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

• Published in: Energies (Basel) 2022-02, Vol.15 (3), p.743
• Main Authors: Cui, Hongjun, Liu, Lijun, Yang, Ying, Zhu, Minqing
• Format: Article
• Language: English
• Subjects: Algorithms; Collapse; Construction industry; Costs; Decision making; Disasters; Emergency preparedness; emergency rescue; Genetic algorithms; Humanitarianism; Logistics; multi-vehicle size; Multiple objective analysis; NSGA-II; Objectives; Optimization; Priority scheduling; Rescue vehicles; Road conditions; Roads; Scheduling; Sorting algorithms; Supplies; Traffic; Transportation networks; Transportation systems; tunnel collapse; Tunnel construction; Tunnels; vehicle scheduling; Vehicles
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15030743

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.