Offline IoT-Enhanced Open Vehicle Routing for Perishable Products: A Resilient Optimization Framework

This paper presents an offline IoT-based framework for the Open Vehicle Routing Problem with Time Windows (OVRPTW) in perishable-goods logistics. It uses historical IoT and GPS data, temperature logs, travel times, and failure records to pre-plan cost-effective, resilient routes. The Mixed-Integer Linear Programming (MILP) formulation integrates perishability constraints, heterogeneous multi-compartment fleets, driver-workload equity, and resilience mechanisms through backup vehicle assignments. The model was implemented using PuLP–CBC and tested on a cold-chain instance. Numerical results confirm that the framework minimizes freshness-loss penalties and operational costs while maintaining service reliability and workload balance. A sensitivity analysis reveals that perishability has the most significant influence on total cost, followed by the fleet and equity factors. In contrast, variations in travel time have a minor effect.