Please use this identifier to cite or link to this item:
Mirabi, Mohammad
Ghomi, S.M.T. Fatemi
Jolai, F.
Year of Publication: 
[Journal:] Journal of Industrial Engineering International [ISSN:] 2251-712X [Volume:] 10 [Year:] 2014 [Pages:] 1-9
Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution.
Hybrid genetic algorithm
Permutation flow-shop
Sequence dependent
Persistent Identifier of the first edition: 
Creative Commons License:
Document Type: 
Social Media Mentions:


Files in This Item:
279.95 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.