The critical interdependence between facility location and vehicle routing is a fundamental component of cold chain logistics management (CCLM). Furthermore, integrating information within CCLM has the potential to enhance operational efficiency, reduce costs, improve risk management, and elevate product quality, ultimately ensuring that temperature-sensitive goods are delivered in best condition. This paper introduces a novel non-linear multi-objective model designed to concurrently optimize warehouse facility location and vehicle routing, addressing the challenges inherent in cold chain logistics processes. The model seeks to minimize the aggregate costs related to transportation, facility location, and delivery tardiness. The study accounts for several pragmatic assumptions to address real-world scenarios: multiple delivery requests per customer, handling mixed commodities, and distributing mixed commodities using a single vehicle. This paper firstly studies simultaneous pickup and delivery with multiple requests and heterogeneous customer demands, each of which should be preserved in a different range of temperatures and needs different vehicle types. The epsilon-constraint method is employed to validate the proposed model, and a set of advanced, hybrid multi-objective evolutionary algorithms (MOEA) are presented to tackle the problem in a real-world context. A comprehensive set of performance metrics is utilized supported by rigorous statistical testing.

设施位置和车辆路线之间的关键相互依赖性是冷链物流管理 (CCLM) 的基本组成部分。此外，在 CCLM 内集成信息有可能提高运营效率、降低成本、改善风险管理并提高产品质量，最终确保对温度敏感的货物以最佳状态交付。本文介绍了一种新颖的非线性多目标模型，旨在同时优化仓库设施位置和车辆路线，解决冷链物流流程中固有的挑战。该模型旨在最大限度地减少与运输、设施位置和交货延迟相关的总成本。该研究考虑了几个务实的假设来解决现实场景：每个客户的多个交付请求、处理混合商品以及使用单一车辆分发混合商品。本文首先研究了具有多种请求和异构客户需求的同时取货和交付，每个请求都需要在不同的温度范围内保存并且需要不同的车辆类型。采用 epsilon 约束方法来验证所提出的模型，并提出了一组先进的混合多目标进化算法（MOEA）来解决现实世界中的问题。在严格的统计测试的支持下，使用了一套全面的性能指标。