Integrated location and routing for cold chain logistics networks with heterogeneous customer demand

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 nonlinear 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.