Optimizing Multi-Depot Vehicle Routing: An ABC-GA Hybrid Algorithm
TL;DR
A hybrid metaheuristic algorithm that integrates artificial bee colony and genetic algorithms is developed to solve MDVRP efficiently, demonstrating that the hybrid approach consistently produces lower routing costs, achieving superior solution quality at the expense of increased computational time.
Abstract
The Multi Depot Vehicle Routing Problem (MDVRP) is a generalized form of the Vehicle Routing Problem (VRP) and Travelling Salesman Problem (TSP). It is considered one of the NP-hard optimization problems. The MDVRP is a logistics problem that involves finding the most efficient route to transport goods between multiple different pickup and delivery locations. In this study a hybrid metaheuristic algorithm that integrates artificial bee colony and genetic algorithms is developed to solve MDVRP efficiently. The main objective of this study is to find the optimal route from multiple depots to serve a set of customers dispersed in different geographical locations. Initially nearest neighbor algorithm is used to assign customer to their nearest depot and randomly generated initial solution. These solutions are subsequently optimized using the proposed hybrid ABC-GA algorithm. The ABC algorithm serves as the main optimization framework, within which GA operators are embedded in the employed and onlooker bee phases. A probabilistic selection mechanism dynamically applies ABC neighbor search, GA operators, and 2-opt local search to improve solution quality. Finally, the results of the ABC-GA approach are compared against the outcomes of the conventional GA and ABC algorithm to assess performance improvements. Experimental results demonstrate that the hybrid approach consistently produces lower routing costs, achieving superior solution quality at the expense of increased computational time.