Timeliness evaluation of emergency resource scheduling

Chi, Huisheng; Li, Jialian; Shao, Xueyan; Gao, Mingang

doi:10.1016/j.ejor.2016.09.034

Cited by 23 publications

(14 citation statements)

References 26 publications

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“…Regarding disaster management and humanitarian operations, abundant literature exists (see e.g., Altay and Green III, 2006 ; Wex et al, 2014 ; Gupta et al, 2016 ; Rodríguez-Espíndola et al, 2018 ) and several papers investigate the healthcare operations for emergency situations (see e.g., Adan et al, 2011 ; Sung and Lee, 2016 ; Luscombe and Kozan, 2016 ; Chi et al, 2017 ). However, the nature of epidemic outbreaks and pandemics are meaningfully different from other disasters in terms of their dynamic nature, resources’ demand, global scale, and length.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource planning strategies for healthcare systems during a pandemic

Fattahi

Keyvanshokooh

Kannan

et al. 2023

European Journal of Operational Research

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We study resource planning strategies, including the integrated healthcare resources’ allocation and sharing as well as patients’ transfer, to improve the response of health systems to massive increases in demand during epidemics and pandemics. Our study considers various types of patients and resources to provide access to patient care with minimum capacity extension. Adding new resources takes time that most patients don't have during pandemics. The number of patients requiring scarce healthcare resources is uncertain and dependent on the speed of the pandemic's transmission through a region. We develop a multi-stage stochastic program to optimize various strategies for planning limited and necessary healthcare resources. We simulate uncertain parameters by deploying an agent-based continuous-time stochastic model, and then capture the uncertainty by a forward scenario tree construction approach. Finally, we propose a data-driven rolling horizon procedure to facilitate decision-making in real-time, which mitigates some critical limitations of stochastic programming approaches and makes the resulting strategies implementable in practice. We use two different case studies related to COVID-19 to examine our optimization and simulation tools by extensive computational results. The results highlight these strategies can significantly improve patient access to care during pandemics; their significance will vary under different situations. Our methodology is not limited to the presented setting and can be employed in other service industries where urgent access matters.

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Section: Literature Reviewmentioning

confidence: 99%

Resource planning strategies for healthcare systems during a pandemic

Fattahi

Keyvanshokooh

Kannan

et al. 2023

European Journal of Operational Research

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“…e postdisaster supply transportation model under uncertainty conditions was established in their research as well. Chi et al [9] put forward that different goals in emergency supply scheduling were correlated. eir study combined two scheduling objectives, i.e., time and resource satisfaction, into a timeliness function and designed a nonlinear evaluation model for emergency supply scheduling that incorporated multiple supply centers and a singleaffected point to realize that function.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Formula (8) denotes the limited number of transport vehicles dispatched from the distribution center. Formula (9) indicates that each vehicle goes to the a ected area only Represents the set of middle-level distribution centers (j ∈ J) It is 1 when the decision is made to repair road segment l, or it becomes 0 q kc e quantity of demand for supply c at location k 6 Complexity once at most. Formulas (10)-(13) guarantee the feasibility of the orderly driving of vehicles, which means that once a vehicle arrives at the supply-needed point later than the required time limit, there will be time delay.…”

Section: E Model For the Lower Levelmentioning

confidence: 99%

Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Chen

Yang

et al. 2020

Complexity

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It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

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“…Moreover, they used the approach of rescue points decomposition to reduce the dimension of the model, thus improving the solving efficiency. In addition, Hong et al [16] considered the problem of a resource supplied from multiple supply sites to a single disaster site, and proposed a non-linear timeliness evaluation function of the emergency resource scheduling which transformed the satisfaction of time and resources into a timeliness function. They provided a new thought for setting the objective function of an emergency scheduling problem.…”

Section: B Related Workmentioning

confidence: 99%

Robust Optimization for Emergency Scheduling of Oil Products After Disaster

Liao

Qiu

et al. 2019

IEEE Access

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Being one of the most important strategic materials, oil products play an irreplaceable role in the supply of emergency resources and the normal operation of various transportation vehicles. Faced with unexpected disasters, it is urgent to allocate oil products to the stricken areas. By taking the shortest emergency scheduling time and the lowest cost as the objective functions, this paper develops a multiobjective mixed integer linear programming (MILP) model accounting for multiple supply sites, multiple disaster sites, multiple transportation modes, uncertain demand and effectiveness of scheduling paths. To obtain the optimal scheduling scheme, the augmented ε-constraint method (AUGMECON) is combined with a robust optimization approach to solve the model. Finally, the model is applied to an emergency scheduling problem under the earthquake that occurred in Sichuan China in 2008. The results show that the model can not only effectively work out the emergency scheduling problem of oil products under demand uncertainty, but also ensure the robustness and optimality of the solution. Moreover, a comparison between single-objective and multi-objective models, as well as the analysis under different control parameters and disturbing parameters, are provided to validate the proposed model.

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Timeliness evaluation of emergency resource scheduling

Cited by 23 publications

References 26 publications

Resource planning strategies for healthcare systems during a pandemic

Resource planning strategies for healthcare systems during a pandemic

Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Robust Optimization for Emergency Scheduling of Oil Products After Disaster

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