Solving the job shop scheduling problem with tabu search

Barnes, J. Wesley; Chambers, John

doi:10.1080/07408179508936739

Cited by 112 publications

(52 citation statements)

References 8 publications

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“…(ii) Barnes and Chambers [17] argued that a JSSP can be converted into FJSSP by incorporating multiple instances of a single machine where a bottleneck is encountered during the scheduling process. This concept is sometimes called parallel machine FJSSP.…”

Section: Classification Of Machine Layoutsmentioning

confidence: 99%

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Amjad

Butt

Kousar

et al. 2018

Mathematical Problems in Engineering

113

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Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions.

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Section: Classification Of Machine Layoutsmentioning

confidence: 99%

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Amjad

Butt

Kousar

et al. 2018

Mathematical Problems in Engineering

113

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“…A number of heuristic search methods have also been proposed, for example, Adams et al (1988) and Applegate and Cook (1991). A variety of metaheuristic approaches have also been applied to the JSSP, such as Neural Networks (Sabuncuoglu and Gurgun, 1996), Beam Search (Sabuncuoglu and Bayiz, 1999), Simulated Annealing (Steinhofel et al 1999), Tabu Search (Barnes and Chambers, 1995;Nowicki and Smutnicki, 1996;Pezzella and Merelli, 2000;Zhang et al 2008), Genetic Algorithms (Falkenauer and Bouffoix, 1991;Storer et al, 1995;Aarts et al, 1994;Bean, 1994;Croce et al, 1995), Evolutionary Algorithms (Mesghouni and Hammadi, 2004), Variable Neighborhood Search (Sevkli and Aydin, 2007), Global Equilibrium Search technique (Pardalos and Shylo, 2006). Jain and Meeran (1999) provide a good survey of techniques used for the JSSP.…”

Section: Literature Reviewmentioning

confidence: 99%

A NeuroGenetic Approach for Multiprocessor Scheduling

Agarwal¹

2007

Multiprocessor Scheduling, Theory and Applications

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This chapter presents a NeuroGenetic approach for solving a family of multiprocessor scheduling problems. We address primarily the Job-Shop scheduling problem, one of the hardest of the various scheduling problems. We propose a new approach, the NeuroGenetic approach, which is a hybrid metaheuristic that combines augmented-neural-networks (AugNN) and genetic algorithms-based search methods. The AugNN approach is a nondeterministic iterative local-search method which combines the benefits of a heuristic search and iterative neural-network search. Genetic algorithms based search is particularly good at global search. An interleaved approach between AugNN and GA combines the advantages of local search and global search, thus providing improved solutions compared to AugNN or GA search alone. We discuss the encoding and decoding schemes for switching between GA and AugNN approaches to allow interleaving. The purpose of this study is to empirically test the extent of improvement obtained by using the interleaved hybrid approach instead of applied using a single approach on the job-shop scheduling problem. We also describe the AugNN formulation and a Genetic Algorithm approach for the JobShop problem. We present the results of AugNN, GA and the NeuroGentic approach on some benchmark job-shop scheduling problems. IntroductionMultiprocessor scheduling problems occur whenever manufacturing or computing operations are to be scheduled on multiple machines, processors or resources. A variety of such scheduling problems are discussed in the literature. The most general scheduling problem is the resource-constrained project-scheduling problem; this problem has received a lot of attention in the literature Herroelen et al. (1998), Kolisch (1996). The open-shop, flow-shop, job-shop and task scheduling problems can be considered special cases of the resource-constrained project-scheduling problem. While smaller instances of the various types of scheduling problems can be solved to optimality in reasonable computing time using exact solution methods such as branch and bound, most real-world problems are unsolvable in reasonable time using exact methods due to the combinatorial explosion of the feasible solution space. For this reason, heuristics and metaheuristics are frequently employed to obtain satisfactory solutions to these problems in reasonable time. In this paper, we propose a new hybrid metaheuristic approach called the NeuroGenetic approach for solving one family of multiprocessor scheduling problems -the job-shop scheduling problem. The NeuroGenetic approach is a hybrid of the Augmented Neural Networks (AugNN) approach and the Genetic Algorithms (GA) approach. The AugNN approach provides a mechanism for local search, while the GA approach provides a mechanism for global search. An interleaving of the two approaches helps guide the search to better solutions. In this chapter, we focus on the job-shop scheduling problem (JSSP). In JSSP, there are n jobs, each having m operations and each operation requires a diffe...

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“…Bu yaklaşımı literatürde ilk kullanan Brandimarte [4] olmuştur. Brandimarte [4], Paulli [5], Barnes ve Chambers [6] hiyerarşik olarak EATÇ problemlerini çözmek için önce makine atamalarını gerçekleştirmiş sonrada problemi ATÇ problemi haline dönüştürerek tabu arama (TA) yöntemini kullanmışlardır. Saidi-Mehrabad ve Fattahi [7], EATÇ problemine hiyerarşik yaklaşımlı TA algoritması geliştirmişlerdir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Esnek atölye tipi çizelgeleme problemlerinin meta sezgisel yöntemler ile çözümüne yönelik bir inceleme

Kaya¹,

Fığlalı²

2016

SAUFenBilDer

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ÖZGerçek yaşam uygulamaları arasında yer alan esnek atölye tipi çizelgeleme (EATÇ) problemleri, klasik atölye tipi çizelgeleme (ATÇ) probleminin genişletilmiş halidir. ATÇ problemlerinde paralel makineler yoktur. Atölyede işlerin paralel makinelerde işlenmesi söz konusu olduğunda EATÇ problemi ortaya çıkar. EATÇ problemlerinde işlerin rotaları birbirinden farklıdır ve her iş en az bir operasyondan oluşmaktadır. Bu operasyonlar, birbirine paralel makine setlerinden herhangi birinde işlem görürler. Literatürde ATÇ problemlerine göre EATÇ problemleri üzerine yapılan çalışmalar oldukça sınırlı kalmıştır. Bu araştırmada, EATÇ problemleri üzerine son yıllarda yapılmış ve literatürde yer alan çalışmalar incelenmiştir. Bu problemlerin meta sezgisel yöntemler ile çözümü üzerine elde edilen bulgular ve öneriler sunulmuştur.Anahtar kelimeler: esnek atölye tipi çizelgeleme, makine atama, operasyon sıralama, hiyerarşik yaklaşım, bütünleşik yaklaşım A review on meta-heuristic approaches to solving flexible job shop scheduling problem ABSTRACT Flexible job shop scheduling (FJSS) problems which are parts of real life applications are the extended case of classical job shop scheduling (JSS) problems. There are no parallel machines at JSS problems. FJSS Problems arise when works need to be done on parallel machines in the workshop. In FJSS problems, jobs have different routes and each job is consisted of at least one operation. These operations are processed by any of the machine sets which are parallel to each other. In literature; the number of studies on FJSS problems are more limited than the one on JSS problems. In this paper, recent studies in the literature on the topic of FJSS problems are reviewed. The findings and recommendations on the solution of this kind of problems by using meta-heuristic methods are presented.

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Solving the job shop scheduling problem with tabu search

Cited by 112 publications

References 8 publications

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

A NeuroGenetic Approach for Multiprocessor Scheduling

Esnek atölye tipi çizelgeleme problemlerinin meta sezgisel yöntemler ile çözümüne yönelik bir inceleme

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