在碳排放限额下优化车辆配送，减少被取消订单数
Demand Selection for VRP with Emission Quota
- 提出最大可行分配法（MFVA）选择需取消的配送需求
- 传统运筹学方法比机器学习方法更稳定高效
- 适合关注低碳物流与智能调度的研究者
组合优化问题传统上使用运筹学（OR）方法解决，包括元启发式算法。本文针对带有碳排放限额的车辆路径问题（VRP），提出需求选择问题，称为QVRP。目标是在遵守污染配额的前提下，最小化被省略的配送数量。研究聚焦于需求选择部分，即最大可行车辆分配（MFVA），而车辆路径构建则采用经典运筹学方法求解。我们提出了多种选择待取消包裹的方法，涵盖机器学习（ML）和运筹学（OR）两类。结果表明，在静态问题设定下，基于传统运筹学的方法始终优于基于机器学习的方案。
原文摘要 · Abstract (English)
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.
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