is good for complex production scheduling, at calculation time and goodness.
Abstract: The neural network model of Job shop scheduling problem is built. The characteristics and properties of its solutions are studied. A Hybrid Genetic Algorithm, combining genetic algorithm with neural network, for Job shop scheduling problem is described. The corresponding simulation shows that our method is good for complex production scheduling, at calculation time and goodness.
26 May 1998
TL;DR: This work model distributed scheduling as a discrete resource allocation problem, and demonstrates the applicability of economic analysis to this framework, and discusses the existence of equilibrium prices, and the quality of equilibrium solutions.
Abstract: Market mechanisms solve distributed scheduling problems by allocating the scheduled resources according to market prices. We model distributed scheduling as a discrete resource allocation problem, and demonstrate the applicability of economic analysis to this framework. Drawing on results from the literature, we discuss the existence of equilibrium prices for some general classes of scheduling problems, and the quality of equilibrium solutions. We then present two auction protocols for implementing solutions, and analyze their computational and economic properties.
Journal Article•
TL;DR: Simulation results show that the method is able to maintain the control of workload without the use of rigid norms, and an alternative release approach is proposed which improves the due date performance significantly.
TL;DR: In this paper, the authors considered costs on exact waiting times between two consecutive tasks instead of minimal waiting times, which gave rise to a nonlinear objective function in the model and showed that such a general solution methodology outperforms specialized algorithms when minimal waiting costs are used.
TL;DR: This article presents an improvement of Brah and Hunsucker's branch and bound algorithm for solving a k-stage hybrid flowshop scheduling problem and proves that the value of their lower bound may decrease along a path of the search tree.
TL;DR: A heuristic algorithm is developed to reduce the mean flowtime in a permutation flowshop environment based on a job insertion method and results show that the proposed algorithm generates more accurate solutions than other heuristics, especially when ratio of the number of jobs and thenumber of machines is greater than or equal to two.
TL;DR: A disjunctive graph representation of this problem is presented and a connected neighborhood structure is proposed that can be used to derive a local search algorithm such as tabu search.
TL;DR: This work treats a problem of scheduling n jobs on a three stages hybrid flowshop of particular structure to minimize the makespan and proposes two heuristic procedures to cope with realistic problems.
TL;DR: A general hierarchical procedure to address real-life job shop scheduling problems, which model the production environment as a queueing network and develops a procedure that is useful for large, real- life shops.
Abstract: We propose a general hierarchical procedure to address real-life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its ow route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time, and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on an Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four-phase, hierarchical, operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real-life shops. We therefore report on a real-life application.
TL;DR: In this paper, the authors present and compare a number of branch and bound algorithms for minimizing the total weighted tardiness in job shops, and obtain optimal solutions for all the instances with ten jobs and ten machines that they consider, including three tardy versions of a well-known 10 × 10 instance introduced by Muth and Thompson [1] in 1963.
Abstract: We present and compare a number of branch and bound algorithms for minimizing the total weighted tardiness in job shops. There are basically two types of branching schemes. The first one inserts operations in a partial schedule, while the second one fixes arcs in the disjunctive graph formulation of the problem. The bounding schemes are based on the analysis of precedence constraints, and on the solution of nonpreemptive single machine subproblems that are subject to so-called delayed precedence constraints. We obtain optimal solutions for all the instances with ten jobs and ten machines that we consider, including three tardiness versions of a well-known 10 × 10 instance introduced by Muth and Thompson [1] in 1963.
21 Apr 1998
TL;DR: The proposed genetic algorithm can be used to solve traditional job shop scheduling, flow shop Scheduling, and open shop scheduling as well as general machine scheduling problems.
Abstract: This paper deals with the so-called general machine scheduling problems. In the general machine scheduling problems, job shop type jobs and open shop type jobs are scheduled together and the imposition of precedence constraints is allowed between operations belonging to either the same job or different jobs. This paper proposes a genetic algorithm to solve such general machine scheduling problems. Some experimental results are presented to show the applicability of the proposed method. The method can be used to solve traditional job shop scheduling, flow shop scheduling, and open shop scheduling as well as general machine scheduling problems.
01 Oct 1998
TL;DR: An efficient pseudo-polynomial time dynamic programming algorithm is proposed that significantly improves the efficiency of the Lagrangean relaxation approach to job-shop scheduling, and makes it possible to optimize "min-max" criteria by LagRangean relaxation.
Abstract: Concerns the use of Lagrangean relaxation for complex scheduling problems. The technique has been used to obtain near-optimal solutions for single machine and parallel machine problems. It consists of relaxing capacity constraints using Lagrange multipliers. The relaxed problem can be decomposed into independent job level subproblems. Luh et al. (1990, 1991) extended the technique to general job shop scheduling by introducing additional Lagrangean multipliers to relax precedence constraints, so that each job level relaxed subproblem can be further decomposed into a set of operation level subproblems which can easily be solved by enumeration. Unfortunately, the operation level subproblems exhibit solution oscillation from iteration to iteration and, in many cases, prevent convergence. Although several methods to prevent oscillation have been proposed, none is satisfactory. We propose an efficient pseudo-polynomial time dynamic programming algorithm. We show that, by extending the technique to job shop scheduling problems, the relaxation of the precedence constraints becomes unnecessary, and thus the oscillation problem vanishes. This algorithm significantly improves the efficiency of the Lagrangean relaxation approach to job-shop scheduling, and makes it possible to optimize "min-max" criteria by Lagrangean relaxation. These criteria have been neglected in the Lagrangean relaxation literature due to their indecomposability. Computational results are given to demonstrate the improvements due to this algorithm.
01 Feb 1998
TL;DR: This article builds upon results of Walker and Otto, who solved a particular instance of the problem, and derives an optimal scheduling for the most general case, namely, moving from a CYCLIC(r) distribution on a P-processor grid to a CYclIC(s) Distribution on a Q-processor Grid, for arbitrary values of the redistribution parameters P, Q, r, and s.
Abstract: This article is devoted to the run-time redistribution of one-dimensional arrays that are distributed in a block-cyclic fashion over a processor grid. While previous studies have concentrated on efficiently generating the communication messages to be exchanged by the processors involved in the redistribution, we focus on the scheduling of those messages: how to organize the message exchanges into "structured" communication steps that minimize contention. We build upon results of Walker and Otto, who solved a particular instance of the problem, and we derive an optimal scheduling for the most general case, namely, moving from a CYCLIC(r) distribution on a P-processor grid to a CYCLIC(s) distribution on a Q-processor grid, for arbitrary values of the redistribution parameters P, Q, r, and s.
TL;DR: Simulation results show that the new rules for production scheduling in semiconductor wafer fabrication give better performance than existing rules with respect to throughput rate, flow time, and workin-process inventory.
TL;DR: Major contributions in solving PiG using a Hopfield neural network, as well as applications of back-error propagation to general scheduling problems are presented.
Abstract: Complete enumeration of all sequences to establish global optimality is not feasible as the search space; for a general job-shop scheduling problem,PiG has an upper bound of (n!)m. Since the early fifties a great deal of research attention has been focused on solving PiG , resulting in a wide variety of approaches such as branch and bound, simulated annealing, tabu search, etc. However, limited success has been achieved by these methods due to the shear intractability of this generic scheduling problem. Recently, much effort has been concentrated on using neural networks to solve PiG as they are capable of adapting to new environments with little human intervention and can mimic thought processes. Major contributions in solving PiG using a Hopfield neural network, as well as applications of back-error propagation to general scheduling problems are presented. To overcome the deficiencies in these applications a modified back-error propagation model, a simple yet powerful architecture which can be successfu...
TL;DR: Two new heuristics for the flowshop scheduling problem with sequence-dependent setup times (SDSTs) and a greedy randomized adaptive search procedure (GRASP) which is a technique that has achieved good results on a variety of combinatorial optimization problems are presented.
TL;DR: In this paper, a mathematical model is developed to examine the machines' layout and the pattern of material flow for the typical job shop and flow shop manufacturing environments, and a genetic algorithm is used to provide the optimal solution to the facilities' layout problem.