Assignment of Medical Staff to Operating Rooms in Disaster Preparedness: A Novel Stochastic Approach

Shavarani, Seyed Mahdi; Golabi, Mahmoud; Vízvári, Béla

doi:10.1109/tem.2019.2940352

Cited by 13 publications

(10 citation statements)

References 66 publications

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“…Sathish Kumar et al [49] used queuing theory to study the resource scheduling problem in postdisaster management. Shavarani et al [50] proposed a non-linear model and developed three metaheuristic algorithms to solve the allocation of medical staff to operating rooms in a disaster problem. Xu et al [51] proposed a mixed-integer non-linear model multi-stage construction of rescue teams in disaster management and used an accelerated bi-level decomposition algorithm to solve it.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a bi-objective decision support model for the disaster management

et al. 2021

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This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a Bi-Objective Mixed Integer Programming (BOMIP) model is proposed to assign and schedule of the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents and the second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to more similar to the real-world, time-windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position on the corresponding relief team and fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the optimal solution in the reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large size test problems. Finally, in order to investigate the impact of some parameters on the Pareto frontier, the sensitivity analysis and the managerial suggestions are provided.

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Section: Literature Reviewmentioning

confidence: 99%

Designing a bi-objective decision support model for the disaster management

et al. 2021

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“…Table 8 gives a detailed breakdown of preparedness related studies based on problem type and methodology used. Heuristic [96][97][98][99][100][101][102][103][104][105][106][107][108] Decision analysis [85,102] Game theory [109] AI [86] Pre-positioning medical centers Mathematical programing [110] Relief inventory management Mathematical programing [81][82][83][84][85][86][87][91][92][93][94][95][111][112][113] Heuristic [98-100,104-108] AI [86] Simulation [111] Stochastic modeling [114] Staff planning Simulation [115][116][117] Machine learning [117] Heuristic [118] Shelter site location…”

Section: Preparedness Stagementioning

confidence: 99%

“…Minimize cost [86,87,94] Minimize cost and shortages [85,[91][92][93],100] Minimize cost and transport time [104] Minimize cost and victim travel time [106] Minimize cost and fatalities [82,105] Minimize transport time and shortages [81,95] Minimize cost and maximize equity [84,99] Minimize cost and shortages and maximize equity [107] Minimize cost, transport time and shortages [108] Minimize cost and maximize equity and reliability [98] Minimize cost, transport time and shortages and maximize equity [83] Maximize probability of satisfied demand [111] Maximize min. covered demand [112] Minimize cost and shortages and maximize lives saved [113] Staff planning Staffing levels Maximize expected number of functional operating rooms and minimize expected travel distance [118] * References highlighted in bold address multiple problem types.…”

Section: Inventory Levels Of Relief Suppliesmentioning

confidence: 99%

“…Random Real Khojasteh & Macit [81] W/ERS Real Paul & Wang [82] W/ERS Real Rahafrooz & Alinaghian [83] Random Random Boostani et al [84] Random Real Rezai et al [85] W/ERS Real Chen & Wang [86] Random Real Salehi et al [87] W/ERS Real Cavdur et al [88] W/ERS Real Yahyaei & Bozorgi-Amiri [89] W/ERS Real Noyan et al [90] W/ERS Real Bozorgi-Amiri et al [92] W/ERS Real Mahootchi & Golmohammadi [93] W/ERS Real Renkli & Duran [95] W/ERS Real Salman & Yucel [97] W/ERS Real Molladavoodi et al [98] W/ERS Real Ghasemi et al [100] W/ERS Real Lu [101] Random Random Saeidian et al [102] W/ERS Real Verma & Gaukler [103] Random Real Paul & MacDonald [105] W/ERS Real Mohammadi et al [107] W/ERS Real Tofighi et al [108] W/ERS Real Bell et al [109] Random Real Acar et al [110] W/ERS Real Yang et al [113] W/ERS Real Xu et al [115] W/ERS Real Shavarani et al [118] W/ERS Real Coutinho-Rodrigues et al [119] Random Real Bayram et al [120] W/ERS Real Bayram & Yaman [121] W/ERS Real Trivedi & Singh [122] W/ERS Real Kınay et al [123] W/ERS Real Kınay et al [124] W/ERS Real Xu et al [127] W/ERS Real Response Stage Ahmadi et al [147] W/ERS Real Najafi et al [151] Random Random Mohammadi et al [152] W/ERS Real Khalilpourazari et al …”

Section: Studymentioning

confidence: 99%

See 1 more Smart Citation

Use of OR in earthquake operations management: A review of the literature and roadmap for future research

Çoban

Scaparra

O’Hanley

2021

International Journal of Disaster Risk Reduction

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“…Specially in densely populated urban centers. An unexpected peak on the number of arrivals, besides of being hard to predict, can certainly have negative consequences (Shavarani et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Generally, hospitals and health centers have a specialized area called Intensive Care Unit (ICU) aimed to provide treatment for people who are extremely ill. An ICU is staffed with highly trained professionals who operate advance equipment. Patients are referred to ICUs when they have a condition that cannot be treated in a regular hospital room. For example: after being in a serious car accident, after having a heart attack or stroke, when suffering a serious infection such as pneumonia, or after having major surgery. The present research focusses in the development of prediction models based on machine learning algorithms to help planning and allocating medical staff at an ICU. The research was carried out following a classic 4-phase methodology: analysis, design, development, and validation. In the analysis, independent variables such as patient's age, gender, reason for visit, and arrival mode are identified. Data are then pre-processed and prepared for the following phases. Specialize software packages for data mining and machine learning were selected for the investigation. Most of the investigation's activities were planned at this point. During the design, different subsets of the independent variables were selected to be build prediction models. The size of the training and test set as well as the size of the validation set were also defined here. The phases of development and validation were carried out entirely using the software WEKA 3.9.6. By means of executing a battery of experiment and "trial and error" adjustments the prediction models were completed. To accomplish the goals of this research, thousands of records collected from a public hospital were considered. The dataset was divided in a part for training and test (80%) and another part for validation (20%). The approach to predict the medical specialty require for the arriving patients considered the development of models based on artificial neural networks and decision trees. In all cases, instead of a simple hold-out, a 10-fold crossvalidation scheme was applied. Results showed that, in general, prediction models with fewer classes generate higher rates of correct predictions and, therefore, they can be more useful when planning and allocating medical staff at an ICU. Even though correct prediction rates fluctuate significantly from one model to another, between 80% and 83%, some of the proposed models are reliable. In conclusion, results showed that prediction models based on machine learning algorithms can help planning and managing the need for professionals and support staff at ICU. The research might help understand the benefits of using machine learning in hospitals and health centers.

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Assignment of Medical Staff to Operating Rooms in Disaster Preparedness: A Novel Stochastic Approach

Cited by 13 publications

References 66 publications

Designing a bi-objective decision support model for the disaster management

Designing a bi-objective decision support model for the disaster management

Use of OR in earthquake operations management: A review of the literature and roadmap for future research

Developing Models Based on Machine Learning Algorithms to Estimate Medical Staffing Needs at Intensive Care Units

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