A hybrid algorithm based on PSO and ACO approach for solving combinatorial fuzzy unrelated parallel machine scheduling problem
Date
2011
Authors
Senthilkumar, K.M.
Selladural, V.
Raja, K.
Thirunavukkarasu, V.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
European Journal of Scientific Research, 2011; 64(2):293-299
Statement of Responsibility
Conference Name
Abstract
Flexible and agile manufacturing systems have led to the growing interest in scheduling problems considering both earliness and tardiness penalties. The problem studied in this work is the unrelated parallel machine earliness-tardiness non-common due date sequence-dependent set-up time scheduling problem (UPETNDDSP) for jobs with varying processing times, where the objective is to minimize the sum of the absolute deviations of job completion times from their corresponding due dates for the different weighted earliness and tardiness combinations. A hybrid approach based on Particle Swarm optimization algorithm (PSO) and Ant Colony optimization algorithms (ACO) have been devised to generate optimal solutions for different weighted earliness and tardiness measures. Fuzzy logic approach is been used to select the optimal weighted earliness-tardiness combinations in an unrelated parallel machine environment. The hybrid algorithm identifies the best sequences for the different weighted combinations of earliness and tardiness measures for each given set of jobs. Fuzzy logic is then used to select the optimal weighted combination, which satisfies the combined objective function to a larger extent. The test problems for evaluating the proposed hybrid technique are generated by following the procedure given by Funda and Giindiiz (1999) for generating benchmark parallel machine earliness tardiness scheduling problems. The performance of the combined objective function obtained by the proposed hybrid technique has been compared with the solutions yielded by the genetic algorithm techniques proposed by Funda and Giindiiz (1999) known as Genetic Algorithm with partially mapped crossover operator (GA-PMX), Genetic Algorithm with multi component uniform order based crossover generator (GA-MCUOX) and with the GA-Fuzzy algorithm as suggested by Raja et al (2008). The comparison shows that the proposed hybrid technique outperforms all the GA-PMX, GA-MCUOX and GA-Fuzzy techniques.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2011 EuroJournals Publishing