DOI: https://doi.org/10.36347/sjpms.2026.v13i08.002

Pages: 269-276

Academic staff distribution remains a major manpower planning challenge in many Nigerian higher institutions due to imbalances across academic cadres and qualification levels. This study developed a Hybrid Markov Chain Programming (HMC) model for achieving an optimal distribution of academic staff by cadre and qualification, using Federal Polytechnic Idah, Nigeria, as a case study. Secondary data on academic staff distribution, qualifications, recruitment, promotion, retirement, and other workforce transitions covering the period 2016–2025 were obtained from the institution's personnel records. Markov Chain analysis was employed to estimate transition probabilities and forecast academic staff distribution for the period 2026–2030, while Goal Programming was integrated to optimize staff distribution subject to institutional manpower requirements. The estimated transition probability matrix revealed high retention probabilities across academic cadres, indicating a relatively stable workforce with predictable promotion patterns. The developed hybrid model successfully generated forecast distributions and produced an optimized staff structure that improved cadre balance and qualification distribution while satisfying multiple manpower objectives simultaneously. The findings demonstrate that integrating stochastic forecasting with mathematical optimization provides a more robust framework for academic manpower planning than the application of either technique independently. The proposed Hybrid Markov Chain Programming model offers a practical decision-support tool for strategic workforce planning, succession management, and accreditation compliance in Nigerian polytechnics and other higher education institutions.