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Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm
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Author(s): Alexandros Xanthopoulos (Democritus University of Thrace, Greece)and Dimitrios E. Koulouriotis (Democritus University of Thrace, Greece)
Copyright: 2011
Pages: 20
Source title:
Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods
Source Author(s)/Editor(s): Ioannis Minis (University of the Aegean, Greece), Vasileios Zeimpekis (University of the Aegean, Greece), Georgios Dounias (University of the Aegean, Greece)and Nicholas Ampazis (University of the Aegean, Greece)
DOI: 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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