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首页> 外文期刊>Networks & Spatial Economics >Efficient Insertion Heuristic Algorithms for Multi-Trip Inventory Routing Problem with Time Windows, Shift Time Limits and Variable Delivery Time
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Efficient Insertion Heuristic Algorithms for Multi-Trip Inventory Routing Problem with Time Windows, Shift Time Limits and Variable Delivery Time

机译:具有时间窗,班次时限和交货时间可变的多行程库存路由问题的高效插入启发式算法

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摘要

Efficient insertion heuristic algorithms allowing multi trips per vehicle (EIH-MT) and allowing a single trip per vehicle with post-processing greedy heuristic (EIH-ST-GH) are proposed to solve the multi-trip inventory routing problem with time windows, shift time limit and variable delivery time (MTIRPTW-STL-VDT) with short planning horizon. The proposed algorithms are developed based on an original algorithm with two enhancements. First, the delivery volumes, the associated beginning delivery times and the exact profits are calculated and maintained. Second, the process to finalize a best-objective and feasible solution is developed. These algorithms are shown to have the complexity of O(n(4)). These heuristics maximize the profit function, which is the weighted summation of total delivery volume and negative total travel time. EIH-MT and EIH-ST-GH are performed on 280 instances based on Solomon's test problems with three weight sets. Best-objective solutions are examined to illustrate the feasibility of various constraints. The trade-offs between total delivery volume and total travel time are observed when varying weight values. There is not a single winner heuristic based on the number-of-vehicles, profit and CPU criteria across the three customer configuration types. On average performance, EIH-ST-GH is preferred over EIH-MT for cluster configuration type with the following average improvement percentages: 1.03% for profit, 2.93% for number-of-vehicles and 38.68% for CPU. For random and random-cluster configuration types, EIH-ST-GH should be preferred because of better profit (0.27% for random and 0.22% for random-cluster) and CPU (46.96% for random and 44.06% for random-cluster) improvements. In the comparison of the multi-trip algorithms against the single-trip algorithm, the benefits in reducing the number of vehicles on-average are shown across all customer configuration types.
机译:提出了一种有效的插入启发式算法,该算法允许每辆车多程行驶(EIH-MT),并允许每辆车单程行驶并具有后处理贪婪启发式算法(EIH-ST-GH),以解决带有时间窗,班次的多程库存路由问题时间限制和可变交付时间(MTIRPTW-STL-VDT),规划期很短。所提出的算法是在具有两个增强功能的原始算法的基础上开发的。首先,计算并维护交货量,相关的开始交货时间和确切的利润。其次,开发了最终确定最佳目标可行方案的过程。这些算法显示具有O(n(4))的复杂度。这些试探法使利润函数最大化,这是总交付量和负总旅行时间的加权总和。 EIH-MT和EIH-ST-GH在280个实例的基础上执行了所罗门的三组权重测试问题。研究了最佳目标解决方案以说明各种约束的可行性。当改变重量值时,可以观察到总输送量和总行程时间之间的权衡。没有基于三种客户配置类型的车辆数量,利润和CPU标准的获胜者启发。在平均性能方面,对于群集配置类型,EIH-ST-GH比EIH-MT更受青睐,具有以下平均改进百分比:利润为1.03%,车辆数量为2.93%,CPU为38.68%。对于随机和随机集群配置类型,应首选EIH-ST-GH,因为它具有更好的利润(随机集群为0.27%,随机集群为0.22%)和CPU(随机集群为46.96%,随机集群为44.06%)的改进。在将多行程算法与单行程算法进行比较时,所有客户配置类型均显示出减少平均车辆数量的优势。

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