Tractable approximations for the distributionally robust conditional vertex p-center problem: Application to the location of high-speed railway emergency rescue stations

Location of Railway Emergency Rescue Spots Based on a Near-Full Covering Problem: From a Perspective of Diverse Scenarios

“…Tang and Sun [28] built a multi-objective model to minimize the dispatch time of railway emergency resources and the number of railway emergency rescue bases. Wang et al [2] used a robust conditional vertex p-center problem for the location of a high-speed railway emergency rescue station. In their work, uncertainty of travel time between two points on the network is considered.…”

“…According to the National Bureau of Statistics of China 2021, the total operational mileage of the railway network is nearly 150,000 km [1]. Unfortunately, some damage or accidents caused by uncontrollable factors, i.e., natural disaster or human activity, may occur anywhere on the railway network [2][3][4]. If unexpected operation disruptions occur, this will bring a series of negative consequences, such as economic loss, injury, and/or even death.…”

“…For instance, the Langfang section on the Beijing-Shanghai railway was damaged by color-coated steel blown by strong winds on 12 August 2018. Indeed, the nearly 5 h rescue time for this emergency event could have been reduced by 50% if the relief train had been located at Beijing Nan station, closer to the rescue demand point [2]. Obviously, location of the emergency rescue spot based on strategic decision plays an vital role in emergency rescue management [5], and the emergency rescue management is an important aspect in sustainable development of city and transportation [6][7][8][9].…”

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

“…Esfahani and Kuhn [40] proved that the dual form of a Wasserstein-metric DRO model could be decomposed into finitely convex optimization problems with tractable computational complexity. Recently, this approach has also attracted much attention from certain engineering fields [41] , [42] , [43] , [44] , [45] , [46] , [47] . For example, focusing on the planning and scheduling problem under uncertain demand, Shang and You [41] formulated a two-stage DRO model to produce less conservative solutions.…”

“…Considering the integration of urban and rural public transport systems, Shang et al [43] developed a DRO model with ambiguous chance constraints regarding the travel time requirement, with the aim of minimizing the total construction cost. For a disaster relief management problem under demand uncertainty, Wang et al [44] proposed a DRO model to simultaneously optimize the integrated facility location, inventory pre-positioning, and delivery decisions. Yang et al [45] presented a distributionally robust chance-constrained program to model the last-train coordination planning problem in an urban rail transit system, in which uncertain passenger demand was captured by an ambiguous set.…”