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首页> 外文期刊>Computers & Industrial Engineering >A modified firefly algorithm for optimizing a multi stage supply chain network with stochastic demand and fuzzy production capacity
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A modified firefly algorithm for optimizing a multi stage supply chain network with stochastic demand and fuzzy production capacity

机译:一种改进的萤火虫算法,用于优化具有随机需求和模糊生产能力的多阶段供应链网络

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摘要

Production-distribution network (PDN) design is among complex problems with dynamic relationships that cause substantial amount of uncertainty including customers' demands, production capacity and others. This paper addresses a multi stage production distribution planning (PDP) problem with multi suppliers, producers, potential entrepots, potential retailers and inland and outland customers under uncertain environment. A mixed integer linear programming (MILP) model is presented to describe the purpose problem for optimizing the integrated total cost of the system. The proposed model considers operational risks involving uncertainties related to producers' capacity and customers' demand with applying probability distribution and fuzzy set theory. Commercial software cannot solve large sized instances in a reasonable run time. So, we presented a novel heuristic based on firefly algorithm (FA) called selective firefly algorithm (SFA) to solve the large sized problems. In the proposed SFA, each firefly identifies all fireflies with more brightness and evaluates its brightness change before moving, implicitly. Afterwards, the firefly that makes the best change is selected and initial firefly moves toward the selected firefly. Several numerical examples in both small and large sizes are applied to demonstrate the performance of the proposed heuristic.
机译:生产-分销网络(PDN)设计是具有动态关系的复杂问题之一,动态关系会导致大量不确定性,包括客户的需求,生产能力等。本文解决了不确定环境下的多个供应商,生产商,潜在转口商,潜在零售商以及内陆和外地客户的多阶段生产分配计划(PDP)问题。提出了一种混合整数线性规划(MILP)模型来描述优化系统集成总成本的目的问题。提出的模型运用概率分布和模糊集理论考虑了涉及生产者能力和客户需求不确定性的操作风险。商业软件无法在合理的运行时间内解决大型实例。因此,我们提出了一种基于萤火虫算法(FA)的新颖启发式算法,称为选择性萤火虫算法(SFA),以解决大型问题。在拟议的SFA中,每个萤火虫都可以识别出所有具有更高亮度的萤火虫,并隐式地评估其移动前的亮度变化。此后,选择做出最佳更改的萤火虫,初始萤火虫移向所选萤火虫。应用了几个大小型的数值示例,以证明所提出的启发式方法的性能。

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