A memetic algorithm for solving multiperiod vehicle routing problem with profit

Authors:
Chan Hou Che;Zizhen Zhang;Andrew Lim
Affiliations:
City University, Hong Kong, Hong Kong;City University, Hong Kong, Hong Kong;City University, Hong Kong, Hong Kong
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

Most literature on variations of vehicle routing problem assumes that a vehicle is continuously available within the planning horizon. However, in practice, due to the working time regulation, this assumption may not be valid in some applications. In this paper, we study a multiperiod vehicle routing problem with profit (mVRPP), where the goal is to determine a set of routes within the planning horizon that maximizes the collected reward from nodes visited. The vehicles can only travel during working hours within each period in the planning horizon. An effective memetic algorithm with giant-tour representation is proposed to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy split procedure to optimally partition a given giant-tour into individual routes. We conduct extensive experiments on a set of modified benchmark instances. The result demonstrates that our approach generates promising solutions which are close to the upper bounds.