Vehicle routing with probabilistic capacity constraints
研究了随机需求下的机会约束车辆路径问题，提出集合划分模型并用分支定价法求解，首次最优求解了VRP库中的大型实例，并通过仿真和敏感性分析揭示了路径失败概率对决策变量、期望成本和路径可靠性的影响。
In this paper, we study chance-constraint vehicle routing with stochastic demands. We propose a set-partitioning formulation for the underlying problem and solve it via a branch-and-price method. Our method is flexible in modeling different types of demand randomness while ensuring that the resulting problem is tractable. An extensive computational analysis, which includes simulation tests and a sensitivity analysis, is carried out to investigate the solution quality and computational efficiency. Some large instances of the underlying problems from the VRP library are solved to optimality for the first time. Our sensitivity analysis provides some useful insights about the impact of the probability of route failure on the decision variables, the expected cost and the route reliability.