车辆路径问题的启发式方法综述

Kerem Buyukozdemir, Kazim Yildiz, Anil Bas, B. Uslu
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引用次数: 0

摘要

本研究回顾了基于启发式方法的方法,并在车辆路线问题的解决方案中包含处理过的GPS数据。车辆路径问题具有一定的局限性和复杂性。由于车辆路线的性质,必须在合理的时间范围内找到适当的解决方案。然而,由于点的数量和约束的限制,当问题的复杂性增加时,不可能扫描整个解空间并在理想的时间范围内找到最优解。为了解决这些情况,目标是找到最优解,或者是尽可能接近的最优解。这种情况揭示了使用启发式和元启发式算法作为路由问题解决算法的一些场景。在求解算法中加入GPS数据,提高了启发式算法和路由解的性能。处理后的数据还考虑了环境因素,如拥堵点、路线上的平均速度和按小时计算的交通密度。通过这种方式,可以为实际应用程序开发更一致的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Heuristic Approaches to Vehicle Routing Problems
This study reviews the approaches that are based on heuristic methods and include processed GPS data in their solutions for vehicle routing problems. Vehicle routing problems are challenging given their constraints and complexity. Due to the nature of vehicle routing, the appropriate solution must be found within a reasonable timeframe. However, it is not possible to scan the entire solution space and find the optimum solution within the ideal timeframe when the problem's complexity increases because of the number of points and the constraints. In order to solve these kinds of situations, the aim is finding the optimum solution, or the optimum solution as close as possible. This situation reveals some scenarios where heuristic and meta-heuristic algorithms are used as solution algorithms to routing problems. GPS data is included in the solution algorithms to increase the performance of heuristic algorithms and routing solutions. As a result of the processed data, environmental factors such as congestion points, average speed on the route and traffic density according to hours are also taken into account. In this way, more consistent solutions are developed for real-life applications.
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