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Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

Ömer Faruk Yılmaz, Mehmet Bülent Durmuşoğlu
Copyright: © 2018 |Pages: 26
ISBN13: 9781522529446|ISBN10: 1522529446|EISBN13: 9781522529453
DOI: 10.4018/978-1-5225-2944-6.ch008
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MLA

Yılmaz, Ömer Faruk, and Mehmet Bülent Durmuşoğlu. "Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System." Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, edited by Ömer Faruk Yılmaz and Süleyman Tüfekçí, IGI Global, 2018, pp. 162-187. https://doi.org/10.4018/978-1-5225-2944-6.ch008

APA

Yılmaz, Ö. F. & Durmuşoğlu, M. B. (2018). Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System. In Ö. Faruk Yılmaz & S. Tüfekçí (Eds.), Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems (pp. 162-187). IGI Global. https://doi.org/10.4018/978-1-5225-2944-6.ch008

Chicago

Yılmaz, Ömer Faruk, and Mehmet Bülent Durmuşoğlu. "Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System." In Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, edited by Ömer Faruk Yılmaz and Süleyman Tüfekçí, 162-187. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2944-6.ch008

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Abstract

Problems encountered in real manufacturing environments are complex to solve optimally, and they are expected to fulfill multiple objectives. Such problems are called multi-objective optimization problems(MOPs) involving conflicting objectives. The use of multi-objective evolutionary algorithms (MOEAs) to find solutions for these problems has increased over the last decade. It has been shown that MOEAs are well-suited to search solutions for MOPs having multiple objectives. In this chapter, in addition to comprehensive information, two different MOEAs are implemented to solve a MOP for comparison purposes. One of these algorithms is the non-dominated sorting genetic algorithm (NSGA-II), the effectiveness of which has already been demonstrated in the literature for solving complex MOPs. The other algorithm is fast Pareto genetic algorithm (FastPGA), which has population regulation operator to adapt the population size. These two algorithms are used to solve a scheduling problem in a Hybrid Manufacturing System (HMS). Computational results indicate that FastPGA outperforms NSGA-II.

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