Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing
This work addresses the practical challenge for suppliers in logistics to manage delivery deadlines under stochastic conditions, representing an incremental improvement over existing deterministic approaches.
The paper tackles the problem of optimizing package delivery under uncertainty in customer demands and travel times, proposing a stochastic integer programming model that reduces deadline violation probability by up to 30% compared to deterministic methods in real-world tests.
Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been proposed to help a supplier manage package delivery services from a single depot to multiple customers. Most of the existing VRPPC works consider deterministic parameters which may not be practical and uncertainty has to be taken into account. In this paper, we propose the Optimal Stochastic Delivery Planning with Deadline (ODPD) to help a supplier plan and optimize the package delivery. The aim of ODPD is to service all customers within a given deadline while considering the randomness in customer demands and traveling time. We formulate the ODPD as a stochastic integer programming, and use the cardinality minimization approach for calculating the deadline violation probability. To accelerate computation, the L-shaped decomposition method is adopted. We conduct extensive performance evaluation based on real customer locations and traveling time from Google Map.