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Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

Alexandros Xanthopoulos, Dimitrios E. Koulouriotis
Copyright: © 2011 |Pages: 20
ISBN13: 9781615206339|ISBN10: 1615206337|EISBN13: 9781615206346
DOI: 10.4018/978-1-61520-633-9.ch010
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MLA

Xanthopoulos, Alexandros, and Dimitrios E. Koulouriotis. "Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm." Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods, edited by Ioannis Minis, et al., IGI Global, 2011, pp. 212-231. https://doi.org/10.4018/978-1-61520-633-9.ch010

APA

Xanthopoulos, A. & Koulouriotis, D. E. (2011). Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm. In I. Minis, V. Zeimpekis, G. Dounias, & N. Ampazis (Eds.), Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods (pp. 212-231). IGI Global. https://doi.org/10.4018/978-1-61520-633-9.ch010

Chicago

Xanthopoulos, Alexandros, and Dimitrios E. Koulouriotis. "Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm." In Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods, edited by Ioannis Minis, et al., 212-231. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-633-9.ch010

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Abstract

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

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