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首页> 外文期刊>International journal of management science and engineering management >A priority-based modified encoding-decoding procedure for the design of a bi-objective SC network using meta-heuristic algorithms
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A priority-based modified encoding-decoding procedure for the design of a bi-objective SC network using meta-heuristic algorithms

机译：基于元启发式算法设计双目标SC网络的基于优先级的改进编解码程序

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摘要

This paper aims at a bi-objective optimization of the multi-periodic multi-product supply chain network design (SCND) problem under uncertainty consisting of manufacturing centers, distribution centers, and customer nodes. The problem is considered as a two-stage transportation model while there are several candidate transportation options for each plant. The supposed uncertainty is related to the services of the warehouses and affects the decision making of the SCND that are critical issues. So, the goal is to make the best decisions to maximize the minimum average percentages of products dispatched for each customer in each period while minimizing the sum of strategic, tactical and operational costs. Since the proposed model is of constrained bi-objective mixed-integer nonlinear programming type, we apply a linear programming metric and goal attainment methods to turn the problem into a single-objective model. Each of two methods is separately embedded in simulated annealing, a genetic algorithm, and an imperialist competition algorithm to solve this bi-objective model by MATLAB™ software. Furthermore, we extend a priority-based encoding-decoding method that leads to feasible solutions. The performance of the proposed algorithms is examined on a set of problem instances. Then, some numerical results are provided to demonstrate the algorithm's effectiveness and efficiency further. For this purpose, we propose Duncan's multi-range test and TOPSIS methods.
机译：本文旨在在不确定性下，由制造中心，分销中心和客户节点组成的多周期多产品供应链网络设计（SCND）问题的双目标优化。该问题被视为两阶段运输模型，而每个工厂都有几种候选运输选择。假定的不确定性与仓库的服务有关，并影响到SCND的决策，这是至关重要的问题。因此，目标是做出最佳决策，以使每个时段为每个客户分发的产品的最小平均百分比最大化，同时最大程度地减少战略，战术和运营成本的总和。由于所提出的模型是约束双目标混合整数非线性规划类型，因此我们应用线性规划度量和目标达成方法将问题转化为单目标模型。两种方法分别嵌入模拟退火，遗传算法和帝国主义竞争算法中，以通过MATLAB™软件求解该双目标模型。此外，我们扩展了基于优先级的编解码方法，从而得出了可行的解决方案。在一组问题实例上检查了所提出算法的性能。然后，提供了一些数值结果，进一步证明了该算法的有效性和效率。为此，我们提出了邓肯的多范围测试和TOPSIS方法。

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