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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem
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A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem

机译：m机无等待作业车间调度问题的交叉熵遗传算法

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No-wait job-shop scheduling (NWJSS) problem is one of the classical scheduling problems that exist on many kinds of industry with no-wait constraint, such as metal working, plastic, chemical, and food industries. Several methods have been proposed to solve this problem, both exact (i.e. integer programming) and metaheuristic methods. Cross entropy (CE), as a new metaheuristic, can be an alternative method to solve NWJSS problem. This method has been used in combinatorial optimization, as well as multi-external optimization and rare-event simulation. On these problems, CE implementation results an optimal value with less computational time in average. However, using original CE to solve large scale NWJSS requires high computational time. Considering this shortcoming, this paper proposed a hybrid of cross entropy with genetic algorithm (GA), called CEGA, on m-machines NWJSS. The results are compared with other metaheuritics: Genetic Algorithm-Simulated Annealing (GASA) and hybrid tabu search. The results showed that CEGA providing better or at least equal makespans in comparison with the other two methods.
机译：无等待作业车间调度（NWJSS）问题是经典的调度问题之一，存在于许多没有等待约束的行业中，例如金属加工，塑料，化工和食品工业。已经提出了几种解决该问题的方法，包括精确的（即整数编程）方法和元启发式方法。交叉熵（CE）作为一种新的元启发式方法，可以作为解决NWJSS问题的替代方法。该方法已用于组合优化，多外部优化和罕见事件仿真。针对这些问题，CE实施可产生平均时间较少的最优值。但是，使用原始CE解决大规模NWJSS需要大量的计算时间。考虑到这一缺点，本文提出了在m机NWJSS上将交叉熵与遗传算法（GA）混合的方法，称为CEGA。将结果与其他元启发法进行比较：遗传算法模拟退火（GASA）和混合禁忌搜索。结果表明，与其他两种方法相比，CEGA可以提供更好或至少相等的制造期限。

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