Showing papers on "Flow shop scheduling published in 1996"
TL;DR: Several well-documented applications of no-wait and blocking scheduling models are described and some ways in which the increasing use of modern manufacturing methods gives rise to other applications are illustrated.
Abstract: An important class of machine scheduling problems is characterized by a no-wait or blocking production environment, where there is no intermediate buffer between machines. In a no-wait environment, a job must be processed from start to completion, without any interruption either on or between machines. Blocking occurs when a job, having completed processing on a machine, remains on the machine until a downstream machine becomes available for processing. A no-wait or blocking production environment typically arises from characteristics of the processing technology itself, or from the absence of storage capacity between operations of a job. In this review paper, we describe several well-documented applications of no-wait and blocking scheduling models and illustrate some ways in which the increasing use of modern manufacturing methods gives rise to other applications. We review the computational complexity of a wide variety of no-wait and blocking scheduling problems and describe several problems which remain open as to complexity. We study several deterministic flowshop, jobshop, and openshop problems and describe efficient and enumerative algorithms, as well as heuristics and results about their performance. The literature on stochastic no-wait and blocking scheduling problems is also reviewed. Finally, we provide some suggestions for future research directions.
815 citations
TL;DR: It is shown that the performance-ranking of priority rules does not differ for single-pass scheduling and sampling, that sampling improves the performance of single- pass scheduling significantly, and that the parallel method cannot be generally considered as superior.
685 citations
Book•
01 Oct 1996
TL;DR: In this paper, a theoretical and application oriented analysis of deterministic scheduling problems arising in computer and manufacturing environments is presented and discussed, where different problem parameters such as task processing times, urgency weights, arrival times, deadlines, precedence constraints, and processor speed factor are involved.
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.
631 citations
TL;DR: A job shop consists of a set of different machines that perform operations on jobs, each job is composed of an ordered list of operations each of which is determined by the machine required and the processing time on it.
548 citations
TL;DR: In this paper, a multi-objective genetic algorithm was proposed for flow shop scheduling with a concave Pareto front and the performance of the algorithm was examined by applying it to the flowshop scheduling problem with two objectives: minimizing the makespan and minimizing the total tardiness.
502 citations
TL;DR: A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in the permutation flow shop is presented in this article, based on a tabu search technique with a specific neighborhood definition which employs a block of jobs.
443 citations
TL;DR: In this paper, the authors assume that the machine may not always be available in real industry settings, and they study the scheduling problem under this general situation and for the deterministic case.
Abstract: Most literature in scheduling assumes that machines are available simultaneously at all times. However, this availability may not be true in real industry settings. In this paper, we assume that the machine may not always be available. This happens often in the industry due to a machine breakdown (stochastic) or preventive maintenance (deterministic) during the scheduling period. We study the scheduling problem under this general situation and for the deterministic case.
432 citations
TL;DR: This paper applies a genetic algorithm to flowshop scheduling problems and examines two hybridizations of the genetic algorithm with other search algorithms, showing two hybrid genetic algorithms: genetic local search and genetic simulated annealing.
396 citations
TL;DR: This work presents the state of the art for multiprocessor task scheduling and shows the rationale behind the concept of multip rocessor tasks, and the standard three-field notation is extended to accommodate multi-processor tasks.
253 citations
TL;DR: It is shown that preemptive scheduling in a two-stage flow shop with at least two identical parallel machines in one of the stages so as to minimize makespan is NP-hard in the strong sense.
216 citations
22 Sep 1996
TL;DR: This paper introduces a new permutation representation for job shop scheduling and shows that a genetic algorithm using an operator which preserves the absolute order also obtains a superior solution quality.
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.
01 Jan 1996
TL;DR: In this paper, the authors present an overview of Adaptive Scheduling and Evolutionary Algorithms for job shop and local search techniques, as well as population flow in adaptive 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.
TL;DR: Techniques that aim at reducing the effective size of the search space to be explored in order to find a satisfactory solution by judiciously selecting the order in which variables are instantiated and the sequence in which possible values are tried for each variable are discussed.
03 Jun 1996
TL;DR: This work proposes several new lower bounding procedures for this problem, and shows how to incorporate them into a branch-and-bound procedure, and obtains the best known lower bounds on each.
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.
08 Jul 1996
TL;DR: This work provides improved performance guarantees for several of the most basic scheduling models, and gives the first constant performance guarantee for a number of more realistically constrained scheduling problems.
Abstract: We consider the problem of finding near-optimal solutions for a variety of NP-hard scheduling problems for which the objective is to minimize the total weighted completion time. Recent work has led to the development of several techniques that yield constant worst-case bounds in a number of settings. We continue this line of research by providing improved performance guarantees for several of the most basic scheduling models, and by giving the first constant performance guarantee for a number of more realistically constrained scheduling problems. For example, we give an improved performance guarantee for minimizing the total weighted completion time subject to release dates on a single machine, and subject to release dates and/or precedence constraints on identical parallel machines. We also give improved bounds on the power of preemption in scheduling jobs with release dates on parallel machines.
03 Jun 1996
TL;DR: Quite simple polynomialtime approximation algorithms that are based on linear programming formulations with completion time variables and give the best known performance guarantees for minimizing the total weighted completion time in several scheduling environments are presented.
Abstract: There has been recent success in using polyhedral formulations of scheduling problems not only to obtain good lower bounds in practice but also to develop provably good approximation algorithms. Most of these formulations rely on binary decision variables that are a kind of assignment variables. We present quite simple polynomialtime approximation algorithms that are based on linear programming formulations with completion time variables and give the best known performance guarantees for minimizing the total weighted completion time in several scheduling environments. This amplifies the importance of (appropriate) polyhedral formulations in the design of approximation algorithms with good worst-case performance guarantees.
03 Jun 1996
TL;DR: It is proved that the algorithms proposed have performance bound 2 and (√5 + 1)/2, respectively, and it is shown that for both problems there cannot exist an on-line algorithm with a better performance guarantee.
Abstract: We consider single-machine on-line scheduling problems where jobs arrive over time. A set of independent jobs has to be scheduled on the machine, where preemption is not allowed and the number of jobs is unknown in advance. Each job becomes available at its release date, which is not known in advance, and its characteristics, e.g., processing requirement, become known at its arrival. We deal with two problems: minimizing total completion time and minimizing the maximum time by which all jobs have been delivered. For both problems we propose and analyze an on-line algorithm based on the following idea: As soon as the machine becomes available for processing, choose an available job with highest priority, and schedule it if its processing requirement is not too large. Otherwise, postpone the start of this job for a while. We prove that our algorithms have performance bound 2 and (√5 + 1)/2, respectively, and we show that for both problems there cannot exist an on-line algorithm with a better performance guarantee.
TL;DR: An algorithm based on constraint satisfaction techniques to handle the multiple capacitated job shop scheduling problem effectively is presented and it is shown that the algorithm performs well for both sets of instances.
TL;DR: In this article, the authors studied the problem of minimizing total completion time in a two-machine flow shop and proposed a branch and bound method to minimize the total time in the flow shop.
Book•
18 Mar 1996
TL;DR: This paper presents a meta-modelling framework for adaptive scheduling that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and scheduling populations.
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.
TL;DR: The computational experience of heuristic provides several observations of the application of GA, and strongly supports that the applications of GA are problem specific and also shows that GA can be good techniques for scheduling problems.
IBM1
TL;DR: The main steelmaking processes are described and it is shown how scheduling affects the effectiveness of plant operations, and several different approaches for computerized scheduling solutions are described.
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.
TL;DR: Two Genetic Algorithms (GA) based approaches are proposed to solve the two-stage bicriteria flow shop scheduling problem with the objective of minimizing the total flow time subject to obtaining the optimal makespan.
TL;DR: In this paper, a branch and bound algorithm is presented for a very general scheduling problem withn jobs andm machines, where each job consists of a set of operations and each operation has to be processed on a dedicated machine.
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.
01 May 1996
TL;DR: In this article, an enhanced algorithm for hydro-thermal power system generation scheduling is presented by applying tabu search, a decomposition method and taking into account the nonlinearity of time-varying starting costs, an effective algorithm is proposed to increase the computing accuracy of the dynamic scheduling process.
Abstract: In this paper, an enhanced algorithm for hydro-thermal power system generation scheduling is presented. By applying the tabu search, a decomposition method and taking into account the nonlinearity of time-varying starting costs, an effective algorithm is proposed to increase the computing accuracy of the dynamic scheduling process. The proposed approach may start from any traditional scheduling method proposed earlier, and improve the sub-optimal solution by a neighborhood search method. In this regard, additional constraints can be added to the problem formulation which provides a fast scheduling or rescheduling solution to large scale hydro-thermal power systems. The results show that the proposed algorithm is both efficient and reliable.
TL;DR: This paper investigates the problem of scheduling a set of TVF-based tasks under the criterion of maximizing the sum of tasks' contributions, and proposes an algorithm which yields sub-optimal scheduling and finds out the optimal decomposition.
Abstract: Some Real-Time systems may need a multivalence description of tasks. A generic way to achieve it consists in characterizing each task by a Time Value Function (TVF), which gives the contribution of the related task at its actual completion time. This approach can be viewed as a complementary paradigm to the bivalent deadline-driven paradigm, especially in the case of overload. In this paper, we investigate the problem of scheduling a set of TVF-based tasks under the criterion of maximizing the sum of tasks' contributions. To deal with this NP-hard problem, we use a decomposition approach. The latter consists in partitionning the initial task set into several subsets for which we can establish a scheduling order (on the subset level). We propose an algorithm which yields sub-optimal scheduling and finds out the optimal decomposition. This O(n
3
) on-line scheduling algorithm yields sequences which are equal or statistically very close to the optimum, as suggested by our simulation study for various scenarii.
TL;DR: A new neural network approach is proposed to solve the single machine mean tardiness scheduling problem and the minimum makespan job shop scheduling problem that combines the characteristics of neural networks and algorithmic approaches.
26 Aug 1996
TL;DR: The proposed scheduling technique is proven to produce the optimal scheduling result if the topology of the input task graph is linear, and compared with an existing technique called the general dynamic level (GDL) scheduling with various classes of randomly generated input graphs, resulting in about 20% performance improvement.
Abstract: This paper presents a static scheduling heuristic called bestimaginary-level (BIL) scheduling for heterogeneous processors. The input graph is an acyclic precedence graph, where a node has different execution times on different processors. The static level of a node, or BIL, incorporates the effect of interprocessor communication (IPC) overhead and processor heterogeneity. The proposed scheduling technique is proven to produce the optimal scheduling result if the topology of the input task graph is linear. The performance of the BIL scheduling is compared with an existing technique called the general dynamic level (GDL) scheduling with various classes of randomly generated input graphs, resulting in about 20% performance improvement.
TL;DR: In this paper, the authors discuss job shop scheduling from the viewpoint of dealing with fuzziness inherent in the problem and present a branch-and-bound algorithm for solving static and dynamic problems with fuzzy information.
TL;DR: This paper proposes several heuristics, including taboo search (TS) and simulated annealing (SA) methods, for this generalized flowshop scheduling problem with the objective of minimizing mean tardiness.