Advances in Hybrid EDA for Manufacturing Scheduling with Uncertainty: Part I

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 problems. Because real world manufacturing problems often contain nonlinearities, multiple objectives conflicting each other and uncertainties that are too complex to be modelled analytically. In these environments, a hybrid metaheuristic-based optimization such as genetic algorithm (GA) is a powerful tool to find optimal system settings for a complicated stochastic manufacturing scheduling problem. In this paper, we briefly introduce hybrid genetic algorithm (HGA) for estimation of distribution algorithm (EDA) and a basic frame work on EDA to expand an EDA to Markov network-based EDA. Secondly, we summarized stochastic jobshop scheduling problem (S-JSP) and demonstrated an empirical validation by experiments for various benchmark S-JSP models by the effective EDA proposed.