Supply Chain Logistics With Hybrid Optimization using ADMM and Vehicle Routing Problem

Delivery routing and truck assignment for transportation resources are essential elements in the dynamic world of supply chain management and logistics. This study proposes a framework for maximising the number of trucks assigned and the delivery routes by using OR-Tools to solve the Vehicle Routing Problem (VRP) and the Alternating Direction Method of Multipliers (ADMM) for demand assignment. With consideration for each vehicle’s capacity, air conditioning condition, and demand priority, we created a realistic dataset that included warehouse locations, truck specifications, and delivery demand priorities. A time matrix was created by converting a distance matrix and assuming a constant speed of movement between the warehouses. Based on compatibility restrictions including vehicle capacity, air conditioning requirements, demand priority, and overall cost reduction, the ADMM algorithm allocates delivery demands to trucks. To successfully balance these aspects, the objective function incorporates weights for cost, priority, and distance. Following demand assignment, delivery routes are optimised using OR-Tools. Time windows and vehicle capacities are included as limitations to guarantee that deliveries are made within predetermined windows while reducing travel time and distance. In addition, a web application that offers an interactive platform for managing trucks, warehouses, and needs was created utilising the Flask framework. Through an easy-to-use interface, users can examine optimised routes, add new entries, and view extensive informative dashboard. By meeting delivery deadlines and maximising resource utilisation, this integrated approach produces notable operational efficiencies. This illustrates the potential of fusing cutting-edge optimisation techniques with useful application to handle real-world logistics problems.