(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-92 July-2022
Full-Text PDF
Paper Title : An optimized deployment plan of ambulances for trauma patients
Author Name : Zaheeruddin and Hina Gupta
Abstract :

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.

Keywords : Ambulance allocation, Ambulance deployment, Emergency medical service, Trauma victims, Accident victims.
Cite this article : Zaheeruddin , Gupta H. An optimized deployment plan of ambulances for trauma patients. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(92):941-960. DOI:10.19101/IJATEE.2021.875567.
References :
[1]Matinrad N, Reuter-Oppermann M. A review on initiatives for the management of daily medical emergencies prior to the arrival of emergency medical services. Central European Journal of Operations Research. 2022; 30(1):251-302.
[Crossref] [Google Scholar]
[2]Bélanger V, Ruiz A, Soriano P. Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles. European Journal of Operational Research. 2019; 272(1):1-23.
[Crossref] [Google Scholar]
[3]Prabhu SB, Ravithejaswi UR, Shetty S, Hegde SS, Prasad SM. NavIC driven dynamic ambulance allocation and tracking. In international students conference on electrical, electronics and computer science 2022 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[4]Andersson H, Granberg TA, Christiansen M, Aartun ES, Leknes H. Using optimization to provide decision support for strategic emergency medical service planning–three case studies. International Journal of Medical Informatics. 2020.
[Crossref] [Google Scholar]
[5]Tshokey T, Tshering U, Lhazeen K, Abrahamyan A, Timire C, Gurung B, et al. Performance of an emergency road ambulance service in Bhutan: response time, utilization, and outcomes. Tropical Medicine and Infectious Disease. 2022; 7(6):87.
[Crossref] [Google Scholar]
[6]Zhang R, Zeng B. Ambulance deployment with relocation through robust optimization. IEEE Transactions on Automation Science and Engineering. 2018; 16(1):138-47.
[Crossref] [Google Scholar]
[7]Zhen L, Wang K, Hu H, Chang D. A simulation optimization framework for ambulance deployment and relocation problems. Computers & Industrial Engineering. 2014; 72:12-23.
[Crossref] [Google Scholar]
[8]Sharma N, Bairwa M, Gowthamghosh B, Gupta SD, Mangal DK. A bibliometric analysis of the published road traffic injuries research in India, post-1990. Health Research Policy and Systems. 2018; 16(1):1-11.
[Crossref] [Google Scholar]
[9]Singh SK. Road traffic accidents in India: issues and challenges. Transportation Research Procedia. 2017; 25:4708-19.
[Crossref] [Google Scholar]
[10]Blanchard IE, Doig CJ, Hagel BE, Anton AR, Zygun DA, Kortbeek JB, et al. Emergency medical services response time and mortality in an urban setting. Prehospital Emergency Care. 2012; 16(1):142-51.
[Crossref] [Google Scholar]
[11]https://healthmarketinnovations.org/program/centralized-ambulance-trauma-services-/cats-delhi. Accessed 28 February 2022.
[12]Toregas C, Swain R, Revelle C, Bergman L. The location of emergency service facilities. Operations Research. 1971; 19(6):1363-73.
[Crossref] [Google Scholar]
[13]Church R, Revelle C. The maximal covering location problem. In papers of the regional science association 1974 (pp. 101-18). Springer-Verlag.
[Google Scholar]
[14]Zonouzi MN, Kargari M. Modeling uncertainties based on data mining approach in emergency service resource allocation. Computers & Industrial Engineering. 2020.
[Crossref] [Google Scholar]
[15]Daskin MS. A maximum expected covering location model: formulation, properties and heuristic solution. Transportation Science. 1983; 17(1):48-70.
[Crossref] [Google Scholar]
[16]Erkut E, Ingolfsson A, Erdoğan G. Ambulance location for maximum survival. Naval Research Logistics. 2008; 55(1):42-58.
[Crossref] [Google Scholar]
[17]Gendreau M, Laporte G, Semet F. Solving an ambulance location model by tabu search. Location Science. 1997; 5(2):75-88.
[Crossref] [Google Scholar]
[18]Gendreau M, Laporte G, Semet F. A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel Computing. 2001; 27(12):1641-53.
[Crossref] [Google Scholar]
[19]Schmid V, Doerner KF. Ambulance location and relocation problems with time-dependent travel times. European Journal of Operational Research. 2010; 207(3):1293-303.
[Crossref] [Google Scholar]
[20]Shariat-mohaymany A, Babaei M, Moadi S, Amiripour SM. Linear upper-bound unavailability set covering models for locating ambulances: application to Tehran rural roads. European Journal of Operational Research. 2012; 221(1):263-72.
[Crossref] [Google Scholar]
[21]Rodriguez SA, Rodrigo A, Aguayo MM. A simulation-optimization approach for the facility location and vehicle assignment problem for firefighters using a loosely coupled spatio-temporal arrival process. Computers & Industrial Engineering. 2021.
[Crossref] [Google Scholar]
[22]Benabdouallah M, Yaakoubi OE, Bojji C. Genetic algorithm hybridised by a guided local search to solve the emergency coverage problem. International Journal of Mathematical Modelling and Numerical Optimisation. 2017; 8(1):23-41.
[Google Scholar]
[23]Apornak A, Raissi S, Keramati A, Khalili-Damghani K. Human resources optimization in hospital emergency using the genetic algorithm approach. International Journal of Healthcare Management. 2021; 14(4):1441-8.
[Crossref] [Google Scholar]
[24]Rivera G, Cisneros L, Sánchez-solís P, Rangel-valdez N, Rodas-osollo J. Genetic algorithm for scheduling optimization considering heterogeneous containers: a real-world case study. Axioms. 2020; 9(1):1-16.
[Crossref] [Google Scholar]
[25]Paul PV, Moganarangan N, Kumar SS, Raju R, Vengattaraman T, Dhavachelvan P. Performance analyses over population seeding techniques of the permutation-coded genetic algorithm: an empirical study based on traveling salesman problems. Applied Soft Computing. 2015; 32:383-402.
[Crossref] [Google Scholar]
[26]Victer PP, Ganeshkumar C, Dhavachelvan P, Baskaran R. A novel ODV crossover operator-based genetic algorithms for traveling salesman problem. Soft Computing. 2020; 24(17):12855-85.
[Crossref] [Google Scholar]
[27]Mundhenk T, Landajuela M, Glatt R, Santiago CP, Petersen BK. Symbolic regression via deep reinforcement learning enhanced genetic programming seeding. Advances in Neural Information Processing Systems. 2021; 34:24912-23.
[Google Scholar]
[28]Mirshekarian S, Süer GA. Experimental study of seeding in genetic algorithms with non-binary genetic representation. Journal of Intelligent Manufacturing. 2018; 29(7):1637-46.
[Crossref] [Google Scholar]
[29]Kolomvatsos K, Panagidi K, Hadjiefthymiades S. Optimal spatial partitioning for resource allocation. In ISCRAM 2013 (pp.747-57).
[Google Scholar]
[30]Hajipour V, Pasandideh SH. Proposing an adaptive particle swarm optimization for a novel bi-objective queuing facility location model. Economic Computation and Economic Cybernetics Studies and Research. 2012; 46(3):223-40.
[Google Scholar]
[31]Tsai Y, Chang KW, Yiang GT, Lin HJ. Demand forecast and multi-objective ambulance allocation. International Journal of Pattern Recognition and Artificial Intelligence. 2018; 32(7).
[Crossref] [Google Scholar]
[32]Swalehe M, Aktas SG. Dynamic ambulance deployment to reduce ambulance response times using geographic information systems: a case study of Odunpazari district of Eskisehir province, Turkey. Procedia Environmental Sciences. 2016; 36:199-206.
[Crossref] [Google Scholar]
[33]Jagtenberg CJ, Bhulai S, Van DMRD. Dynamic ambulance dispatching: is the closest-idle policy always optimal? Health Care Management Science. 2017; 20(4):517-31.
[Crossref] [Google Scholar]
[34]Schmid V. Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research. 2012; 219(3):611-21.
[Crossref] [Google Scholar]
[35]Naoum-sawaya J, Elhedhli S. A stochastic optimization model for real-time ambulance redeployment. Computers & Operations Research. 2013; 40(8):1972-8.
[Crossref] [Google Scholar]
[36]Enayati S, Mayorga ME, Rajagopalan HK, Saydam C. Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers. Omega. 2018; 79:67-80.
[Crossref] [Google Scholar]
[37]Ji S, Zheng Y, Wang W, Li T. Real-time ambulance redeployment: a data-driven approach. IEEE Transactions on Knowledge and Data Engineering. 2019; 32(11):2213-26.
[Crossref] [Google Scholar]
[38]Yavari M, Maihami R, Esmaeili M. Ambulance dispatching and relocation problem considering overcrowding of emergency departments. IISE Transactions on Healthcare Systems Engineering. 2022:1-2.
[Crossref] [Google Scholar]
[39]Wilde ET. Do emergency medical system response times matter for health outcomes? Health Economics. 2013; 22(7):790-806.
[Crossref] [Google Scholar]
[40]https://delhitrafficpolice.nic.in/sites/default/files/uploads/2020/Road-accident-in-delhi-2019. Accessed 23 June 2022.
[41]Bertsimas D, Ng Y. Robust and stochastic formulations for ambulance deployment and dispatch. European Journal of Operational Research. 2019; 279(2):557-71.
[Crossref] [Google Scholar]
[42]Koenig KL. Emergency ambulance utilization in Harlem, New York (July 1985). Prehospital Emergency Care. 2022; 26(3): W1-7.
[Crossref] [Google Scholar]
[43]El-masri S, Saddik B. An emergency system to improve ambulance dispatching, ambulance diversion and clinical handover communication—a proposed model. Journal of Medical Systems. 2012; 36(6):3917-23.
[Crossref] [Google Scholar]
[44]Liu J, Li J, Wang K, Zhao J, Cong H, He P. Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions. Advances in Mechanical Engineering. 2019; 11(4).
[Crossref] [Google Scholar]
[45]Pitchipoo P, Vincent DS, Rajakarunakaran S. Analysis of prime reasons for night time accidents in public transport corporations. In proceedings of international conference on advances in industrial engineering applications 2014.
[Google Scholar]
[46]Carvalho AS, Captivo ME, Marques I. Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness. European Journal of Operational Research. 2020; 283(3):1064-80.
[Crossref] [Google Scholar]
[47]Houck CR, Joines J, Kay MG. A genetic algorithm for function optimization: a Matlab implementation. Ncsu-ie tr. 1995; 95(9):1-10.
[Google Scholar]
[48]Yong-jie M, Wen-xia Y. Research progress of genetic algorithm. Application Research of Computers. 2012; 4:1201-6.
[49]Li X, Xiao N, Claramunt C, Lin H. Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem. Computers & Industrial Engineering. 2011; 61(4):1024-34.
[Crossref] [Google Scholar]
[50]Bajer D, Martinović G, Brest J. A population initialization method for evolutionary algorithms based on clustering and Cauchy deviates. Expert Systems with Applications. 2016; 60:294-310.
[Crossref] [Google Scholar]
[51]Xu P, Luo W, Xu J, Qiao Y, Zhang J, Gu N. An alternative way of evolutionary multimodal optimization: density-based population initialization strategy. Swarm and Evolutionary Computation. 2021.
[Crossref] [Google Scholar]
[52]Poli R, Kennedy J, Blackwell T. Particle swarm optimization. Swarm Intelligence. 2007; 1(1):33-57.
[Crossref] [Google Scholar]