Advances in Hybrid Metaheuristics for Stochastic Manufacturing Scheduling: Part II Case Studies

Abstract

Manufacturing Scheduling plays a very important role in the 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 manufacturing scheduling problems since most of them fall into the class of NP-hard problem. Because real world manufacturing problems often contain nonlinearities, multiple objectives conflicting each other and also uncertainties that are too complex to be modeled analytically. In these environments, hybrid metaheuristic based optimization is a powerful tool to find optimal system settings to the stochastic manufacturing scheduling problems. Evolutionary algorithm (EA) in hybrid metaheuristics is a generic population-based metaheuristic, which can find compromised optimal solutions well for a complicated manufacturing scheduling problem. By using the hybrid sampling strategy-based EA (HSS-EA) and the multi-objective estimation of distribution algorithm (MoEDA), we survey several case studies such as stochastic multi-objective jobshop scheduling problem (S-MoJSP), stochastic multi-objective assembly line balancing (S-MoALB) problem and stochastic multi-objective resource-constrained project scheduling problem (S-MoRcPSP) with numerical experimental results to get the better efficacy and efficiency than existing NSGA-II, SPEA2 and awGA algorithms.