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Fuzzy two-stage material procurement planning problem

机译:模糊的两阶段物料采购计划问题

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Material procurement planning (MPP) deals with the problem that purchasing the right quantity of material from the right supplier at the right time, a purchaser can reduce the material procurement costs via a reasonable MPP model. In order to handle the MPP problem in a fuzzy environment, this paper presents a new class of two-stage fuzzy MPP models, in which the material demand, the spot market material unit price and the spot market material supply quantity are assumed to be fuzzy variables with known possibility distributions. In addition, the procurement decisions are divided into two groups. Some procurement decisions, called first-stage decisions, must be taken before knowing the the particular values taken by the fuzzy variables; while some other decisions, called second-stage decisions, can be taken after the realizations of the fuzzy variables are known. The objective of the proposed fuzzy MPP model is to minimize the expected material procurement costs over the two stages. On other hand, since the fuzzy material demand, the fuzzy spot market material unit price and the fuzzy spot market material supply quantity are usually continuous fuzzy variables with infinite supports, the proposed MPP model belongs to an infinite-dimensional optimization problem whose objective function cannot be computed exactly. To avoid this difficulty, we suggest an approximation approach (AA) to evaluating the objective function, and turn the original MPP model into an approximating finite-dimensional one. To show the credibility of the AA, the convergence about the objective function of the approximating MPP model to that of the original MPP one is discussed. Since the exact analytical expression for the objective function in the approximating fuzzy MPP model is unavailable, and the approximating MPP model is a mixed-integer program that is neither linear nor convex, the traditional optimization algorithms cannot be used to solve it. Therefore, we design an AA-based particle swarm optimization to solve the approximating two-stage fuzzy MPP model. Finally, we apply the two-stage MPP model to an actual fuel procurement problem, and demonstrate the effectiveness of the designed algorithm via numerical experiments.
机译:物料采购计划(MPP)解决了以下问题:在适当的时间从适当的供应商处购买了适当数量的物料,购买者可以通过合理的MPP模型降低物料采购成本。为了处理模糊环境下的MPP问题,提出了一种新型的两阶段模糊MPP模型,在该模型中,假设物料需求,现货市场物料单价和现货市场物料供应量是模糊的。具有已知可能性分布的变量。此外,采购决策分为两组。在知道模糊变量所取的特定值之前,必须先进行一些称为第一阶段决策的采购决策。在知道模糊变量的实现之后,可以做出其他一些称为第二阶段决策的决策。提出的模糊MPP模型的目的是在两个阶段中将预期的物料采购成本降至最低。另一方面,由于模糊物料需求,模糊现货市场物料单价和模糊现货市场物料供应量通常是带有无限支持的连续模糊变量,因此所提出的MPP模型属于目标函数不能满足的无限维优化问题。精确计算。为避免这种困难,我们建议一种近似方法(AA)评估目标函数,并将原始MPP模型转换为近似有限维模型。为了显示AA的可信度,讨论了近似MPP模型的目标函数与原始MPP模型的目标函数的收敛性。由于无法获得近似模糊MPP模型中目标函数的精确解析表达式,并且近似MPP模型是既不是线性也不是凸的混合整数程序,因此无法使用传统的优化算法来求解。因此,我们设计了一种基于AA的粒子群优化算法来求解近似的两阶段模糊MPP模型。最后,我们将两阶段MPP模型应用于实际燃料采购问题,并通过数值实验证明了所设计算法的有效性。

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