Performance Comparison of Three Hybridization Categories to Solve Multi-Objective Flow Shop Scheduling Problem

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.