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

Authors:
C. Oguz;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:
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Year:
2003

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Abstract

Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Since most scheduling problems are NPhard, 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.