论文标题
一种自适应数据驱动的方法来解决物流中的实际车辆路线问题
An adaptive data-driven approach to solve real-world vehicle routing problems in logistics
论文作者
论文摘要
运输占物流成本中数量的三分之一，因此，运输系统在很大程度上影响了物流系统的性能。这项工作提出了一种自适应数据驱动的创新模块化方法，用于解决物流领域中现实世界中车辆路由问题（VRP）。该作品由两个基本单元组成：（i）成功且完全可行地解决物流中VRP问题的创新多步算法，（ii）一种适应性方法，用于调整和设置所提出算法的参数和常数。所提出的算法结合了几种数据转换方法，启发式方法和禁忌搜索。此外，由于算法的性能取决于控制参数和常数集，因此提出了根据历史数据适应性地调整这些参数和常数的预测模型。使用决策支持系统与预测模型进行了比较，对获得的结果进行了比较：广义线性模型（GLM）和支持向量机（SVM）。该算法以及使用预测方法的控制参数被纳入了基于Web的企业系统中，该系统正在波斯尼亚和Herzegovina的多家大型分销公司中使用。将所提出的算法的结果与一组基准实例进行了比较，并在实际基准实例上进行了验证。在真实环境中，还提出了给定路线的成功可行性。
Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world Vehicle Routing Problems (VRP) in the field of logistics. The work consists of two basic units: (i) an innovative multi-step algorithm for successful and entirely feasible solving of the VRP problems in logistics, (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the Decision Support System with predictive models: Generalized Linear Models (GLM) and Support Vector Machine (SVM). The algorithm, along with the control parameters, which using the prediction method were acquired, was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.
