Abstract
The industries must preserve a rate of constant productivity; however, weaknesses appear at the level of production system which engenders high manufacturing costs. Scheduling is considered the most significant issue in the production system, the solution to that problem need complex methods to solve it. The goal of this paper is to establish three hybridization categories of the evolutionary methods ABC and PSO to solve multi-objective flow shop scheduling problem: Synchronous parallel hybridization using the weighted sum method of the fitness function, sequential hybridization using or not using the weighted sum method of the fitness function, and asynchronous parallel hybridization using the weighted sum method of the fitness function. Then to test these methods in an automotive multi-objective flow shop and to perform an in-depth comparison for verifying how the multi hybridization and the hybridization categories influence the resolution of multi-objective flow shop scheduling problems. The results are consistent with other studies that have shown that the multi hybridization improve the effectiveness of the algorithm.
Highlights
The objectives of companies are diversified and the scheduling became multi-criterion
To perform an in-depth comparison for verifying how the multi hybridization and the hybridization categories influence the resolution of multi-objective flow shop scheduling problems
The results show that the asynchronous parallel hybridization method ABC//PSO is given the equal results to the results obtained by the sequential hybridization method ABC(F1)+PSO(F2), the asynchronous parallel hybridization method ABC//PSO is given the best results compared with other results obtained by the other sequential hybridization methods [ABC+PSO](F), PSO(F1)+ABC(F2), [PSO+ABC] (F)
Summary
The objectives of companies are diversified and the scheduling became multi-criterion. The scheduling objective are related to the time or the resources or the cost. The scheduling problem in the production system is a accomplishment of a tasks group by taking in consideration some constraints. The hybrid metaheuristics are proposed by Talbi [1] and are classified in three classification [2]: Synchronous parallel hybridization consists of incorporating an approach in an operator of another approach. Sequential hybridization is composed by various approaches, the solution of the first approach is an initialization of the approach. Asynchronous parallel hybridization, the hybrid approaches share data throughout the search process
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