Scheduling on-site service deliveries to minimise the risk of missing appointment times

Ji, Chenlu; Mandania, Rupal; Liu, Jiyin; Liret, Anne

doi:10.1016/j.tre.2021.102577

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…The research on emergency vehicle scheduling gradually becomes more comprehensive. Concerned about the urban emergency situation, emergency rescue involves various transportation tasks, such as sending the injured to the rescue center (RC) for treatment as soon as possible and accurately transporting different emergency supplies to each demand point (9,10). It is easy to overlook that the number of vehicles available for scheduling, and changes in vehicle speed at the current time, will also have an impact on the entire scheduling process.…”

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%