A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains. (16th November 2020)

• Record Type: Journal Article
• Authors: Long, Sihui; Meng, Lingyun; Luan, Xiaojie; Trivella, Alessio; Miao, Jianrui; Corman, Francesco
• Other Names: Dong Jing Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2020(2020)
• Publication Date: 2020-11-16
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2020/8894174
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : The metro system is an important component of the urban transportation system due to the large volume of transported passengers. Hub stations connecting metro and high-speed railway (HSR) networks are particularly critical in this system. When HSR trains are delayed due to a disruption on the HSR network, passengers of these trains arriving at the hub station at night may fail to get their last metro connection. The metro operator can thus decide to schedule extra metro trains at night to serve passengers from delayed HSR trains. In this paper, we consider the extra metro train scheduling problem in which the metro operator decides how many extra metro trains to dispatch and their schedules. The problem is complex because (i) the arrival of delayed HSR trains is usually uncertain, and (ii) the operator has to minimize operating costs (i.e., number of additional trains and operation-ending time) but maximize the number of served passengers, which are two conflicting objectives. In other words, the problem we consider is stochastic and biobjective. We formulate this problem as a two-stage stochastic program with recourse and use an epsilon-constrained method to find a set of nondominated solutions. We perform extensive numerical experiments using realistic instances based on the Beijing metro network and two HSR lines connected to this network. We find that our stochastic model outperforms out-of-sample a deterministic model that relies on forecasts of the delay by a range of 3–5%. Moreover, we show that our solutions are nearly optimal by computing a perfect information dual bound and obtaining average optimality gaps below 1%.