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Optimal resource allocation in military hospitals using inverse Erlang-B models

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  • Jihad S. Daba
  • Moustapha H. El Hassan
  • Elias G. Saadeh

Abstract

Effective resource allocation is vital for procurement management, optimising assets' use to meet fluctuating demands while minimising operational bottlenecks. This paper advances resource allocation strategies by employing inverse Erlang-B models, specifically applied to military hospitals. Incomplete gamma and Marcum-Q functions are used in novel computational formulations to improve accuracy across various traffic intensities. In contrast, inverse functions offer tight constraints for calculating server requirements under high load. Assuming a 5% rejection rate and an average patient stay of 1.5 days, a case study models daily military hospital ward admissions as an autoregressive process (AR(1)), projecting an average demand of 86 hospital beds, with a minimum of 79 and a maximum of 91 beds per day. The findings demonstrate the effectiveness of inverse Erlang-B models in capacity planning, providing scalable solutions for optimising resource utilisation in healthcare and industrial sectors (Gil et al., 2013; Marinkovic and Stosic, 2023a).

Suggested Citation

  • Jihad S. Daba & Moustapha H. El Hassan & Elias G. Saadeh, 2026. "Optimal resource allocation in military hospitals using inverse Erlang-B models," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 25(4), pages 485-511.
    • Handle: RePEc:ids:ijpman:v:25:y:2026:i:4:p:485-511
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