Multi-period collection and distribution problems and their application to humanitarian gleaning operations
Multi-period collection and distribution problems and their application to humanitarian gleaning operations
复制标题
多时期收集和分配问题及其在人道主义拾取行动中的应用
作者：
Ohad Eisenhandler;M. Tzur
摘要我们关注的是一个多周期的物流环境，在这个环境中，一个给定的车队被用来从供应商那里收集产品，将它们转移到一个仓库，在那里它们经历了一些耗时的处理过程，只有在最后才能将它们分发给客户。问题是安排对供应商的访问，以及将加工产品分发给客户的车辆路线，使得每辆车每天最多可以执行这些活动类型中的一种。在仓库处理的库存在这些活动之间产生了额外的依赖性。非盈利性的粮食银行收集业务提供了一个现实生活中的动机，这种设置的分析。它也适用于献血的收集和分配。在我们提出的解决方法中，问题被分解成它的收集和分发方面，这两个方面都构成了以前没有研究过的非平凡的子问题。我们展示了如何单独处理它们中的每一个，同时考虑从它们的对应物中获得的信息，并将库存存储作为它们的联系。我们进一步提出了一个滚动的地平线框架的问题。我们展示了这种方法的实施的基础上的活动，以色列食品银行管理收集业务，并测试它与现实生活中的数据，以及随机生成的实例。数值实验建立了所提出的方法的优势，目前在实践中使用的天真的方法相比。
Abstract We focus on a multi-period logistic setting, in which a given fleet of vehicles is used to collect products from suppliers, transfer them to a depot where they undergo certain time-consuming processing procedures, only at the end of which can they be distributed to customers. The problem is to schedule visits to the suppliers, as well as vehicle routes that distribute the processed products to the customers, such that each vehicle can perform at most one of these activity types every day. The processed inventory at the depot creates an additional dependence between these activities. Non-profit gleaning operations performed by food banks provide a real-life motivation for the analysis of this setting. It is also applicable for the collection and distribution of blood donations. In the solution method we propose, the problem is decomposed into its collection and distribution aspects, both of which constitute non-trivial sub-problems that have not been previously studied. We show how to tackle each of them individually, while considering information obtained from their counterpart, with the inventory storage as their linkage. We further present a rolling horizon framework for the problem. We demonstrate the implementation of this approach based on the activity of an Israeli food bank that manages gleaning operations, and test it with real-life data, as well as with randomly generated instances. The numerical experiments establish the advantage of the proposed method, compared to naïve methods currently being used in practice.