车辆路线优化器用于垃圾收集和路线优化问题

IF 4.3
Hussam Fakhouri , Amjad Hudaib , Faten Hamad , Sandi Fakhouri , Niveen Halalsheh , Mohannad S. Alkhalaileh
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引用次数: 0

摘要

本文介绍了一种新的动态优化策略,称为车辆路径优化器(VRO),专门用于提高智慧城市的效率和可持续性。受车辆行为和交通系统中观察到的动态和相互作用的启发,VRO有效地平衡了探索和开发阶段,以发现最佳解决方案。该算法已经使用IEEE CEC2022基准套件进行了严格测试,与其他18个优化器相比,证明了其优越的性能。在智慧城市中,高效的废物管理和路径规划对于降低运营成本和最大限度地减少对环境的影响至关重要。因此,通过将垃圾箱分配和路由组件集成到一个单目标优化框架中,VRO已被应用于解决智慧城市的垃圾收集和路由优化问题(WCROP)。在解决智慧城市的WCROP问题时,使用从PVRP-IF案例中衍生的综合实例来评估VRO。结果表明,VRO算法在总成本、计算效率和求解可行性方面优于传统的分层和启发式算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Vehicle route optimizer for waste collection and routing optimization problem

Vehicle route optimizer for waste collection and routing optimization problem
This paper introduces a novel dynamic optimization strategy called the Vehicle Route Optimizer (VRO), specifically designed to enhance the efficiency and sustainability of smart cities. Inspired by the dynamics and interactions observed in vehicle behavior and traffic systems, VRO effectively balances exploration and exploitation phases to discover optimal solutions. The algorithm has been rigorously tested using the IEEE CEC2022 benchmark suites, demonstrating its superior performance compared to 18 other optimizers. In smart cities, efficient waste management and routing are critical for reducing operational costs and minimizing environmental impact. Thus, VRO has been applied to solve the Waste Collection and Routing Optimization Problem (WCROP) in smart cities by integrating bin allocation and routing components into a single-objective optimization framework. In addressing WCROP in Smart Cities, VRO was evaluated using synthetic instances derived from PVRP-IF cases. The results show that VRO outperforms traditional hierarchical and heuristic methods in terms of total cost, computational efficiency, and solution feasibility.
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CiteScore
5.60
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