Efficient active–passive vehicle coordination in multimodal transportation networks
研究了一种新型门到门按需运输服务，通过混合整数线性规划和自适应大邻域搜索启发式算法，协调主动车辆与被动模块化容器，在慕尼黑地铁网络模拟中使客户到达时间降低约17%，基础出行时间降低21%。
This work examines a complex routing problem involving passive, modular vehicle containers (pods) in conjunction with mobile platforms like trains, trucks, and boats. These are deployed as part of a door-to-door, demand-responsive transportation service designed to fulfill customer requests. The novel approach aims to optimize customer satisfaction while minimizing operator costs, offering significant improvements over traditional public transport systems. The main challenge lies in the need to synchronize active and passive vehicles, creating strong interdependencies between individual routes. To tackle this, we present a mixed-integer linear program and solve the problem using an adaptive large neighborhood search heuristic. Since a real-life scenario requires rapid solutions, a clustering approach is introduced to speed up the heuristic. We validate the efficiency of our method through an extensive numerical study on novel benchmark sets of varying sizes. Furthermore, we generate realistic instances by replicating Munich’s Zone M metro network and demonstrate that, beyond the improved customer comfort enabled by the modular approach, the multimodal Ansatz reduces customer arrival times by approximately 17% and base travel times by 21% compared to a unimodal baseline.