Advances in Hybrid Metaheuristics for Stochastic Manufacturing Scheduling: Part I Models and Methods
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 scheduling problems since most of t
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Advances in Hybrid Metaheuristics for Stochastic Manufacturing Scheduling: Part I Models and Methods Mitsuo Gen, Xinchang Hao and Wenqiang Zhang
Abstract 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 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 scenarios, hybrid metaheuristics based optimization is a powerful tool to determine optimal system settings to the stochastic manufacturing scheduling problems. Evolutionary algorithm (EA) in hybrid metaheuristics is a generic population-based metaheuristic optimization algorithm, which can find compromised optimal solutions well for a complicated scheduling problem. This paper surveys recent hybrid metaheuristics such as hybrid sampling strategy-based EA(HSS-EA) which combines vector evaluated genetic algorithm (VEGA) and a new archive maintenance strategy to preserve both the convergence rate and the distribution performance, and multi-objective estimation of distribution algorithm (MoEDA) which builds and samples explicit probabilistic model for the distribution of promising candidate solutions found so far and use the constructed model to guide further search behavior.
M. Gen (B) Fuzzy Logic Systems Institute, Tokyo University of Science, Tokyo, Japan e-mail: [email protected] X. Hao Graduate School of Information, Production and Systems, Waseda University, Tokyo, Japan W. Zhang College of Information Science and Engineering, Henan University of Technology, Henan, People’s Republic of China © Springer Science+Business Media Singapore 2017 J. Xu et al. (eds.), Proceedings of the Tenth International Conference on Management Science and Engineering Management, Advances in Intelligent Systems and Computing 502, DOI 10.1007/978-981-10-1837-4_88
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Keywords Multi-objective optimization problem (MOP) · Multi-objective evolutionary algorithm (MoEA) · Stochastic MOP (S-MOP) · Estimation of distribution algorithm (EDA) · Hybrid sampling strategy-based EA (HSS-EA)
88.1 Introduction In real world manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously. Manufacturing scheduling is one of the important and complex COP models, where it can have a major impact on the productivity of a production process. Moreover, the COP models make the problem intractable to the traditional optimization techniques because most of scheduling problems fall into the class of NP-hard combinatorial problems [20, 21]. In order to develop effective and efficient solution algorithms that are in a sense good, i.e., whose computation
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