Multipopulation multiobjective genetic algorithm for multiobjective permutation flow shop scheduling problem
FU Ya-ping
HUANG Min
WANG Hong-feng
WANG Xing-wei
Abstract：Since the permutation flow shop scheduling problem exits extensively in manufacturing enterprises, a mul-tiobjective flow shop scheduling problem with the objectives of minimizing the makespan and the total tardiness is inves-tigated in this paper. In order to solve it, a multipopulation multiobjective genetic algorithm based on decomposition is proposed. The proposed algorithm decomposes the investigated problem into multiple single objective subproblems intro-duced into the iteration course step by step. At each iteration, multiple subpopulations are constructed for the current solved subproblems based on the distribution of population, which realizes the goal of solving them simultaneously. The evolution of multiple subpopulations can be used to search the optimal solutions of multiple subproblems. Experimental results on some instances show that the proposed algorithm can get better performance in solving the multiobjective permutation flow shop scheduling problem.
Keywords：multipopulationgenetic algorithmmultiobjective optimizationflow shop scheduling
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1281-1288 )