A Hybrid Evolutionary Algorithm Framework and Its Applications to Multiobjective Scheduling Problems

Abstract

AbstractScheduling plays a very important role in intelligent manufacturing system, where it can have a major impact on the productivity of a production process. However, it is very difficult to find an optimal solution for scheduling problems since most of them fall into the class of NP-hard problem. Evolutionary algorithm (EA) is a generic population-based meta-heuristic optimization algorithm, which can find compromised optimal solutions well for complicated scheduling problem. Moreover, multiobjective evolutionary algorithm (MOEA) has attracted attention with respect to multiobjective scheduling problems because ofthe global and local search abilities. This paper designs a multiobjective hybrid evolutionary algorithm (MoHEA) framework which combines vector evaluated genetic algorithm and a new archive maintenance strategy to preserve both the convergence rate and the distribution performance. The MoHEA is applied to solve the most import practical scheduling problems such as multiobjective process planning and scheduling (MoPPS) problem and multiobjective assembly line balancing (MoALB) problem. Numerical experimental results show that the MoHEA could get the better efficacy and efficiency than existing MOEAs.KeywordsHybrid evolutionary algorithm (HEA)Process planning and scheduling (PPS)Assembly line balancing (ALB)Multiobjective optimization problem (MOP)