Robust Optimization of Carpooling Routing Problem Under Travel Time Uncertainty

In order to relieve the negative impact of time uncertainty in carpooling process, the study focuses on the carpooling problem with travel time uncertainty. A budget uncertainty set is used to describe the travel time variable, and a budget coefficient with an adjustable uncertainty level is introduced to build a robust optimization model with the shortest total vehicle mileage and the least number of vehicles as the objective function. A two- stage algorithm is designed. In the first stage, based on the feasible carpooling routes between two passengers, we design a formulation to quantify the matching chance in terms of total vehicle mileage saving rate and the passenger time window matching flexibility, and then the matching chance is used as the weight to construct a passenger graph network and cluster the passengers. In the second stage, we design a Tabu search algorithm to solve the problem by constructing an initial solution with the sequential insertion heuristic method. The experimental results show that the clustering method can ensure the solution quality and improve the computational efficiency by more than 85% while reducing the passenger waiting time and detour distance. The robustness of the solution gradually improves when increasing the budget coefficient, but it increases the number of vehicles by 10%~40% and reduces the mileage saving rate by 1%~10%. The carpooling routes of the large-scale cases and the narrow time window cases are more sensitive to the uncertain time, and the wide time window cases can achieve a high level of robustness without adding too many additional vehicles and total mileage. © 2022 Science Press. All rights reserved.