During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.

在 COVID-19 等重大传染病爆发期间，隔离人员的货物和包裹供应和分配已成为值得关注的关键问题。在这项研究中，我们提出了在重大传染病爆发期间的最后一英里交付中出现的交付问题。问题是在候选停车节点的子集上构建哈密顿旅行，每个客户被分配到旅行中最近的停车节点以取货或包裹。目的是最小化总成本，包括路线、分配和停车成本。我们提出了三个模型来表述这个问题，它们是基于节点的、基于流的和双层编程公式。此外，我们基于双层规划公式的思想开发了一种可变邻域搜索算法来解决该问题。最后，