平衡动态多个旅行推销员：算法和连续近似
Balanced dynamic multiple travelling salesmen: algorithms and continuous approximations
摘要：
动态路由发生在客户事先不知道的情况下，例如实时路由。提出了两种求解平衡动态多旅行商问题（BD-mTSP）的算法.这些物流代表了动态（在线，实时）路由的操作（战术）工具。提出了动力学的几种类型和范围。特别注意顺序动态。平衡动态最近车辆启发式算法（BD-CVH）和平衡动态分配车辆启发式算法（BD-AVH）适用于这种类型的动态。该算法适用于广泛的测试实例。出租车服务和仓库中的调色板转移演示了如何在真实场景中使用BD-mTSP算法。连续近似模型的BD-mTSP的推导和动态路由的战略工具。该模型使用车辆、客户和动态范围来表示路线长度，而不需要运行算法。使用机器学习方法获得回归模型。其中两个模型的平均绝对百分误差低于3%。
英文摘要：
Dynamic routing occurs when customers are not known in advance, e.g. for real-time routing. Two heuristics are proposed that solve the balanced dynamic multiple travelling salesmen problem (BD-mTSP). These heuristics represent operational (tactical) tools for dynamic (online, real-time) routing. Several types and scopes of dynamics are proposed. Particular attention is given to sequential dynamics. The balanced dynamic closest vehicle heuristic (BD-CVH) and the balanced dynamic assignment vehicle heuristic (BD-AVH) are applied to this type of dynamics. The algorithms are applied to a wide range of test instances. Taxi services and palette transfers in warehouses demonstrate how to use the BD-mTSP algorithms in real-world scenarios. Continuous approximation models for the BD-mTSP's are derived and serve as strategic tools for dynamic routing. The models express route lengths using vehicles, customers, and dynamic scopes without the need of running an algorithm. A machine learning approach was used to obtain regression models. The mean absolute percentage error of two of these models is below 3%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
