arXiv subjects

Zhexi Fu

Publications and source records attributed to Zhexi Fu.

3 recordsLinked to original sources

arXiv2025

On the modular platoon-based vehicle-to-vehicle electric charging problem

We formulate a mixed integer linear program (MILP) for a platoon-based vehicle-to-vehicle charging (PV2VC) technology designed for modular vehicles (MVs) and solve it with a genetic algorithm (GA). A set of numerical experiments with five scenarios are tested and the computational performance between the commercial software applied to the MILP model and the proposed GA are compared on a modified Sioux Falls network. By comparison with the optimal benchmark scenario, the results show that the PV2VC technology can save up to 11.07% in energy consumption, 11.65% in travel time, and 11.26% in total cost. For the PV2VC operational scenario, it would be more beneficial for long-distance vehicle routes with low initial state of charge, sparse charging facilities, and where travel time is perceived to be higher than energy consumption costs.

cs.CY↗

arXiv2022

Dial-a-ride problem with modular platooning and en-route transfers

Modular vehicles (MV) possess the ability to physically connect/disconnect with each other and travel in platoon with less energy consumption. A fleet of demand-responsive transit vehicles with such technology can serve passengers door to door or have vehicles deviate to platoon with each other to travel at lower cost and allow for en-route passenger transfers before splitting. A mixed integer linear programming (MILP) model is formulated to solve this "modular dial-a-ride problem" (MDARP). A heuristic algorithm based on Steiner-tree-inspired large neighborhood search is developed to solve the MDARP for practical scenarios. A set of small-scale synthetic numerical experiments are tested to evaluate the optimality gap and computation time between exact solutions of the MDARP using commercial software and the proposed heuristic. Large-scale experiments are conducted on the Anaheim network with 378 candidate join/split nodes to further explore the potentials and identify the ideal operation scenarios of MVs. The results show that MV technology can save up to 52.0% in vehicle travel cost, 35.6% in passenger service time, and 29.4% in total cost against existing on-demand mobility services in the scenarios tested. Results suggest that MVs best benefit from platooning by serving "enclave pairs" as a hub-and-spoke service.

cs.CY↗

arXiv2021

The pickup and delivery problem with synchronized en-route transfers for microtransit planning

Microtransit and other flexible transit fleet services can reduce costs by incorporating transfers. However, transfers are costly to users if they must get off a vehicle and wait at a stop for another pickup. A mixed integer linear programming model (MILP) is proposed to solve pickup and delivery problems with vehicle-synchronized en-route transfers (PDPSET). The transfer location is determined by the model and can be located at any candidate node in the network rather than a static facility defined in advance. The transfer operation is strictly synchronized between vehicles within a hard time window. A heuristic algorithm is proposed to solve the problem with an acceptable solution in a much shorter computation time than commercial software. Two sets of synthetic numerical experiments are tested: small-scale instances based on a 5x5 grid network, and large-scale instances of varying network sizes up to 250x250 grids to test scalability. The results show that adding synchronized en-route transfers in microtransit can further reduce the total cost by 10% on average and maximum savings can reach up to 19.6% in our small-scale test instances. The heuristic on average has an optimality gap less than 1.5% while having a fraction of the run time and can scale up to 250x250 grids with run times within 1 minute. Two large-scale examples demonstrate that over 50% of vehicle routes can be further improved by synchronized en-route transfers and the maximum savings in vehicle travel distance that can reach up to 20.37% for the instance with 100 vehicles and 300 requests on a 200x200 network.

math.OC↗