Abstract In this paper, a ride-sharing system that supports frequent updates of participants’ information is studied, for which driver-rider matching and associated routes need to be decided quickly. Uncertain travel time is considered explicitly when matching and route decisions are made; a robust optimization approach is proposed to handle it properly. To achieve computational tractability, an extended insertion algorithm in conjunction with a tabu search method is proposed, and a cluster-first-route-second approach is used to find heuristic solutions. In particular, a greedy heuristic and k-means algorithm are used to group the riders and their respective results, along with non-clustering case outcomes, are compared. Numerical examples show that the ride-sharing system considered in this paper can be a viable solution by means of our proposed approaches even for challenging cases where the scale of the system is large and decisions need to be made quickly.

中文翻译：

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出行时间不确定下的拼车：稳健优化和聚类方法

摘要 本文研究了一种支持频繁更新参与者信息的共享乘车系统，需要快速确定驾驶员与乘客的匹配和关联路线。在做出匹配和路线决策时，会明确考虑不确定的旅行时间；提出了一种稳健的优化方法来正确处理它。为了实现计算的易处理性，提出了一种结合禁忌搜索方法的扩展插入算法，并使用聚类优先路由第二方法来寻找启发式解决方案。特别是，贪婪启发式算法和 k 均值算法用于对骑手进行分组，并将他们各自的结果与非聚类案例结果进行比较。