何时使用模拟退火算法求解CVRP？——以波兰燃油配送为例的案例研究
When to use simulated annealing for solving CVRP? A case study of fuel deliveries in Poland
AI总结：
本文针对波兰加油站燃油配送的带容量约束车辆路径问题，提出结合模拟退火算法的定制CVRP模型，经与Gurobi求解的传统混合整数规划模型对比，验证了该启发式方法求解高效配送路径的有效性。
摘要：
本文针对加油站燃油配送场景下的带容量约束车辆路径问题（Capacitated Vehicle Routing Problem, CVRP）展开研究。CVRP的目标是最小化有限容量车队的总行驶距离，而燃油配送在CVRP框架下存在独特的复杂性。本文提出一种将模拟退火（Simulated Annealing, SA）算法与专为加油站网络定制的CVRP模型相结合的新方法，该模型纳入了车辆容量、各站点燃油需求、道路网络距离等现实约束。文章阐述了面向燃油配送的基于SA的CVRP模型设计，详细说明了目标函数（最小化距离）以及SA生成候选解的探索机制。为评估方法有效性，研究在波兰由Samat运输公司服务的加油站网络上开展了计算测试，将基于SA的CVRP模型与由Gurobi求解的传统CVRP混合整数规划模型进行性能对比，结果旨在证明所提出的基于SA的启发式算法在求解高效燃油配送路径方面的有效性。
英文摘要：
The paper addresses Capacitated Vehicle Routing Problem (CVRP) in the context of fuel delivery to gas stations. The CVRP aims to minimize total travel distance for a fleet with limited capacity. Fuel delivery, however, introduces unique complexities within the CVRP framework. We propose a novel approach that integrates the Simulated Annealing (SA) algorithm with a customized CVRP model specifically designed for gas station networks. This model incorporates real-world constraints like vehicle capacity, fuel demands at each station, and road network distances. The paper outlines the design of SA-based CVRP model for fuel delivery. We detail the objective function (minimizing distance) and the SA's exploration mechanism for generating candidate solutions. To assess its effectiveness, the proposed approach undergoes computational tests in Poland's gas station network serviced by the Samat transportation company. We compare the performance of our SA-based CVRP model with the conventional Mixed Integer Programming model for CVRP powered by Gurobi. The results aim to demonstrate the efficacy of the proposed SA-based heuristic in finding efficient routes for fuel deliveries.
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