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A fuzzy multi-objective vehicle routing problem for perishable products using gradient evolution algorithm

机译:一种利用梯度进化算法对易腐产品的模糊多目标载体路由问题

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Managing the distribution process for perishable product is quite challenging. This study extends a classical vehicle routing problem with time windows and time dependent travel time. It consider the uncertainty of perishable products' characteristics in the delivery process through balancing the load in each vehicle. This multi-objective balance cargo vehicle routing problem with time windows and time dependent (MOBCVRPTWTD) is solved by minimizing total cost and balancing the load in each vehicle. This model also considers the uncertainty of travel time which depend on travel congestion. The proposed model is solved using fuzzy multi-objective gradient evolution (GE) algorithm. In this paper, the original GE algorithm is modified into discrete GE algorithm and combined with fuzzy technique in order to solve multi-objective problem. The proposed method is verified using two data sets and compared with genetic algorithm (GA). The computational experiment shows that GE algorithm is able to find better result in shorter computational time.
机译:管理易腐产品的分配过程非常具有挑战性。本研究延长了时间窗口和时间依赖的旅行时间的经典车辆路由问题。它通过平衡每辆车的负荷来考虑易腐产品的特性的不确定性。通过最小化总成本并平衡每辆车的负载,解决了与时间窗口和时间依赖(MobCVrptWTD)解决了这一多目标余额的货运车辆路由问题。该模型还考虑了依赖旅行拥堵的旅行时间的不确定性。使用模糊多目标梯度进化(GE)算法来解决所提出的模型。本文中,原始GE算法被修改为离散GE算法,并与模糊技术组合以解决多目标问题。使用两个数据集进行验证所提出的方法,并与遗传算法(GA)进行比较。计算实验表明,GE算法能够在更短的计算时间内找到更好的结果。

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