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Developing Multiobjective Equilibrium Optimization Method for Sustainable Uncertain Supply Chain Planning Problems

机译:开发多目标均衡优化方法,可持续不确定供应链规划问题

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This paper proposes a new multiobjective two-stage equilibrium optimization method for a supply chain planning problem with uncertain demand. To handle the ambiguity in the distribution of demand, the probability and possibility distributions are integrated to characterize the uncertain demand. As a result, the decision process in our equilibrium optimization problem is divided into two stages. In the first stage, decision variables should be taken before knowing the realizations of uncertain demand; while the second-stage decision variables must be taken after knowing the outcome of subjective uncertainty embedded in demand. On the basis of the proposed dynamic decision scheme, the objectives in the first-stage are constructed via credibilistic optimization methods. The objective and constraints in the second-stage are built via stochastic optimization methods. More specifically, three objectives in the first-stage are constructed based on the expected value operator and conditional value-at-risk of fuzzy variable, and the second-stage optimization model is built as a stochastic expected value model under a probabilistic constraint. When the random parameters follow normal distributions, the proposed equilibrium optimization model is equivalent to a triobjective two-stage credibilistic optimization model. To solve this model, we first employ a sequence of discrete possibility distributions to approximate continuous possibility distributions. Then, we design a new archive-guided multiobjective particle swarm optimization based on decomposition to solve the obtained approximate optimization model. Finally, numerical experiments via a light emitting diode industry problem are conducted to demonstrate the feasibility and effectiveness of the proposed optimization method and new heuristic algorithm.
机译:本文提出了一种新的多目标两级平衡优化方法,用于不确定需求的供应链规划问题。为了处理需求分配的歧义,概率和可能性分布集成到表征不确定的需求。结果,我们的均衡优化问题中的决策过程分为两个阶段。在第一阶段,在知道不确定需求的实现之前,应采取决策变量;虽然在知道需求嵌入的主观不确定性的结果之后,必须采取第二阶段决策变量。在拟议的动态决策方案的基础上,通过信贷优化方法构建第一阶段的目标。第二阶段的目标和约束是通过随机优化方法构建的。更具体地,第一阶段的三个目标是基于预期的值运算符和模糊变量的条件值 - 风险,并且在概率约束下作为随机预期值模型构建了第二阶段优化模型。当随机参数遵循正态分布时,所提出的平衡优化模型相当于三角形两级信任优化模型。为了解决这个模型,我们首先使用一系列离散的可能性分布来近似连续的可能性分布。然后,我们设计基于分解的新存档引导的多目标粒子群优化,以解决所获得的近似优化模型。最后,进行了通过发光二极管行业问题的数值实验,以证明所提出的优化方法和新的启发式算法的可行性和有效性。

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