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Accelerated Bendersa?? Decomposition for Integrated Forward/Reverse Logistics Network Design under Uncertainty

机译：加速本德尔萨？不确定条件下的组合式正向/反向物流网络设计分解

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摘要

In this paper, a two-stage stochastic programming modelling is proposed, to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involved both strategic and tactical decision levels. The first stage dealt with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. In the second stage, tactical decisions, such as base stock level as an inventory policy, were determined. The generic introduced model consisted of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of the proposed model is its applicability to various industries. The problem was formulated as a mixed-integer linear programming model and was solved by using Bendersa?? Decomposition (BD) approach. In order to accelerate the Bendersa?? decomposition, a number of valid inequalities were added to the master problem. The proposed accelerated BD was evaluated through small-, medium-, and large-sized test problems. Numerical results confirmed that the proposed solution algorithm improved the convergence of BD lower bound and the upper bound, enabling to reach an acceptable optimality gap in a convenient time.
机译：本文提出了一种两阶段随机规划模型，设计了不确定条件下的多周期，多阶段，单商品的集成正向/逆向物流网络设计问题。问题涉及战略和战术决策层面。第一阶段处理战略决策，即前进和后退设施的数量，容量和位置。在第二阶段，确定战术决策，例如将基本库存水平作为库存策略。引入的通用模型由前向物流和收集中心的供应商，制造商和分销中心，逆向物流的再制造，再分配和处置中心组成。提出的模型的优势在于它对各种行业的适用性。该问题被公式化为混合整数线性规划模型，并通过使用Bendersa ??解决。分解（BD）方法。为了加速本德尔萨？分解，许多有效的不等式被添加到主问题。拟议的加速BD通过小，中和大型测试问题进行了评估。数值结果表明，所提出的求解算法提高了BD下界和上界的收敛性，能够在方便的时间内达到可接受的最优间隙。

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