Title: Genetic algorithm-based hybrid approach for optimal instance selection of minimising makespan in permutation flowshop scheduling

Authors: R. Balasundaram; S. Sathiya Devi

Addresses: Department of Mechanical Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, India ' University College of Engineering, BIT Campus, Trichirappalli, Tamilnadu, India

Abstract: Recently, the instance selection is getting more attention for the researchers to achieve enhanced performance of algorithms. A typical flowshop dataset can be represented in the form of a number of instances. The instances that are recorded during production process may not be a good example to learn useful knowledge. Therefore, the selection of high quality instances can be considered as a search problem and be solved by evolutionary algorithms. In this work, a genetic algorithm (GA) is proposed to select a sub-set of best instances. The selected instances are represented in the form of IF-Then else rules using a decision tree (DT) algorithm. The seed solution from DT is used as input to a scatter search (SS) algorithm for a few iterations, which acts as a local search to find the best value of the selected instances. The GA is used to select best instances in order to have a smaller tree size with good solution accuracy for minimizing makespan criterion in permutation flowshop scheduling. The computational experiments are performed with standard problems and compared against various existing literatures.

Keywords: instance selection; genetic algorithm; decision tree algorithm; scatter search algorithm; makespan.

DOI: 10.1504/IJBISE.2019.098864

International Journal of Business Intelligence and Systems Engineering, 2019 Vol.1 No.3, pp.197 - 225

Received: 06 Jun 2017
Accepted: 16 Nov 2017
Published online: 09 Apr 2019 *

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