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A Genetic Algorithm to Logistics Distribution Vehicle Routing Problem with Fuzzy Due Time

机译:具有模糊到期时间的物流配送车辆路径问题的遗传算法

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With the intensification of market competition and fast development of science and technology, many enterprises have begun to realize the importance of logistic distribution vehicle routing problem under uncertainty environment, and begin to pay more attention to the research of this problem. In this paper, The traditional vehicle routing problem with time windows is expanded to the situation that the time window is replaced by fuzzy due time which can represent the preferences of the customers. After a simple description of fuzzy due time and the fuzzy vehicle routing problem, a multi-objective mathematical model for the problem is built. Then, an insertion heuristic-based hybrid genetic algorithm is proposed to this kind of problem. In this algorithm, the modified push-bump-throw procedure is employed to handle the fuzzy nature of the problem. Finally, an extensive computational analysis is presented, which identifies the tradeoffs between the different objectives, and the superiority of the genetic algorithm is proved through the comparison of the genetic algorithms with improved Solomon insert heuristic algorithm.
机译:随着市场竞争的加剧和科学技术的飞速发展,许多企业已经开始意识到不确定环境下物流配送车辆选路问题的重要性,并开始更加重视这一问题的研究。本文将传统的带有时间窗的车辆路径问题扩展到用模糊的到期时间代替时间窗的情况,这种时间可以代表顾客的偏好。在对模糊到期时间和模糊车辆路径问题进行简单描述之后,建立了该问题的多目标数学模型。然后,针对此类问题提出了一种基于插入启发式的混合遗传算法。在该算法中,采用了改进的推-跳-投掷程序来处理问题的模糊性质。最后,进行了广泛的计算分析,确定了不同目标之间的折衷,并通过将遗传算法与改进的Solomon插入启发式算法进行比较,证明了遗传算法的优越性。

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