A fast and effective insertion algorithm for multi-depot vehicle routing problem with fixed distribution of vehicles and a new simulated annealing approach

Authors:
Andrew Lim;Wenbin Zhu
Affiliations:
Dept of Industrial Engineering and Logistics Management, Hong Kong Univ of Science and Technology, Kowloon, Hong Kong;Dept of Industrial Engineering and Logistics Management, Hong Kong Univ of Science and Technology, Kowloon, Hong Kong
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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Abstract

Multi-depot Vehicle Routing Problem has great practical value to the logistics and supply chain management. The fixed distribution of vehicles variant (MDVRPFD) brings it one step closer to the practical use. Based on the simple fact that all sub-routes of an optimal route must be optimal, a new randomized best insertion (RBI) algorithm is proposed. The proposed insertion algorithm is highly effective in minimizing number of vehicles and fast. Compared to the best known result published for MDVRPFD variant, the solutions generated by this new insertion algorihtm require 20% fewer vehicles. Adopting a generalized n-op neighborhood operator, a Simulated Annealing approach yields a reduction of 12% in total distance compared to best known results of MDVRPFD. Areas: meta-heuristic, vehicle routing, industrial applications of AI.