On the Value of Optimal Myopic Solutions for Dynamic Routing and Scheduling Problems in the Presence of User Noncompliance
On the Value of Optimal Myopic Solutions for Dynamic Routing and Scheduling Problems in the Presence of User Noncompliance
复制标题
论存在用户不合规情况下动态路由和调度问题的最佳近视解决方案的价值
DOI：
10.1287/trsc.34.1.67.12283
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
A. Marar
中科院分区：
文献类型：
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作者：
Warrren B Powell;Michael T. Towns;A. Marar
在动态环境中建模和解决路由和调度问题的最常见方法是解决一系列确定性的、短视的模型，尽可能接近最优。最常见的论点是，如果数据发生变化，那么我们应该简单地重新优化。我们使用卡车运输中出现的负载匹配问题的设置来比较最优近视解决方案与存在三种不确定性形式的不同程度的贪婪，次最优近视解决方案的价值：客户需求，旅行时间，以及特别感兴趣的用户不合规。利用仿真环境对不同系统动态水平下的调度策略进行了测试。我们考虑的一个重要问题是用户不遵守，即当用户不采用模型的所有建议时优化的影响。我们的研究结果表明，在相对较高的不确定性水平下，（近视）最优解仅略优于贪婪解，而在广泛的条件下，特定的次优解实际上优于最优解。
The most common approach for modeling and solving routing and scheduling problems in a dynamic setting is to solve, as close to optimal as possible, a series of deterministic, myopic models. The argument is most often made that, if the data changes, then we should simply reoptimize. We use the setting of the load matching problem that arises in truckload trucking to compare the value of optimal myopic solutions versus varying degrees of greedy, suboptimal myopic solutions in the presence of three forms of uncertainty: customer demands, travel times, and, of particular interest, user noncompliance. A simulation environment is used to test different dispatching strategies under varying levels of system dynamism. An important issue we consider is that of user noncompliance, which is the effect of optimizing when users do not adopt all of the recommendations of the model. Our results show that (myopic) optimal solutions only slightly outperform greedy solutions under relatively high levels of uncertainty, and that a particular suboptimal solution actually outperforms optimal solutions under a wide range of conditions.