Demand Selection for VRP with Emission Quota
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916757592080384 |
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| author | Najar, Farid Barth, Dominique Strozecki, Yann |
| author_facet | Najar, Farid Barth, Dominique Strozecki, Yann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19315 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demand Selection for VRP with Emission Quota Najar, Farid Barth, Dominique Strozecki, Yann Data Structures and Algorithms Artificial Intelligence Machine Learning 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. |
| title | Demand Selection for VRP with Emission Quota |
| topic | Data Structures and Algorithms Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.19315 |