论文标题
在动态双目标车辆路线中寻求决策支持
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
论文作者
论文摘要
我们考虑一个动态的双目标车辆路由问题，其中一部分客户随着时间的推移要求服务。在其中，通过单个车辆行驶的距离，而动态进化多目标算法（Demoa）将未经来式动态请求的数量最小化，该算法（Demoa）在离散的时间窗口（ERAS）上运行。决策者在每个时代都做出决定，因此任何决定都取决于上述时代中做出的不可逆转的决定。为了了解决策的决策和互动/依赖性序列的影响，我们进行了一系列实验。更确切地说，我们修复了一组决策者首选项$ D $和ERAS $ N_T $的数量，并分析了所有$ | d |^{n_t} $决策者选项的组合。我们发现，对于随机统一实例（a）最终选定的解决方案主要取决于最终决定，而不是决策历史记录，（b）解决方案在不访问的动态客户的数量方面非常健壮，并且（c）动态方法的解决方案甚至可以占主导地位的解决方案。相比之下，对于与群集客户的情况，我们观察到对决策历史以及解决方案多样性的差异的强烈依赖。
We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences $D$ and the number of eras $n_t$ and analyze all $|D|^{n_t}$ combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.
