The reliable design of a hierarchical multi-modes transportation hub location problems (HMMTHLP) under dynamic network disruption (DND). (August 2018)

• Record Type: Journal Article
• Authors: Torkestani, Sara Sadat; Seyedhosseini, Seyed Mohammad; Makui, Ahmad; Shahanaghi, Kamran
• Is Part Of: Computers & industrial engineering. Volume 122(2018)
• Page Start: 39
• Page End: 86
• Publication Date: 2018-08
• Subjects: Dynamic network disruption (DND) -- Hierarchical multi-modes transportation hub location problems (HMMTHLP) -- Dynamic system -- Monte Carlo simulation -- Two stage heuristic algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.027
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Expanding the reliable model of a hierarchical hub network with multi-modes of transportation. Designing the dynamic network in a stable space for airport-railway services. Modeling the multi-level hub network under a site- and time-dependent disruption probability. Developing a two-stage heuristic algorithm because of period-dependent variations. Using the Monte Carlo simulation method to assess the actual undefined dependent probability of disruption. Abstract: In ground and air transportation systems, services are operated as hub-and-spoke networks. For a given discrete planning horizon, disrupted hub nodes and edges can change the location of hub nodes and routes of transferring (allocation) at each level of hierarchy, so that the system can be converted to a flexible network capable of expanding or contracting in the long term. Because of the probability of hub nodes', hub edge' disruption and the periodic changes demands, A novel mixed-integer mathematical programming formulation that is effective for a hierarchical multi-modes transportation hub location problem (HMMTHLP) is presented. The main issue is how to tackle the hub nodes' and edges' disruption in a dynamic system. Various instances from the two different sets of data such as CAB dataset and Airport-Railway Network real-life case study (ARWN) are provided for computational analysis. The value of these tests in a multi-period solution with managerial insights of the two different cases are presented. A Monte Carlo simulation is used in a two-stage heuristic algorithm; this methodology provides a strategy with which to compare and discuss the locations-allocations of a dynamic network with or without any disruption.