research-article

Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop

Authors: Adil Baykasoğlu, Mustafa Göçken, Lale ÖzbakirAuthors Info & Claims

SIMULATION, Volume 86, Issue 12

Pages 715 - 728

https://doi.org/10.1177/0037549709346561

Published: 01 December 2010 Publication History

Abstract

In this paper, a genetic programming based data mining approach is proposed to select dispatching rules which will result in competitive shop performance under a given set of shop parameters (e.g. interarrival times, pre-shop pool length). The main purpose is to select the most appropriate conventional dispatching rule set according to the current shop parameters. In order to achieve this, full factorial experiments are carried out to determine the effect of input parameters on predetermined performance measures. Afterwards, a genetic programming based data mining tool that is known as MEPAR-miner (multi-expression programming for classification rule mining) is employed to extract knowledge on the selection of best possible conventional dispatching rule set according to the current shop status. The obtained results have shown that the selected dispatching rules are appropriate ones according to the current shop parameters. All of the results are illustrated via numerical examples and experiments on simulated data.

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Baykasoğlu, A., M. Göçken and L. Özbakır. 2007 . A Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop. 8th Workshop of the EURO Working Group ‘EU/ME, the European Chapter on Metaheuristics’, Stuttgart, Germany.

Cited By

Zhang FMei YNguyen STan KZhang M(2023)Task Relatedness-Based Multitask Genetic Programming for Dynamic Flexible Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319978327:6(1705-1719)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3199783
Zhang FMei YNguyen STan KZhang M(2023)Instance-Rotation-Based Surrogate in Genetic Programming With Brood Recombination for Dynamic Job-Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.318069327:5(1192-1206)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3180693
Zhang HRoy U(2019)A semantics-based dispatching rule selection approach for job shop schedulingJournal of Intelligent Manufacturing10.1007/s10845-018-1421-z30:7(2759-2779)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10845-018-1421-z
Show More Cited By

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Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop

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Information & Contributors

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Published In

cover image Simulation

Simulation Volume 86, Issue 12

Dec 2010

61 pages

ISSN:0037-5497

Issue’s Table of Contents

© 2010 The Society for Modeling and Simulation International.

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Society for Computer Simulation International

San Diego, CA, United States

Publication History

Published: 01 December 2010

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Cited By

Zhang FMei YNguyen STan KZhang M(2023)Task Relatedness-Based Multitask Genetic Programming for Dynamic Flexible Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319978327:6(1705-1719)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3199783
Zhang FMei YNguyen STan KZhang M(2023)Instance-Rotation-Based Surrogate in Genetic Programming With Brood Recombination for Dynamic Job-Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.318069327:5(1192-1206)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TEVC.2022.3180693
Zhang HRoy U(2019)A semantics-based dispatching rule selection approach for job shop schedulingJournal of Intelligent Manufacturing10.1007/s10845-018-1421-z30:7(2759-2779)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s10845-018-1421-z
Baykasoğlu AMaral S(2014)Fuzzy functions via genetic programmingJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.5555/2684939.268495827:5(2355-2364)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.5555/2684939.2684958

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