Optimization of truck scheduling in cross-docking for perishables using Particle Swarm Optimization (PSO) with dual-mode doors

This study proposes a mathematical model for scheduling docking operations in a cold-chain environment, developed for a major Iranian producer of perishable goods. The model aims to minimize total operational cost by simultaneously addressing three components: (1) loading and unloading costs, determined by dock assignment and service times; (2) storage costs from holding products in cold chambers; and (3) spoilage costs caused by prolonged exposure to ambient conditions. It integrates operational constraints such as truck arrival and departure times, dock availability, cross-docking flows, spatial distances between zones, vehicle–product compatibility, and product-specific shelf-life. The nonlinear, combinatorial problem is solved using Particle Swarm Optimization (PSO) and compared with two alternative metaheuristics: Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). Results indicate significant efficiency gains: average truck turnaround time was reduced, with loading/unloading durations cut from 3.2 to 2.4 hours; spoilage costs fell by nearly 19%; storage costs declined from 34% to 27% of total costs; and total operational cost dropped by 15.6% compared to GA and 11.3% compared to GOA. These outcomes highlight the model’s capability to enhance cost-efficiency, minimize spoilage, and improve time management in cross-docking operations for perishable goods.