期刊
SYMMETRY-BASEL
卷 13, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/sym13071131
关键词
discrete bacterial memetic evolutionary algorithm; simulated annealing; flow shop scheduling problem
资金
- National Office of Research, Development, and Innovation [NKFIH K124055]
This paper focuses on the flow shop scheduling problem and introduces a discrete bacterial memetic evolutionary algorithm which improves local search using simulated annealing. Experimental results show that this algorithm outperforms other methods in solving the no-idle flow shop scheduling problem.
This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers' work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.
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