通过引入养护时间的有界不确定性集合,建立了路径规划问题的鲁棒优化模型,以使总服务成本最小化.设计开发分支切割算法,对该问题进行精确性求解.通过蒙特卡罗模拟法从总服务成本和服务水平两方面对解的鲁棒性进行评价,并对方案的鲁棒性水平进行敏感性分析.实验表明,利用鲁棒优化模型得到的解对服务时间偏差的敏感性较低.
This paper studies the vehicle routing problem for daily maintenance operations of a road network suffered from the uncertainty of service time. A robust optimization model is formulated to minimize total cost and the optimal problem is solved by the branch-and-cut method. In computational experiments, the performances of the robust solutions are analyzed using Monte Carlo simulation. The experimental analysis demonstrates that the robust optimization approach can yield routes that minimize total cost and are less sensitive to substantial deviations of service times. Decision makers may obtain some managerial insights from robust optimization solution to determine an appropriate routing strategy.
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