This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as vehicle time window and capacity. To solve such a multiobjective combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A novel way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions which are robust to the stochastic nature of the problem.
['Chun Yew Cheong', 'Kay Chen Tan', 'Dikai Liu', 'Jian-Xin Xu']
A Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Stochastic Demand