Cold chain delivery route modeling and optimizing based on the clustered Whale Optimization Algorithm

Abstract

In addressing the optimization problem of cold chain logistics distribution paths under multiple constraints, comprehensive consideration is given to refrigeration parameters, cargo damage rates, and carbon emission factors. Systematic analysis is performed to quantify the combined effects of load capacity and ambient temperature on total operational costs. A traffic condition monitoring mechanism is subsequently integrated to dynamically evaluate roadway statuses, thereby enabling the acquisition of empirically validated transportation durations. Based on these operational parameters, a traffic-responsive optimization model for cold chain logistics (CCL) distribution routes is formulated. To address the complex multimodal characteristics of the model, the Clustering Whale Optimization Algorithm (CWOA) is proposed. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is employed to achieve dynamic population reorganization. An innovative path encoding rule based on search agent addresses is developed, with a sine-cosine oscillation operator introduced to replace linear search strategies during stochastic search processes, thereby enhancing the flexibility of individual search movements. Comparative testing on 23 benchmark functions from the IEEE Congress on Evolutionary Computation (CEC) effectively verifies CWOA's high precision and rapid convergence performance. The model and algorithm are subsequently applied to simulation experiments for cold chain logistics distribution in the Yangtze River Delta region, demonstrating CWOA's superior capability in solving CCL distribution path planning problems.