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An improved genetic algorithm approach for on-line optimisation problems
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DOI：10.1080/09537287.2010.543556.png)
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Abstract
En 中文
Real-time systems have to react to disruptive events in manufacturing environments within tight time constraints. This article aims to enhance standard genetic algorithms (GA) to reduce the execution time and improve the solution quality when the objective function is time-expensive to evaluate. More precisely, we propose the addition of a cache memory to avoid re-testing an already tested solution and a predator mechanism to relax evaluation. After discussing the applicability conditions of those mechanisms, we propose a demonstrative example, based on a real scenario in the aluminium industry, in which a GA and a real-time discrete event simulation model are integrated to support the rescheduling process in enterprise resource planning controlled environments.
Keywords:
genetic algorithm
rescheduling
real-time decision
predator
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