OPTIMIZING DELIVERY ROUTES AND PACKAGE ALLOCATION FOR ENHANCED LOGISTICS EFFICIENCY

Abstract

With rapid advancements in e-commerce, increasing customer demands have put effective delivery management at the forefront of issues related to logistics and transportation companies. This study examines the Vehicle Routing Problem (VRP) and proposes a clever and metaheuristic way for effective delivery routing and distribution of items. Essentially, a means to improve scheduling, cost, resource productivity, and customer satisfaction. Research includes data on where items are delivered, where the depots are situated, what maximum loading each vehicle is capable of carrying, and obstacles to delivering services. Routing through clustering organizes the delivery points in respective modes and places where and how goods are to be delivered. Encoding uses ant colony optimization (ACO) to optimize routes through complicated problems such as vehicle constraints, time delivery, and dynamic destinations. Improvements in the algorithm performance shall also be attained by incorporating heuristics and evolution strategies. A web API was developed. During analysis, access to the most up-to-date routing information was ensured in real-time, with the application presenting the routes on interactive maps. Whereas static orders are currently managed, planned enhancements will allow for dynamic order handling, thereby enabling demand prediction via machine learning and real-time traffic information to enhance operational efficiency.