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

Jihad S. Daba, Moustapha H. El Hassan and Elias G. Saadeh

International Journal of Procurement Management, 2026, vol. 25, issue 4, 485-511

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).

Keywords: Erlang-B model; inverse queueing theory; resource allocation optimisation; stochastic modelling; hospital bed capacity planning; mathematical optimisation in healthcare acquisitions; military hospital ward. (search for similar items in EconPapers)
Date: 2026
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