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

Author:

Baykasoğlu Adil1,Göçken Mustafa2,Özbakir Lale3

Affiliation:

1. Department of Industrial Engineering, University of Gaziantep, Gaziantep, Turkey,

2. Department of Industrial Engineering, University of Gaziantep, Gaziantep, Turkey

3. Department of Industrial Engineering, Erciyes University, Kayseri, Turkey

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.

Publisher

SAGE Publications

Subject

Computer Graphics and Computer-Aided Design,Modeling and Simulation,Software

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