Publication Type : Journal Article
Publisher : International Journal of Advanced Manufacturing Technology
Source : International Journal of Advanced Manufacturing Technology, Volume 27, Number 7-8, p.804-815 (2006)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-29344433807&partnerID=40&md5=521250cabe8ae05e6c31e541db13a9c2
Keywords : Flow shop scheduling, Genetic algorithms, Makespan, Multi-objective genetic algorithms, Problem solving, Scheduling, Total flow time
Campus : Coimbatore
School : School of Engineering
Year : 2006
Abstract : In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.
Cite this Research Publication : Ta Pasupathy, Rajendran, Ca, and Suresh, R. Kb, “A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs”, International Journal of Advanced Manufacturing Technology, vol. 27, pp. 804-815, 2006.