پیشرفت های اخیر در الگوریتم های تکاملی ترکیبی برای برنامه ریزی تولید چند منظوره

In this paper, we focus on recent hybrid evolutionary algorithms (HEA) to solve a variety of single or multiobjective scheduling problems in manufacturing systems to get a best solution with a smaller computational time. Firstly we summarize multiobjective hybrid genetic algorithm (Mo-HGA) and hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and then propose HSS-MoEA combining with differential evolution (HSS-MoEA-DE). We also demonstrate those hybrid evolutionary algorithms to bicriteria automatic guided vehicle (B-AGV) dispatching problem, robot-based assembly line balancing problem (R-ALB), bicriteria flowshop scheduling problem (B-FSP), multiobjective scheduling problem in thin-film transistor-liquid crystal display (TFT-LCD) module assembly and bicriteria process planning and scheduling (B-PPS) problem. Also we demonstrate their effectiveness of the proposed hybrid evolutionary algorithms by several empirical examples.