基于启发式遗传算法的流水车间调度

Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001. Pub Date : 2001-06-19 DOI:10.1109/ITI.2001.938035

U. Chakraborty, D. Lah, M. Chakraborty

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引用次数: 5

摘要

流程车间调度是指在多台机器上确定要处理的作业的最优顺序，以满足一定的调度准则。它是np完全的。启发式算法使用特定于问题的信息来产生良好的工作解决方案。遗传算法是基于自然进化概念的随机、自适应、通用搜索启发式算法。本文提出了一种新的启发式遗传算法(NGA)，它结合了遗传算法和启发式搜索的优点。在若干问题上运行了NGA算法，并将其性能与传统遗传算法和著名的NEH启发式算法进行了比较。NGA在几乎所有情况下都表现得更好。

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A heuristic genetic algorithm for flowshop scheduling

Flowshop scheduling deals with determining the optimum sequence of jobs to be processed on several machines so as to satisfy some scheduling criterion. It is NP-complete. Heuristic algorithms use problem-specific information to yield a good working solution. Genetic algorithms are stochastic, adaptive, general-purpose search heuristics based on concepts of natural evolution. We have developed a new heuristic genetic algorithm (NGA) which combines the good features of both the GA and heuristic search. The NGA is run on several problems and its performance is compared with that of the conventional genetic algorithm and the well-known NEH heuristic. The NGA is seen to perform better in almost all instances.

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Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001.

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