Optimizing Repair Allocation in Healthcare: Comparing Volume- and Value-Based Models under Capacity Constraints
DOI:
https://doi.org/10.47654/v29y2025i4p38-62Keywords:
Healthcare equipment maintenance, Optimization model, Repair task allocation, Eligibility-weighted assignment, Capacity-constrained schedulingAbstract
Purpose: This study compares two optimization models—volume-based and value-based—for allocating repair resources in healthcare equipment maintenance. It introduces a dual-model framework with eligibility-weighted fairness constraints, a combination not previously explored in the literature. The study highlights trade-offs between service volume and strategic value, offering practical and adaptable solutions, including for low-resource settings.
Design/methodology/approach: A linear optimization approach evaluates both models under identical capacity and eligibility constraints. The volume-based model maximizes repair volume, while the value-based model maximizes total repair value based on equipment criticality and facility performance. Both incorporate eligibility-weighted proportionality constraints to ensure fair and feasible assignments. Real-world–inspired data simulate multiple demand scenarios to compare assignment patterns and system outcomes.
Findings: The value-based model prioritized high-impact repairs and achieved a higher total repair value score of 1,157,270 compared to 1,130,998 in the volume-based model, with only a one-unit difference in repair volume. These dimensionless scores reflect strategic benefit rather than monetary value. The results highlight trade-offs between broad service coverage and targeted system impact. This study contributes to Decision Sciences by applying linear programming to optimize repair allocation under resource constraints.
Practical implications: Healthcare administrators can use these insights to align repair strategies with institutional priorities—whether maximizing throughput or clinical value.
Social implications: Timely repair of critical equipment enhances patient safety, service reliability, and health system resilience.
Originality/value: This research presents a novel comparison of volume- and value-based optimization models for healthcare repair. Both models incorporate eligibility-weighted proportionality constraints to support fair, effective planning across diverse facilities.
References
Bahreini, R., Doshmangir, L., & Imani, A. (2018). Factors affecting medical equipment maintenance management in developing countries: A systematic review. Journal of Clinical and Diagnostic Research, 12(4), IC01–IC07. https://doi.org/10.7860/JCDR/2018/31646.11375
Boppana, V. R. (2023). Data analytics for predictive maintenance in healthcare equipment. International Journal of Business & Management Science, 9(2). https://doi.org/10.53555/eijbms.v10i1.176
Chakraborty, S., Raut, R. D., Rofin, T. M., & Chakraborty, S. (2023). A comprehensive and systematic review of multi-criteria decision-making methods and applications in healthcare. Healthcare Analytics, 4, 100232. https://doi.org/10.1016/j.health.2023.100232
Dantzig, G. B. (1963). Linear programming and extensions. Princeton University Press.
De Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European Journal of Operational Research, 285(3), 805–824. https://doi.org/10.1016/j.ejor.2019.09.047
González-Domínguez, J., Sánchez-Barroso, G., & García-Sanz-Calcedo, J. (2020). Scheduling of preventive maintenance in healthcare buildings using Markov chain. Applied Sciences, 10(15), 5263. https://doi.org/10.3390/app10155263
Hillebrecht, M., Schmidt, C., Saptoka, B. P., Neuhann, F., & Jahn, A. (2022). Maintenance versus replacement of medical equipment: A cost-minimization analysis among district hospitals in Nepal. BMC Health Services Research, 22, 1023. https://doi.org/10.1186/s12913-022-08392-6
Hillier, F. S., & Lieberman, G. J. (2015). Introduction to Operations Research (10th ed.). McGraw-Hill Education.
Hossain, M. A., Ahmad, M., Islam, M. R., & David, Y. (2020). Mathematical modeling of clinical engineering approach to evaluate the quality of patient care. Health and Technology, 10(2), 547–561. https://doi.org/10.1007/s12553-019-00390-9
Laktash, V. (2015, March 4). Medical equipment maintenance: Performing an audit to improve efficiency and savings. Health Facilities Management. https://www.hfmmagazine.com/articles/1493-medical-equipment-maintenance
Leenawong, C., & Ritthipakdee, A. (2024). Fuel costs optimization for long-haul flight with refueling layovers. *International Journal of Technology, 15*(6), 1602–1612.
Li, J., Mao, Y., & Zhang, J. (2022). Maintenance and quality control of medical equipment based on information fusion technology. Computational Intelligence and Neuroscience, 2022, 9333328. https://doi.org/10.1155/2022/9333328
Liao, H., Cade, W., & Behdad, S. (2021). Markov chain optimization of repair and replacement decisions of medical equipment. Resources, Conservation and Recycling, 171, 105609. https://doi.org/10.1016/j.resconrec.2021.105609
Mahfoud, H., El Barkany, A., & El Biyaali, A. (2016). Preventive maintenance optimization in healthcare domain: Status of research and perspective. Journal of Quality and Reliability Engineering, 2016, 5314312. https://doi.org/10.1155/2016/5314312
Mahfoud, H., El Barkany, A., & El Biyaali, A. (2018). Dependability-based maintenance optimization in healthcare domain. Journal of Quality in Maintenance Engineering, 24(2), 200–223. https://doi.org/10.1108/JQME-07-2016-0029
Mwanza, J., Telukdarie, A., & Igusa, T. (2022). Optimizing maintenance systems of healthcare facilities in low-resource settings through modeling and multi-scenario discrete event simulation. SSRN. https://doi.org/10.2139/ssrn.4215608
Pedistat. (2025, March 4). Reduce medical equipment's maintenance cost. Pedistat Blog. https://www.pedistat.com/blog/reduce-medical-equipments-maintenance-cost
Salami, A., Afshar-Nadjafi, B., & Amiri, M. (2023). A two-stage optimization approach for healthcare facility location-allocation problems with service delivering based on genetic algorithm. International Journal of Public Health, 68, 1605015. https://doi.org/10.3389/ijph.2023.1605015
Stodola, P., & Stodola, J. (2020). Model of predictive maintenance of machines and equipment. Applied Sciences, 10(1), 213. https://doi.org/10.3390/app10010213
Taha, H. A. (2017). Operations Research: An Introduction (10th ed.). Pearson Education.
Yinusa, A., & Faezipour, M. (2023). Optimizing healthcare delivery: A model for staffing, patient assignment, and resource allocation. Applied System Innovation, 6(5), 78. https://doi.org/10.3390/asi6050078
Zamzam, A. H., Abdul Wahab, A. K., Azizan, M. M., Satapathy, S. C., Lai, K. W., & Hasikin, K. (2021). A systematic review of medical equipment reliability assessment in improving the quality of healthcare services. Frontiers in Public Health, 9, 753951. https://doi.org/10.3389/fpubh.2021.753951
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