Parallel genetic algorithm for a flow-shop problem with multiprocessor tasks

Authors:
Ceyda Oǧuz;Yu-Fai Fung;M. Fikret Ercan;X. T. Qi
Affiliations:
Dept. of Management, The Hong Kong Polytechnic University, Hong Kong SAR;Dept. of Electrical Eng., The Hong Kong Polytechnic University, Hong Kong SAR;School of Electrical and Electronic Eng., Singapore Polytechnic, Singapore;Dept. of Management, The Hong Kong Polytechnic University, Hong Kong SAR
Venue:
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Year:
2003

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Abstract

Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Hence, most scheduling problems are NP-hard and it is impossible to find the optimal schedule in reasonable time. In this paper, we consider a flow-shop scheduling problem with multiprocessor tasks. A parallel genetic algorithm using multithreaded programming technique is developed to obtain a quick but good solution to the problem. The performance of the parallel genetic algorithm under various conditions and parameters are studied and presented.