Multi-objective Emergency Scheduling for Geological Disasters

Wan, Fang; Guo, Hao; Li, Jinling; Gu, Mingyun; Pan, Wenwen

doi:10.1007/s11069-020-04356-3

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…About the solution of the model, Yao et al used the non-dominated sorting multi-objective genetic algorithm with elite strategy (NSGA-II) to solve an emergency vehicle scheduling model with three objectives (31). Then, Fang et al proposed a hybrid ant colony optimization algorithm for solving multi-objective and multi-type dynamic vehicle scheduling models based on the center of the polygon circumference, and compared it with NSGA-II to prove its superiority (32). At present, the latest algorithm framework of NSGA-II can be used to improve the efficiency of the algorithm and the superiority of the solution, and solve the multi-objective emergency scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Li,

Wang,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Ye et al [31] proposed an enhanced particle swarm optimization combining multiagent theory and evolutionary population dynamics to solve the problem of emergency resources dispatching in oil spillage accidents. Wan et al [32] designed a hybrid ant colony optimization algorithm for solving a multibase dynamic emergency dispatching problem under geological disasters. Zahedi et al [33] used the NSGA-II algorithm to solve a multiobjective programming model for emergency material distribution and vehicle routing.…”

Section: Bioinspired Algorithmsmentioning

confidence: 99%

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

Duan

Xiong

2022

Journal of Advanced Transportation

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In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.

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