点击次数：

论文类型：会议论文

发表时间：2015-10-28

收录刊物：EI、Scopus

摘要：In real manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues with multiple objectives. However it is very difficult for solving the COP problems intractable by the traditional approaches because of NP-hard problems. For developing effective and efficient algorithms that are in a sense "good," i.e., whose computational time is small as within 3 minutes, we have to consider three issues: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP). In this paper, we focus on recent hybrid metaheuristics to solve a variety of multiobjective scheduling problems in manufacturing systems: hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MoEA) and hybrid multiobjective genetic algorithm and particle swarm optimization with Cauchy distribution (HMoGA.PSO+CD). We also demonstrate their applications to AGV dispatching model by random key-based hybrid evolutionary algorithm, assembly line balancing model with worker capability by HSS-MoEA and two-stage re-entrant flexible flowshop scheduling with blocking by Hybrid PSO with FLC.