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首页> 外文期刊>Nature reviews Cancer >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:高效插入启发式算法,用于时间窗口,换档时间限制和可变交付时间的多行程库存路由问题

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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:有效的插入启发式算法,允许每辆车多次旅行(EIH-MT),并提出使用后处理贪婪启发式(EIH-ST-GH)的每辆车单行,以解决时间窗口的多行程库存路由问题,换档具有短规划地平线的时间限制和可变交付时间(MTIRPTW-STL-VDT)。所提出的算法是基于具有两个增强功能的原始算法开发的。首先,计算和维护传递体积,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和可行解决方案的过程。这些算法显示为具有O的复杂性(n(4))。这些启发式最大化利润函数最大化,这是总交付量和负总旅行时间的加权求和。基于所罗门的测试问题,在280个实例上进行EIH-MT和EIH-ST-GH,三个重量套。检查最佳客观解决方案以说明各种限制的可行性。在不同重量值时,会观察到总输送量和总行程时间之间的权衡。基于车辆数量,利润和CPU标准的三个客户配置类型,没有单一获胜者启发式。在平均性能上,EIH-ST-GH优先于EIH-MT,用于群集配置类型,平均平均提高百分比:1.03%的利润,2.93%,CPU的车辆数量为38.68%。对于随机和随机群集配置类型,EIH-ST-GH应该由于更好的利润(用于随机0.27%和0.22%为随机簇)和CPU(46.96%随机和44.06%对随机簇)的改进是优选的。在针对单跳闸算法多跳闸算法的比较,在减少平均的车辆的数目的好处示在所有客户配置类型。

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