This study investigates an on-demand first-and-last-mile ridesharing service (FLRS) problem considering the time-dependent travel time for an operator who manage a heterogeneous vehicle fleet. The operator, aiming to minimize the total operational cost, needs to simultaneously serve both first-mile (FM) and last-mile (LM) trips around a public transportation hub, such as a metro station. To holistically address this problem, we formulate a time-discretized mixed integer linear programming (MILP) model by constructing a time-expanded network and then extend a route-based set partitioning model. To yield good-quality solutions in a short computational time, a rolling-horizon-based column generation (RHCG) method is developed to handle real-time requests. An exact branch-and-price (BP) algorithm and a customized adaptive large neighborhood search (ALNS) algorithm are utilized to assess the solution quality of the applied RHCG. We conduct extensive numerical experiments created from real-world instances in Singapore to demonstrate the effectiveness of the proposed research methodology. The results of large-scale cases indicate that the RHCG outperforms both the commercial solver and the BP, and significantly reduces computational time in comparison with the ALNS. The implemented FLRS solution can decrease system-wide costs by 21.38% and increase shared-ride efficiency by 1.47 times, compared with the FM and LM services that operate separately.

中文翻译：

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通过异构车队和与时间相关的出行时间优化第一英里和最后一英里的拼车服务


本研究调查了按需第一英里和最后一英里拼车服务 （FLRS） 问题，考虑了管理异构车队的运营商的时间依赖性旅行时间。为了最大限度地降低总运营成本，运营商需要同时为公共交通枢纽（如地铁站）周围的第一英里 （FM） 和最后一英里 （LM） 行程提供服务。为了全面解决这个问题，我们通过构建一个时间扩展网络，然后扩展一个基于路由的集合分区模型，来构建一个时间离散的混合整数线性规划 （MILP） 模型。为了在短时间内产生高质量的解决方案，我们开发了一种基于滚动水平的色谱柱生成 （RHCG） 方法来处理实时请求。使用精确的分支和价格 （BP） 算法和定制的自适应大邻域搜索 （ALNS） 算法来评估所应用 RHCG 的求解质量。我们根据新加坡的真实世界实例进行了广泛的数值实验，以证明所提出的研究方法的有效性。大规模案例的结果表明，RHCG 的性能优于商业求解器和 BP，并且与 ALNS 相比，大大缩短了计算时间。与单独运行的 FM 和 LM 服务相比，实施的 FLRS 解决方案可以降低 21.38% 的系统成本，并将共享乘车效率提高 1.47 倍。