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首页> 外文期刊>Applied Mathematical Modelling >Location of cross-docking centers and vehicle routing scheduling under uncertainty: A fuzzy possibilistic-stochastic programming model
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Location of cross-docking centers and vehicle routing scheduling under uncertainty: A fuzzy possibilistic-stochastic programming model

机译：不确定性下的对接中心位置和车辆路径调度：模糊的可能随机规划模型

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摘要

The location of multiple cross-docking centers (CDCs) and vehicle routing scheduling are two crucial choices to be made in strategic/tactical and operational decision levels for logistics companies. The choices lead to more realistic problem under uncertainty by covering the decision levels in cross-docking distribution networks. This paper introduces two novel deterministic mixed-integer linear programming (MILP) models that are integrated for the location of CDCs and the scheduling of vehicle routing problem with multiple CDCs. Moreover, this paper proposes a hybrid fuzzy possibilistic-stochastic programming solution approach in attempting to incorporate two kinds of uncertainties into mathematical programming models. The proposed solving approach can explicitly tackle uncertainties and complexities by transforming the mathematical model with uncertain information into a deterministic model, m' imprecise constraints are converted into 2Rm' precise inclusive constraints that agree with Rα-cut levels, along with the concept of feasibility degree in the objective functions based on expected interval and expected value of fuzzy numbers. Finally, several test problems are generated to appraise the applicability and suitability of the proposed new two-phase MILP model that is solved by the developed hybrid solution approach involving a variety of uncertainties and complexities.
机译：在物流公司的战略/战术和运营决策层面，多个交叉配送中心（CDC）的位置和车辆路线安排是两个至关重要的选择。通过覆盖跨坞配送网络中的决策级别，这些选择会导致不确定性下更现实的问题。本文介绍了两个新颖的确定性混合整数线性规划（MILP）模型，这些模型已集成到CDC的位置以及具有多个CDC的车辆路径问题的调度中。此外，在试图将两种不确定性纳入数学规划模型的过程中，提出了一种混合的模糊可能性-随机规划求解方法。所提出的求解方法可以通过将具有不确定信息的数学模型转换为确定性模型来明确解决不确定性和复杂性，将m'不精确约束转换为与Rα割水平相符的2Rm'精确包容性约束，以及可行程度的概念在目标函数中基于模糊数的期望区间和期望值。最后，产生了几个测试问题，以评估所提出的新的两阶段MILP模型的适用性和适用性，该模型由已开发的包含各种不确定性和复杂性的混合解决方案方法解决。

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