Human resources optimization in hospital emergency using the genetic algorithm approach

Apornak, Arash; Raissi, Sadigh; Keramati, Abbas; Khalili‐Damghani, Kaveh

doi:10.1080/20479700.2020.1763236

Cited by 23 publications

(12 citation statements)

References 15 publications

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“…GA is an optimization procedure that is used to apply the “crossover,” “selection,” “mutation,” and “elitism” procedures. Recently, GA has been used in cancer, lung prediction [ 40 ], pupils' academic performance prediction [ 41 ], wellhead back based pressure control model [ 42 ], mutation for the test data generation systems [ 43 ], bearing accountability analysis of induction motors [ 44 ], optimization of human resources in sanitorium emergency [ 45 ], sensor acquisition frequency [ 46 ], singular nonlinear model [ 47 ] and automated manufacturing networks [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Botmart

Sabir

Javeed

et al. 2022

Informatics in Medicine Unlocked

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Section: Methodsmentioning

confidence: 99%

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Botmart

Sabir

Javeed

et al. 2022

Informatics in Medicine Unlocked

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“…In another work put forward by the authors in [4] three different case studies are analyzed using optimization techniques to support in the decision making policies for medical services. Apart from handling the deployment of ambulances some authors have improved the emergency services by optimizing the management of human resources for handling emergencies in hospitals [23]. Another work incorporating the use of GA is in scheduling surgeries in a Mexican Public Hospital [24].To improve the results of GA; a technique of population seeding is used to initialize the population in GA.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimized deployment plan of ambulances for trauma patients

2022

IJATEE

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Emergency medical service (EMS) control centres should focus on strategically deploying ambulances to enable trauma patients to receive better care. The work proposed here aims to find an optimal deployment plan of ambulances for the existing base stations using the genetic algorithm (GA) based optimization component. The GA has been modified by incorporating a new proportion-based population seeding method for initializing the population. Considering a set of assumptions, the authors have applied the new strategy for allocating an optimal count of ambulances to 28 base stations in Delhi. The working environment of EMS that includes stochastic requests, travel time, and dynamic traffic conditions has been taken into account, and the optimization strategy has been implemented in a MATLAB environment. With the proposed work, the authors have been able to reduce the average response time (ART) by 6.7%. The simulation result has also demonstrated a comparison between GA and particle swarm optimization (PSO). In addition, some numerical experiments are performed to conclude the impact of different attributes on the value of ART.

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“…Their findings show that four methods are generally utilized in stock (training, productivity, employee-to-population ratio and skill mix) and three in the field of demand (economic, requirements and service purposes). Apornak et al 's (2020) study proposed a genetic algorithm (GA) for identifying the number of variety nurses. Nikzad et al 's (2021) proposed resource assignment, districting, scheduling and placement decisions simultaneously for a healthcare system through a two-stage stochastic mixed-integer method.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-level nursing workforce planning considering talent management in healthcare with a dynamic quantitative approach

Ebrahimpour

Rahimnia

Pooya

et al. 2021

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PurposeWorkforce planning must answer how many workforces, in which positions, and talents, and when each organization is needed. To find the requirements workforce, organizations need to know the organizational position and talents pools. Clarifying the number of workforces required in each pool requires attention to workforce flows, including hiring, promotion, degradation, horizontal movement, and exiting the organization. It is a dynamic issue and must be addressed over several periods over a specific duration, which adds to the complexity. According to the talent management presented in this research, all the above complex questions are answered by applying the optimal control (OC) model according to talent management presented in this research.Design/methodology/approachThis research presents a dynamic model by using a linear-quadratic optimal control model, which was solved by Pontryagin's maximum principle, to achieve an optimal number of workforce requirements for each of the positions of nursing services manager, supervisor, head nurses and nurses in the health sector according to the required talents in each position.FindingsThe results have shown that the target value of workforce numbers has been achieved in the planning period, and the validation test and sensitivity analysis justified the model by reaching the workforce planning targets.Originality/valueThis study provides a dynamic model for achieving quantitative workforce planning targets; the model presented in this manuscript has included an important qualitative factor, namely workforce talents. According to the authors' review, there is no comprehensive research devoted to workforce planning through optimal control models by attention to workforces skills.

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Human resources optimization in hospital emergency using the genetic algorithm approach

Cited by 23 publications

References 15 publications

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses

An optimized deployment plan of ambulances for trauma patients

Multi-level nursing workforce planning considering talent management in healthcare with a dynamic quantitative approach

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