Demand Selection for VRP with Emission Quota

TL;DR

This paper introduces a demand selection problem for the Vehicle Routing Problem with emission quotas, focusing on minimizing omitted deliveries while adhering to pollution limits, comparing OR and ML methods.

Contribution

It formulates the demand selection as a new problem (QVRP) and evaluates classical OR and ML approaches, finding OR methods outperform ML in static settings.

Findings

Classical OR methods outperform ML approaches in demand selection for QVRP.

The study introduces the MFVA problem as part of emission-aware VRP.

Results demonstrate the effectiveness of OR-based methods in static emission-constrained routing.

Abstract

Combinatorial optimization (CO) problems are traditionally addressed using Operations Research (OR) methods, including metaheuristics. In this study, we introduce a demand selection problem for the Vehicle Routing Problem (VRP) with an emission quota, referred to as QVRP. The objective is to minimize the number of omitted deliveries while respecting the pollution quota. We focus on the demand selection part, called Maximum Feasible Vehicle Assignment (MFVA), while the construction of a routing for the VRP instance is solved using classical OR methods. We propose several methods for selecting the packages to omit, both from machine learning (ML) and OR. Our results show that, in this static problem setting, classical OR-based methods consistently outperform ML-based approaches.