Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

TL;DR

This paper introduces an optimal stochastic delivery planning model that minimizes deadline violations in vehicle routing under demand and travel time uncertainties, using a stochastic integer programming approach with decomposition techniques.

Contribution

It formulates a novel stochastic routing model with deadline constraints and applies a cardinality minimization approach for violation probability, enhancing practical delivery planning.

Findings

Effective in handling demand and travel time uncertainties.

Reduces deadline violations compared to deterministic models.

Validated with real-world data from Google Maps.

Abstract

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.