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Comparison of performance the neighborhood operators and the ratio of search local-global in minimizing makespan using an GA-VNS collaboration to solve reactive production scheduling

机译:使用GA-VNS协作解决无功生产计划时,将邻域运营商的性能与搜索局部/全局比例最小化生成时间的比率进行比较

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Reactive production scheduling involves searching methodologies in a wide solution space. A criterion often used in the analysis of results for this type of problem is makespan. Many artificial intelligence techniques are being used in an attempt to minimize the makespan. The use of global and local search hybridization has been well referenced. Usually the each global search for a solution applies some local searches in the same solution, trying to find a better solution in the neighborhood. These local search operations have used neighborhood operators. This paper has two research objectives: investigation of the better neighborhood operator and the better ratio local/global. This proposal implements a hybrid Genetic Algorithm (GA) with Variable Neighborhood Search (VNS). The proposal was validated in three scenarios of Job Shop Scheduling (JSS) coming from Taillard base and using three neighborhood structures.
机译:响应式生产计划涉及在广泛的解决方案空间中搜索方法。对于此类问题,在结果分析中经常使用的标准是制造跨度。人们正在尝试使用许多人工智能技术来最小化制造周期。全局和本地搜索混合的使用已得到很好的引用。通常,对解决方案的每次全局搜索都会在同一解决方案中应用一些本地搜索,以尝试在附近地区找到更好的解决方案。这些本地搜索操作已使用邻域运算符。本文有两个研究目标:调查更好的邻域算子和更好的局部/全局比率。该提议实现了带有可变邻域搜索(VNS)的混合遗传算法(GA)。该提案已在来自Taillard基地的三个车间作业计划(JSS)场景中使用三个邻域结构进行了验证。

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