CLInstGen：面向双层城市物流的实例生成框架
CLInstGen: An Instance Generation Framework for Two-Tier City Logistics
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中文总结 AI 辅助
针对双层城市物流缺乏标准化基准数据的问题，提出开源实例生成框架CLInstGen及基准库CLLIB，基于真实地理信息生成标准化实例，并通过两类问题验证其通用性。
中文摘要 AI 辅助
城市物流关注城市区域内高效货运交通的设计与管理。双层网络尤为有效，在该网络中，货物通过不同的车队，利用中间整合设施，从配送中心运送到客户。尽管已从路径规划、选址和网络设计等角度进行了研究，但由于缺乏标准化、最先进的基准数据，研究进展受到阻碍。许多研究依赖于过时且人工构造或针对特定案例的实例，限制了结果的可重复性、可比性和普适性。为弥补这一空白，我们识别了双层城市物流的一般问题特征，提出了适用于广泛问题设置的实例生成框架CLInstGen，并构建了首个基准库CLLIB。该库和CLInstGen代码均为开源，使研究人员能够轻松使用、调整并生成适用于其特定应用的实例。CLLIB基于城市区域的真实地理信息，提供了一套经过整理的标准化实例，用于模型和方法的直接基准测试以及统计评估。一项涉及两种不同问题设置的计算研究，即双层战术服务网络设计和单层同时取送货车辆路径问题，证实了生成器和库的多功能性，并提供了关于CLLIB基准实例的首批数值和管理见解。
英文摘要
City logistics addresses the design and management of efficient freight transportation in urban areas. Two-tier networks, where goods are moved by different vehicle fleets from distribution centers to customers using intermediate consolidation facilities, are particularly effective. Although studied from routing, location, and network design perspectives, the research progress is slowed by the lack of standardized, state-of-the-art benchmark data. Many studies rely on outdated and artificial or case-specific instances, limiting reproducibility, comparability, and generality of results. To address this gap, we identify general problem characteristics for two-tier city logistics, provide an instance generation framework CLInstGen that is applicable to a wide range of problem settings, and propose the first benchmark library CLLIB. The library and the CLInstGen code are open-source, allowing researchers to easily use, adapt, and generate instances for their specific applications. CLLIB is based on real-world geographies of urban regions and provides a curated collection of standardized instances for direct benchmarking of models and methods as well as statistical evaluation. A computational study with two different problem settings, namely tactical service network design on two tiers and vehicle routing with simultaneous pickups and deliveries on a single tier, confirms the versatility of both the generator and the library, and provides first numerical and managerial insights on the CLLIB benchmark instances.
发表机构
- RPTU Kaiserslautern-Landau（莱茵兰-普法塔尔应用技术大学凯泽斯劳滕-兰道分校）
- Catholic University Eichstätt Ingolstadt（天主教艾希施泰特因戈尔施塔特大学）
机构由 AI 辅助整理，请以论文原文为准。
