A New Approached Method for Solving the Four Index Transportation Problem with Fuzzy Parameters and Application Perspective in Industry

H. Pham, Fabien Escande, Laurent Klupinski
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Abstract

The four index transportation problem (4ITP: origin, destination, goods type, vehicle type) is an extension of the Hitchcock problem, a problem best suited for the goods allocation planning in the case for using the shuttle type. Based on the research results in 1979, in 2011, we developed an exact method for fully solving the 4ITP with determinist parameters. However, because of the limitation of human in recognizing the imprecise natural events in the decision making science, it is necessary to use fuzzy numbers for uncertain condition. By extending the problem toward this direction, in this paper, we develop a method, an extension of fuzzy programming, solving the 4ITP when all parameters such as the cost coefficients, the supply and demand quantities, the goods type and vehicle type quantities are fuzzy numbers and obey the Gaussian law in order 2. We use Gauss method in the fuzzy programming to obtain the costs under form of point cloud. From these results, we use Pham’s method, a method for the conventional 4ITP, to obtain a point cloud with the agglomerating costs. By using Kronechker’s method, we extract the obtained minimal cost under form of an interval for a given solution. It allows us to obtain a goods allocation planning with the minimal total transportation cost.
求解模糊参数四指标运输问题的新方法及其在工业中的应用前景
四指标运输问题(4ITP:出发地、目的地、货物类型、车辆类型)是希区柯克问题的延伸,是最适合于使用班车类型情况下货物分配规划的问题。在1979年研究成果的基础上,2011年提出了具有确定性参数的4ITP全解的精确方法。然而,在决策科学中,由于人类对不精确自然事件的识别能力有限,有必要对不确定条件使用模糊数。在此基础上,本文提出了一种模糊规划的扩展方法,用于求解成本系数、供给量、需求量、货种、车种数量等参数均为模糊数且服从2阶高斯律的4ITP问题。在模糊规划中采用高斯方法求解点云形式下的成本。根据这些结果,我们使用Pham的方法(传统的4ITP方法)来获得具有聚集成本的点云。利用Kronechker的方法,对给定的解在区间形式下提取得到的最小代价。它使我们能够以最小的总运输成本获得货物分配计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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