Demand Selection for VRP with Emission Quota
This work addresses a specific optimization challenge in logistics and environmental management, but it is incremental as it applies existing methods to a new variant of a known problem.
The study tackled the demand selection problem for the Vehicle Routing Problem with an emission quota (QVRP), aiming to minimize omitted deliveries while respecting pollution limits, and found that classical operations research methods consistently outperformed machine learning approaches in this static setting.
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.