Hybrid Multiobjective Evolutionary Algorithm with Differential Evolution for Process Planning and Scheduling Problem

In an intelligent manufacturing environment, process planning and scheduling (PPS) plays a very important role as a most complex and practical scheduling problem, which processes a set of prismatic parts into completed products by determining the optimal process plans and moments to execute each operation with competitive manufacturing resources. Many research works use multiobjective evolutionary algorithm (MOEA) to solve PPS problems with consideration of multiple complicated objectives to be optimized. This paper proposes a hybrid multiobjective evolutionary algorithm with differential evolution (HMOEA-DE) to solve the PPS problem. HMOEA-DE uses a special designed fitness function to evaluate the dominated and nondominated individuals and divides the population and elitism into two parts, which close to the center and edge areas of Pareto frontier. Moreover, differential evolution applied on elitism tries to improve the convergence and distribution performances much more by guiding the search directions through different individuals with different fitness function. Numerical comparisons indicate that the efficacy of HMOEA-DE outperforms the traditional HMOEA without DE in convergence and distribution performances.