An exact and metaheuristic optimization framework for solving Vehicle Routing Problems with Shipment Consolidation using population-based and Swarm Intelligence

The Vehicle Routing Problem with Shipment Consolidation (VRPSC) is a novel variation of the vehicle routing problem that involves multiple commodities, multiple dimensions, a fleet with different types of vehicles, and the challenge of consolidating shipments during the route. Exact algorithms have been suggested to solve the VRPSC problems. Besides exact algorithms, specific metaheuristic algorithms are employed to deliver solutions of superior quality, albeit not necessarily optimal. The Artificial Immune System (AIS) and Genetic Algorithm (GA) are the metaheuristic optimization techniques applied in this research. Genetic programming is included in evolutionary computing, while AIS is included in swarm intelligence. This research presents a vehicle routing model with product consolidation and different product dimensions. These criteria were selected due to their significant impact on the complexity of mathematical problem-solving in VRPSC. The results from applying these metaheuristic algorithms will be compared with those of exact algorithms to compare and analyse different VRPSC solution approaches. • Optimize the Vehicle Routing Problem with Shipment Consolidation using exact (Gurobi) and metaheuristic methods of Genetic Algorithm and Artificial Immune System. • Show Genetic Algorithm has consistent and stable travel distance results, and Artificial Immune System has faster execution times but more significant variability in results. • Recommend Genetic Algorithm for reliability and consistency, and further optimization for Artificial Immune System to reduce variability. • Show the number of nodes and demand significantly impact travel distance for both algorithms. • Suggest exploring hybrid algorithms combining Genetic Algorithm and Artificial Immune System strengths for improved optimization in future research.