Demand Selection for VRP with Emission Quota

Farid Najar, Dominique Barth, Yann Strozecki

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Tracks: Special Session 1: (Deep) Reinforcement Learning in OR Optimization

Keywords: Demand selection, Reinforcement learning, Operations research

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.

Submission Number: 11