Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10332826 | Journal of Computational Science | 2014 | 7 Pages |
Abstract
This paper proposes two parallel algorithms which are improved by heuristics for a bi-objective flowshop scheduling problem with sequence-dependent setup times in a just-in-time environment. In the proposed algorithms, the population will be decomposed into the several sub-populations in parallel. Multiple objectives are combined with min-max method then each sub-population evolves separately in order to obtain a good approximation of the Pareto-front. After unifying the obtained results, we propose a variable neighborhood algorithm and a hybrid variable neighborhood search/tabu search algorithm to improve the Pareto-front. The non-dominated sets obtained from our proposed algorithms, a genetic local search and restarted iterated Pareto greedy algorithm are compared. It is found that most of the solutions in the net non-dominated front are yielded by our proposed algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
J. Behnamian,