Stochastic Fleet Size and Mix Consistent Vehicle Routing Problem for Last Mile Delivery

TL;DR

This paper presents a stochastic optimization model for last mile delivery that jointly determines fleet size, mix, and routing, incorporating demand uncertainty and using heuristics for large-scale problems.

Contribution

It introduces a two-stage stochastic mixed-integer programming model with a path-based reformulation and a Kernel Search heuristic for scalable solutions.

Findings

Effective in small synthetic instances

Validates approach on large real-world data

Shows impact of demand stochasticity on routing

Abstract

In this paper, we address the joint optimization of fleet size and mix, along with vehicle routing, under uncertain customer demand. We propose a two-stage stochastic mixed-integer programming model, where first-stage decisions concern the composition of the delivery fleet and the design of consistent baseline routes. In the second stage, approximate recourse actions are introduced to adapt the initial routes in response to realized customer demands. The objective is to minimize the total delivery cost, including vehicle acquisition, travel distance, and penalty costs for unserved demand. To tackle the computational challenges arising in realistic problem instances, we develop a path-based reformulation of the model and design a Kernel Search-based heuristic to enhance scalability. Computational experiments on small synthetic instances, generated through a population-density-based sampling approach, are conducted to validate the formulation and assess the effects of demand stochasticity through standard stochastic measures, after applying a scenario reduction technique. Additional tests on large-scale real-world instances, based on data from the Italian postal company, demonstrate the effectiveness of the proposed approach and provide managerial and practical insights.