The Application of Parallel Multipopulation Genetic Algorithms to Dynamic Job-Shop Scheduling

Qi, Jianguo; Burns, George; Harrison, David K.

doi:10.1007/s001700070052

Cited by 52 publications

(21 citation statements)

References 9 publications

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“…Since there is no unique rule which is effective for all problems [17], linear or random combinations of dispatching rules are used to obtain better results. Baykasoglu and Özbakır [18] analysed the performance of various dispatching rules for the FJSP with different machine flexibilities.…”

Section: A Brief Literature Reviewmentioning

confidence: 99%

A Taxonomy for the Flexible Job Shop Scheduling Problem

Çınar

Topçu

Oliveira

2015

Springer Proceedings in Mathematics &Amp; Statistics

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This chapter aims at developing a taxonomic framework to classify the studies on the flexible job shop scheduling problem (FJSP). The FJSP is a generalization of the classical job shop scheduling problem (JSP), which is one of the oldest NP-hard problems. Although various solution methodologies have been developed to obtain good solutions in reasonable time for FSJPs with different objective functions and constraints, no study which systematically reviews the FJSP literature has been encountered. In the proposed taxonomy, the type of study, type of problem, objective, methodology, data characteristics, and benchmarking are the main categories. In order to verify the proposed taxonomy, a variety of papers from the literature are classified. Using this classification, several inferences are drawn and gaps in the FJSP literature are specified. With the proposed taxonomy, the aim is to develop a framework for a broad view of the FJSP literature and construct a basis for future studies.

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Section: A Brief Literature Reviewmentioning

confidence: 99%

A Taxonomy for the Flexible Job Shop Scheduling Problem

Çınar

Topçu

Oliveira

2015

Springer Proceedings in Mathematics &Amp; Statistics

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“…Shaw et al (1992) and Piramuthu et al (1994) exploited inductive learning to determine the preferable scheduling policies for different shop floor states. Mesghouni et al (1999), Qi et al (2000), Rossi and Dini (2000), and Chryssolouris and Subramaniam (2001) solved the dynamic scheduling problem in FMS using genetic algorithms. Yu et al (1999) and Subramaniam et al (2000) employed fuzzy logic and Trentesaux et al (2000) used intelligent agents to select the appropriate scheduling rule.…”

Section: Literature Reviewmentioning

confidence: 99%

Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

Shnits

Sinreich

2006

Int J Flex Manuf Syst

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This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level.A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system's performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment.

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“…The dynamic JSSP incorporates uncertainty with respect to the number of jobs and the release dates associated with the jobs which are to be scheduled (Aydin andOztemel 2000 andQi et al 2000), while the stochastic JSSP focuses on incorporating uncertainty into the process time estimates (Lei and Xiong 2007;Singer 2000;and Yoshitomi and Yamaguchi 2003).…”

Section: The Problem Contextmentioning

confidence: 99%

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

Grobler

Engelbrecht²,

Kok³

et al. 2009

Ann Oper Res

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This paper investigates the application of particle swarm optimization (PSO) to the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time. To achieve this goal, alternative particle representations and problem mapping mechanisms were implemented within the PSO paradigm. This resulted in the development of four PSO-based heuristics. Benchmarking on real customer data indicated that using the priority-based representation resulted in a significant performance improvement over the existing rule-based algorithms commonly used to solve this problem. Additional investigation into algorithm scalability led to the development of a priority-based differential evolution algorithm. Apart from the academic significance of the paper, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability, and overall competitive advantage.

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The Application of Parallel Multipopulation Genetic Algorithms to Dynamic Job-Shop Scheduling

Cited by 52 publications

References 9 publications

A Taxonomy for the Flexible Job Shop Scheduling Problem

A Taxonomy for the Flexible Job Shop Scheduling Problem

Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time

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