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A hybrid algorithm for stochastic single-source capacitated facility location problem with service level requirements

机译:具有服务水平要求的随机单源受限设施定位问题的混合算法

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Facility location models are observed in many diverse areas such as communication networks, transportation, and distribution systems planning. They play significant role in supply chain and operations management and are one of the main well-known topics in strategic agenda of contemporary manufacturing and service companies accompanied by long-lasting effects. We define a new approach for solving stochastic single source capacitated facility location problem (SSSCFLP). Customers with stochastic demand are assigned to set of capacitated facilities that are selected to serve them. It is demonstrated that problem can be transformed to deterministic Single Source Capacitated Facility Location Problem (SSCFLP) for Poisson demand distribution. A hybrid algorithm which combines Lagrangian heuristic with adjusted mixture of Ant colony and Genetic optimization is proposed to find lower and upper bounds for this problem. Computational results of various instances with distinct properties indicate that proposed solving approach is efficient.
机译:在许多不同的领域都可以看到设施的位置模型,例如通信网络,运输和配电系统规划。它们在供应链和运营管理中发挥着重要作用,并且是当代制造和服务公司战略议程中的主要知名主题之一,并具有长期影响。我们定义了一种新的方法来解决随机的单源限制的设施位置问题(SSSCFLP)。具有随机需求的客户将被分配到一组能力有限的设施中,这些设施将为他们服务。实践证明,该问题可以转化为确定性的单源容量设施定位问题(SSCFLP),用于泊松需求分配。提出了一种结合拉格朗日启发式算法和蚁群调整后混合算法以及遗传优化算法的混合算法,以找到该问题的上下限。具有不同属性的各种实例的计算结果表明,提出的求解方法是有效的。

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