Open AccessUtilizing Ant Colony Optimization and Intelligent Water Drop for Solving Multi Depot Vehicle Routing Problem
Author(s) -
Sherylaidah Samsuddin,
Mohd Shahizan Othman,
Lizawati Mi Yusuf
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012095
Subject(s) - ant colony optimization algorithms , vehicle routing problem , metaheuristic , depot , computer science , ant colony , routing (electronic design automation) , mathematical optimization , operations research , artificial intelligence , engineering , computer network , mathematics , geography , archaeology
Multi-depot vehicle routing problem (MDVRP) is a real-world variant of the vehicle routing problem (VRP). MDVRP falls under NP-hard problem where trouble in identifying the routes for the vehicles from multiple depots to the customers and then, returning to the similar depot. The challenging task in solving MDVRP is to identify optimal routes for the fleet of vehicles located at the depots to transport customers’ demand efficiently. In this paper, two metaheuristic methods have been tested for MDVRP which are Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD). The proposed algorithms are validated using six MDVRP Cordeau’s data sets which are P01, P03, P07, P10, P15 and P21 with 50, 75, 100, 249, 160 and 360 customers, respectively. Thus, the results using the proposed algorithm solving MDVRP, five out of six problem data sets showed that IWD is more capable and efficient compared to ACO algorithm.