This paper deals with the Stochastic Capacitated Arc Routing Problem (SCARP), obtained by randomizing quantities on the arcs in the CARP. Optimization problems for the SCARP are characterized by decisions that are made without knowing their full consequences. For real-life problems, it is important to create solutions insensitive to variations of the quantities to collect because of the randomness of these quantities. Efficient robust solutions are required to avoid unprofitable costly moves of vehicles to the depot node. Different criteria are proposed to model the SCARP and advanced concepts of a genetic algorithm optimizing both cost and robustness are provided. The method is benchmarked on the well-known instances proposed by DeArmon, Eglese and Belenguer. The results prove it is possible to obtain robust solutions without any significant enlargement of the solution cost. This allows treating more realistic problems including industrial goals and constraints linked to variations in the quantities to be collected.

翻译：本文涉及通过随机抽查CARP弧形弧上的数量获得的“斯托卡能力弧路路”问题。优化SARP的问题的特点是在不知道其全部后果的情况下作出决定。对于实际生活问题,必须创造对因这些数量随机而需收集的数量变化不敏感的解决方案。需要高效、稳健的解决办法,以避免将车辆转移到仓库节点,以避免无利可图的昂贵车辆移动到仓库节点。提出了不同的标准来模拟SARP,并提出了优化成本和稳健的先进遗传算法概念。该方法以DeArmon、Eglese和Belenguer提出的众所周知的例子为基准,其结果证明有可能在不大幅提高解决方案成本的情况下获得稳健的解决办法。这有利于处理更现实的问题,包括工业目标和与所收集的数量变化有关的制约因素。