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Using the Vehicle Routing Problem (VRP) to Provide Logistics Solutions in Agriculture

机译：使用车辆路径问题（VRP）为农业提供物流解决方案

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摘要

Agricultural producers consider utilizing multiple machines to reduce field completion times for improving effective field capacity. Using a number of smaller machines rather than a single big machine also has benefits such as sustainability via less compaction risk, redundancy in the event of an equipment failure, and more flexibility in machinery management. However, machinery management is complicated due to logistics issues.;In this work, the allocation and ordering of field paths among a number of available machines have been transformed into a solvable Vehicle Routing Problem (VRP). A basic heuristic algorithm (a modified form of the Clarke-Wright algorithm) and a meta-heuristic algorithm, Tabu Search, were employed to solve the VRP. The solution considered optimization of field completion time as well as improving the field efficiency. Both techniques were evaluated through computer simulations with 2, 3, 5, or 10 vehicles working simultaneously to complete the same operation. Furthermore, the parameters of the VRP were changed into a dynamic, multi-depot representation to enable the re-route of vehicles while the operation is ongoing.;The results proved both the Clarke-Wright and Tabu Search algorithms always generated feasible solutions. The Tabu Search solutions outperformed the solutions provided by the Clarke-Wright algorithm. As the number of the vehicles increased, or the field shape became more complex, the Tabu Search generated better results in terms of reducing the field completion times. With 10 vehicles working together in a real-world field, the benefit provided by the Tabu Search over the Modified Clarke-Wright solution was 32% reduction in completion time. In addition, changes in the parameters of the VRP resulted in a Dynamic, Multi-Depot VRP (DMDVRP) to reset the routes allocated to each vehicle even as the operation was in progress. In all the scenarios tested, the DMDVRP was able to produce new optimized routes, but the impact of these routes varied for each scenario.;The ability of this optimization procedure to reduce field work times were verified through real-world experiments using three tractors during a rotary mowing operation. The time to complete the field work was reduced by 17.3% and the total operating time for all tractors was reduced by 11.5%.;The task of a single large machine was also simulated as a task for 2 or 3 smaller machines through computer simulations. Results revealed up to 11% reduction in completion time using three smaller machines. This time reduction improved the effective field capacity.
机译：农业生产者考虑使用多台机器来减少田间完成时间，以提高有效田间生产能力。使用多个较小的机器而不是单个大型机器也具有好处，例如通过减少压实风险，在设备出现故障时提供冗余以及在机械管理方面具有更大的灵活性来实现可持续性。但是，由于物流问题，机械管理很复杂。在这项工作中，许多可用机械之间的现场路径分配和排序已转换为可解决的车辆路径问题（VRP）。使用基本的启发式算法（Clarke-Wright算法的改进形式）和元启发式算法Tabu Search来求解VRP。该解决方案考虑了优化现场完成时间以及提高现场效率。两种技术均通过计算机模拟对2、3、5或10辆同时工作以完成相同操作的车辆进行了评估。此外，将VRP的参数更改为动态的多站点表示，以便在操作进行过程中能够对车辆进行重新布线。结果表明，Clarke-Wright和Tabu Search算法始终能够产生可行的解决方案。禁忌搜索解决方案的性能优于Clarke-Wright算法提供的解决方案。随着车辆数量的增加，或者场的形状变得更加复杂，禁忌搜索在减少场完成时间方面产生了更好的结果。通过在现实世界中协同工作的10辆汽车，Tabu Search相对于改良的Clarke-Wright解决方案提供的收益是完成时间减少了32％。此外，VRP参数的更改导致动态多站点VRP（DMDVRP）可以重置分配给每辆车的路线，即使操作正在进行中。在所有测试的场景中，DMDVRP都能产生新的优化路线，但这些路线的影响在每种情况下都各不相同;该优化程序减少野外工作时间的能力已通过使用三台牵引车的真实世界实验进行了验证。旋转割草操作。完成现场工作的时间减少了17.3％，所有拖拉机的总运行时间减少了11.5％。通过计算机模拟，将一台大型机器的任务模拟为2到3台小型机器的任务。结果显示，使用三台较小的机器，完成时间最多减少11％。此时间的减少提高了有效场容量。

著录项

  • 作者

    Seyyedhasani, Hasan.;

  • 作者单位

    University of Kentucky.;

  • 授予单位 University of Kentucky.;
  • 学科 Agriculture.;Computer science.;Electrical engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 134 p.
  • 总页数 134
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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