Journal of Intelligent Manufacturing

, Volume 25, Issue 5, pp 849–866 | Cite as

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey



Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.


Manufacturing scheduling Multiobjective evolutionary algorithm (MOEA) Hybrid evolutionary algorithm (HEA) Job shop scheduling (JSP) Flexible JSP (FJSP) Advanced planning and scheduling (APS) Automatic guided vehicle (AGV) 



This work is partly supported by the Japan Society of Promotion of Science (JSPS):Grant-in-Aid for Scientific Research (C) (No. 24510219.0001), National Science Council (NSC 101-2811-E-007-004, NSC 102-2811-E-007-005), the Fundamental Research Funds (Software+X) of Dalian University of Technology (No. DUT12JR05, No. DUT12JR12), and supported by New Teacher Fund of Ministry of Education of China (No. 20120041120053).


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Fuzzy Logic Systems InstituteIizuka CityJapan
  2. 2.National Tsing Hua UniversityHsinchu CityTaiwan
  3. 3.Dalian University of TechnologyDalianChina

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