A study on Efficient Modelling in Higher Education Academic Workforce Using Simulation


  •   Nethal K. Jajo

  •   Shelton Peiris


This paper shows how the Operations Research (OR) tools (in modelling and simulation) can be modified, applied in planning and their understanding of any long-term impacts due to sudden policy changes. The proposed approach is particularly useful to investigate the movements of the university academics and their impacts on changes in research, research funding, teaching and services as they are the integral parts of the career at any level. We argue that the Discrete Event Simulation (DES) approach can be used to model such dynamics in Higher Education Academic Workforce Model (HEAWM) and show that it can provide a comprehensive projection of future requirements within the context of career progression. Consequently, this HEAWM allows universities to interrogate factors influencing the academic workforce planning as this process often requires the new attributes to be tracked in the model which is difficult with other OR models. It is shown that this approach is easy to apply via DES and creation of the corresponding HEAWM provides better understanding of the factors that will influence the future workforce than the existing results.

Keywords: Arena, academic workforce, discrete event simulation, dynamical systems, modelling


B. Efron and G. Gong, “A leisurely look at the bootstrap, the jackknife, and Cross validation,” Am. Stat., 37, pp. 36 – 48, 1983.

A. Soyiho, “Markov Chain application to academic manpower planning,” Vikalpa, 9, 1, pp. 27 – 41, 1984.

G. O. Ogbogbo, G. U. Ebuh and C. O. Aronu, “Prediction of academic manpower system of a Polytechnic institution in Nigeria, Science Journal of Applied Mathematics and Statistics, 1, 5, pp. 54 – 61, 2013.

M. Aref and M. Sabah, “Manpower Planning for Demand Forecasting of Faculty Members using Trend Analysis and Regression,” International Journal of Academic Research in Business and Social Sciences, 5, 2, pp. 11-23, 2015.

J. Wang, “A review of operations research applications in workforce planning and potential modelling of military training,” DSTO-TR-1688, 2005.

J. S. Edwards, “A Survey of Manpower Planning Models and Their Application,” Journal of Operational Research Society, 34, 11, pp. 1031-1040, 1983.

N. K. Jajo, “The trade-off between DES and SD in modelling military manpower,” Management Science Letters, 5, 4, pp. 369-376, doi: 10.5267/j.msl.2015.2.002, 2015.

A. A. Tako and S. Robinson, “Model development in discrete-event simulation and system dynamics: An empirical study of expert modellers,” European Journal of Operational Research, 207, pp. 784 – 794, 2010.

J. Morecroft and S. Robinson, “Explaining puzzling dynamics: Comparing the use of system dynamics and discrete event simulation,” Proceedings of the 23rd International Conference of the System Dynamics Society, The System Dynamics Society, Boston, USA, 2005

J. Morecroft and S. Robinson, “Comparing discrete event simulation and system dynamics: modelling a fishery,” Proceedings of the 2006 OR Society Simulation Workshop SW06, Leanington, Spa, UK, pp. 137 – 148, 2006.

A. A. Tako and S. Robinson, “Comparing discrete-event simulation and system dynamics: users’ perceptions,” Journal of the operational research society, 60, 3, pp. 296 -312, 2009.

K. Chahal and T. Eldabi, “A multi-perspective comparison for selection between system dynamics and discrete event simulation,” International Journal of Business Information Systems, 6, 1, pp. 4-17, 2010.

M. Tanha, D. Sajjadi and S. Shamala, “A discrete event simulator for extensive defense mechanism for denial of service attacks analysis,” American Journal of Applied Sciences, 9, 6, 909 – 916, 2012.

B. Milczarek, “Review of modelling approaches for healthcare simulation,” Operations Research and Decisions, 1, pp. 55-72, 2016.

S. C. Brailsford, S. M. Desai and J. Viana, “Towards the holy grail: combining system dynamics and discrete-event simulation in healthcare,” In Simulation Conference (WSC)}, Proceedings of the 2010 Winter, pp. 2293-2303, 2010.

G. S. Fishman, Discrete Event Simulation: Modeling, Programming and Analysis, New York: Springer-Verlag, 2001.

R. B. Detty and J. C. Yingling, “Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study,” International Journal of Production Research, 38, 2, 2000.

L. P. Baldwin, T. Eldabi and R. J. Paul, “Simulation in healthcare management: a soft approach (MAPIU),” Simulation Modelling Practice and Theory, 12, pp. 541-557, 2005.

S. H. Jacobson, S. N. Hall and J. R. Swisher, Discrete-Event Simulation of Health Care Systems. In: Hall R.W. (eds) Patient Flow: Reducing Delay in Healthcare Delivery. International Series in Operations Research & Management Science, USA Boston, MA: Springer, 2006.

M. M. Günal and M. Pidd, “Discrete event simulation for performance modelling in health care: a review of the literature,” Journal of Simulation, 4, 1, 2010.

B. Kaskie, M. Walker and M. Andersson, “Efforts to Address the Aging Academic Workforce: Assessing Progress Through a Three-Stage Model of Institutional Change,” Innovation High Education,42, pp. 225-237, 2017.

H. Flavell, L. Roberts, G. Fyfe and M. Broughton, “Shifting goal posts: the impact of academic workforce reshaping and the introduction of teaching academic roles on the Scholarship of Teaching and Learning,” Aust. Educ. Res.,45, pp. 179-194, 2018.

C. Whitchurch, “From a diversifying workforce to the rise of the itinerant academic,” High Educ., 77, pp. 679–694, 2019.

M. R. Chernick and R. A. LaBuddle, An Introduction to Bootstrap Methods with Applications to R, Hoboken, New Jersey: John Wiley & Sons, 2011.

G. Gong, “Cross - validation, the jackknife, and the bootstrap: Excess error in forward logistic regression,” J. Am. Statist. Assoc., 81, pp. 108 – 113, 1986.

K. P. Burnham and D. R. Anderson, Model selection and multimodal inference: a practical information-theoretic approach, New York: Springer, 2002.

M. Moffatt. (Aug. 27, 2020). An Introduction to Akaike's Information Criterion (AIC). https://www.thoughtco.com/introduction-to-akaikes-information-criterion-1145956.


How to Cite
Jajo, N. K., & Peiris, S. (2021). A study on Efficient Modelling in Higher Education Academic Workforce Using Simulation. European Journal of Mathematics and Statistics, 2(6), 7–14. https://doi.org/10.24018/ejmath.2021.2.6.63