Multi-objective optimization for emergency healthcare resource allocation
Mahendra Alatemahendra.alate@gmail.comDirectorate of ResearchKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Tanveer Ahmad Wanitanveer.ahmad@niu.edu.inDepartment of PhysicsNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , *Manisha Tushar JadhavCorresponding authormanisha.jadhav@vit.eduDepartment of Electronics and Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Koti Tejasvikotitejasvi@vardhaman.orgDepartment of Computer Science and Engineering (Artificial Intelligence & Machine Learning)Vardhaman College of EngineeringShamshabad, Hyderabad, Telangana, 501218, IndiaView full profile → , K. Anithaanithak@maher.ac.inDepartment of Management StudiesMeenakshi College of Arts & ScienceMeenakshi Academy of Higher Education and ResearchChennai, Tamil Nadu, 600087, IndiaView full profile → , Dipak J. Dahigaonkardahigaonkardj@rbunagpur.inDepartment of Electronics and Communication EngineeringRamdeobaba UniversityNagpur, Maharashtra, 440013, India0000-0003-1878-9794View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2489
- Pages:
- 3619–3630
Abstract
Emergency healthcare systems are faced with the challenging problems of distributing scarce resources quickly, fairly and economically and balancing limited resources with unpredictable demand. This study aims to present a multi-objective optimization methodology for ambulance allocation, healthcare manpower deployment, beds, equipments and emergency supplies to healthcare facilities. The model concurrently seeks to minimize response time, cost of operation, resource limitations and risk to the patient and maximize the scope of coverage and the utilization of resources. Forecasts of demand, facility levels, transportation limitations and priorities are included in the decision process. Balanced allocation alternatives of conflicting objectives are identified using Pareto based optimization. Simulation results show that better results in emergency coverage, waiting time, allocation cost and resource available have been achieved than that of traditional single objective approaches. The framework can be used to guide emergency healthcare planning in case of a routine crisis and/or a large-scale disaster that is responsive, transparent, and based on evidence.
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References
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