A Two-Stage Scheduling Model for the Tunnel Collapse under Construction: Rescue and Reconstruction

Hongjun Cui, Lijun Liu, Ying Yang and Minqing Zhu
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Hongjun Cui: School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
Lijun Liu: School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
Ying Yang: School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China
Minqing Zhu: School of Architecture and Art Design, Hebei University of Technology, Tianjin 300401, China

Energies, 2022, vol. 15, issue 3, 1-17

Abstract: In the process of transportation system construction, the tunnel is always an indispensable part of the traffic network due to terrain constraints. A collapse of the tunnel under construction may give rise to a potential for significant damage to the traffic network, complicating the road conditions and straining relief services for construction workers. To cope with the variety of vehicle types during the rescue effort, this paper divides them into small, medium, and large sizes, herein correcting the corresponding speed considering six road condition factors on account of the previous research. Given the influence of different special road conditions on the speed of different sized vehicles, a multi-objective model which contains two stages is presented to make decisions for rescue vehicle scheduling. Under the priority of saving human life, the first-stage objective is minimizing the arrival time, while the objective of the second stage includes minimizing the arrival time, unmet demand level, and scheduling cost. To solve the currently proposed model, a non-dominated sorting genetic algorithm II (NSGA-II) with a real number coding method is developed. With a real tunnel example, the acceptability and improvement of the model are examined, and the algorithm’s optimization performance is verified. Moreover, the efficiency of applying real number coding to NSGA-II, the multi-objective gray wolf algorithm (MOGWO), and the traditional genetic algorithm (GA) is compared. The result shows that compared with the other two methods, the NSGA-II algorithm converges faster.

Keywords: tunnel collapse; emergency rescue; vehicle scheduling; multi-vehicle size; NSGA-II (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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