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An Ant Colony Optimization Method for Fuzzy Vehicle Routing Problem

机译:模糊车辆路径问题的蚁群优化方法

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This paper deals with the vehicle routing problem involved with fuzzy/imprecise vehicle travel times and customer service times, these fuzzy/imprecise times are represented as fuzzy numbers and interpreted as possibility distributions. According to the same consideration as the stochastic programming with recourse, we treate the in?uence of the fuzziness of travel times and service times as recourse cost. and solve the fuzzy vehicle routing problem through twostage decisions. As the result, a two-stage possibilistic programming model is formulated. By choosing an appropriate definition of fuzzy mean, we can show that the proposed model is equivalent to an ordinary crisp programming problem. Furthermore, We propose a solution method based on Ant Colony System (ACS) to obttain the best solution of the problem. Finally, some examples are given to illustrate the two-stage model and the solution algorithm.
机译:本文研究了与模糊/不精确的车辆行驶时间和客户服务时间有关的车辆路径问题,这些模糊/不精确的时间用模糊数表示,并解释为可能性分布。基于与具有资源的随机规划的相同考虑,我们将旅行时间和服务时间的模糊性作为资源成本的影响。通过两阶段决策解决模糊的车辆路径问题。结果,建立了一个两阶段的可能性规划模型。通过选择适当的模糊均值定义,我们可以证明所提出的模型等效于普通的清晰编程问题。此外,我们提出了一种基于蚁群系统(ACS)的解决方法,以获得对该问题的最佳解决方案。最后,给出一些例子来说明两阶段模型和求解算法。

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