The Influence of Visual Merchandising and Hedonic Motivation on Impulse Buying Through Shopping Enjoyment as an Intervening Variable


Imran Asghar Warraich , Dr. Wasif Ali Waseer ,

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Volume 6 - December 2017 (12)


Purpose: The main aim of the research is to develop a simulation model for manpower planning in organizations having pyramidal organizational structures.
Simulation modeling technique has been used to develop a manpower planning model. A conceptual stock and flow diagram was developed to formulate the transition probabilities and relationship between different grades. Furthermore, seven different equations of the model were developed for all grades of the workforce. In order to implement the model a software application based on the equations of the model was developed to assist HR managers to alter the factors (like accession, promotion and retirement rates) and to analyze the effects on the force growth over long term planning horizon. To test the model a hypothetical assumption was made regarding the desired structure and different scenario based simulations were generated and picked those that yielded the best results.Findings: The test results of the model were quite close to the assumed values of the desired structure that validated the effectiveness of the model.


The model will assist the HR managers of the public sector organizations to attain/maintain the desired strength of the workforce.


        i.            Bartholomew, J.D and Smith, R.A. (1988). Manpower planning in the United Kingdom:

      ii.            An historical review. Journal of the operational Research Society,39, 235-248.

    iii.            Bres, Burns, Charnes and Cooper. (1980). A goal programming model for planning officer accessions. Management Science 26 (1980) 773–783.

     iv.            Bayliss, C. Maere, De. G. Jason, A. D. Atkin, A.D.J. & Paelinck, M. (2017). A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty. Annals of operations research, May 2017, Volume 252, Issue 2, pp 335-363.

       v.            Bazargan, M., & Jiang, B. (2010). A simulation approach to airline maintenance manpower planning. Proceedings of the 2010 Summer Computer Simulation Conference, Pages 556-564.

     vi.            Cai, X., Li. Y. & Tu, F. (2004). Solving manpower planning problem with two types of jobs under uncertainty demand. International Journal of Pure and Applied Mathematics, Volume 17 No. 3 2004, 327-349.

   vii.            Cai, X., Laib, M. & Lib, Y. (2010). Stochastic Manpower Planning by Dynamic Programming. International Conference on Engineering Optimization, September 6-9, 2010, Lisbon, Portugal.

viii.Dimitriou, A.V., Georgiou, C.A., & Tsantas, N. (2013). The multivariate non-homogeneous Markov manpower system in a departmental mobility framework. European Journal of Operational Research 228 (2013) 112–121.

 ix.Dellacca, D. & Justice, C. (2007). Building Tomorrow's Information Assurance Workforce through Experiential Learning. Proceedings of the 40th Annual Hawaii International Conference on System Sciences, p.271c, January 03-06, 2007.

 x.Forrester, J.W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.


xi.Francesco, D.M., Llorente, D.M.N., Zanda, S., & Zuddas, P. (2016). An optimization model for the short-term manpower planning problem in transhipment container terminals. Computers & Industrial Engineering, 97 (2016) 183–190.

   xii.Georgiou, A. C.  & Tsantas, N. (2002). Modeling recruitment training in mathematical human resource planning. Applied Stochastic Models in Business and Industry, Volume 18, Issue 1, pages 53–74, January/March 2002.

 xiii.            Hafeez, K., & Abdelmeguid, H.  (2003). Dynamics of human resource and knowledge management. Journal of the Operations Research Society, No.2, Volume 54, Pages153-164.

 xiv.            Hu, W., Lavieriy, S. M., Toriello, A., & Liuy, X. (2013). Strategic Health Workforce Planning. University of Michigan.

 xv.            Jiang, H. (2013). A System Dynamics Model for Manpower and New Technology Implementation Trade-off and Cost Estimation. University of central Florida.

 xvi.Jessop, W.N. (1966). Manpower planning: operational research and personnel research. New York:  American Elsevier publishing company, inc.

xvii.            Kareem B., & Akande S.O., (2012). Knowledge-Graded Manpower Planning Model for the Manufacturing Industry. International Journal of Engineering Innovation and Management 2, (2012) 49.

xviii.            Khoong, C.M. (1996). An integrated system framework and analysis methodology for manpower planning. International Journal of Manpower, Vol. 17 Iss: 1, pp.26 – 46.

 xix.            Li Y., Chen J., Cai .X, Tu B. (2009). Optimal manpower planning decision with single employee type considering minimal employment period constraint, Asia Pacific. Journal of Operations.Res.27, 411(2010).

   xx.            Mohsen, A., Majd, K., Mahootchi, M., & Zakery, A. (2015). A reinforcement learning methodology for a human resource planning problem considering knowledge-based promotion. Simulation Modelling Practice and Theory.

 xxi.            Mutingi, M. (2012). System dynamics of manpower planning strategies under various demand scenarios. Management Science Letters, Volume 2, Issue 8 pp. 2689-2698, 2012

xxii.            Miller, III. R.J., Haire, M. (2006). Manplan: A micro-simulator for manpower planning. Behavioral Science, Volume 15, Issue 6, pages 524–531.

xxiii.            Nilakantan, K., & Raghavendra, B.G. (2005). Control aspects in proportionality Markov manpower systems. Applied Mathematical Modelling, Volume 29, Issue 1, January 2005, Pages 85 – 116.

xxiv.            Nilakantan. K, & Raghavendra.B. G.  (2008). Length of service and age characteristics in proportionality Markov manpower systems. IMA Journal of Management Mathematics, Volume 19, Issue 3 Pp.  245-268.

xxv.            Nilakantan, K., Sankaran, K.J. & Raghavendra B.G. (2011). A proportionality model of Markov manpower systems.  Journal of Modelling in Management, Vol. 6 No.1.

xxvi.            Noah, & Pin Z.Y. (2002). Managing Learning and Turnover in Employee Staffing. Operations Research, November/December 2002 vol. 50 no. 6 991-100.

xxvii.            Narahari, N.S., & Murthy, N. (2009). System Dynamic Modeling of Human Resource Planning for a typical IT organization. CURIE,2009, Vol,2, No.3.

xxviii.            Nirmala, S., & Jeeva, M. (2010). A dynamic programming approach to optimal manpower recruitment and promotion policies for the two grade system.  African Journal of Mathematics and Computer Science Research, Vol. 3 (12), pp. 297-301.

xxix.            Onggo, S., Pidd, M., Soopramanien, D., & Worthington, J.D. (2012). Behavioural modelling of career progression in the European Commission’, European Journal of Operational Research 222 (2012) 632–641.

xxx.            Parthasarathy, S., Ravichandran, M.K., & Vinoth, R. (2010). An application of stochastic models - grading system in Manpower Planning. International Business Research, Vol. 3, No. 2.

xxxi.            Parthasarathy, S., Vinoth, R., & Chitra, M. (2010) ‘Expected time to recruitment in single graded manpower system with threshold gamma distribution’, Asian Journal of Science and Technology, Vol. 1, pp.023-027.

xxxii.            Seal, H.L.  (1945). The mathematics of a population composed of K strata each recruited from the stratum below and supported at the lowest level by a uniform annual number of entrants. Biometrika 33, 226–230.

xxxiii.            Smith, A.R., & Bartholomew, D.J. (1988). Manpower Planning in the United Kingdom: An Historical Review. Journal of the Operational Research Socieo, Vol. 39(3), p. 235.

xxxiv.            Srinivasan, A.  Mariappan P. & Dhivya S., (2011). Stochastic models on time to recruitment in a two grade manpower system using different policies of recruitment.  Recent Research in Science and Technology, 3(4): 162-168.

xxxv.Topintzi, E. & Nistazakis M. (2004). Using System Dynamics to Analyze Human Resources Management Problem, Centre for Systems and Modeling. School of Engineering and Mathematical sciences, City University, Northampton Square, London EC1V OHB, page 11.

xxxvi.            Udom, A. U. & Uche P. I.  (2009). The use of time as an optimality performance criterion in manpower control. The Pacific Journal of Science and Technology, Volume 10.

xxxvii.Valevaa, S. Hewittb, M., Thomasc, W.B., & Brownd, G.K. (2017). Balancing flexibility and inventory in workforce planning with learning. International Journal of Production Economics,183 (2017), 194 - 207.

xxxviii.Ward, D., Bechet, T.P.  & Tripp, R. (1994). Human resource forecasting and modeling. New York:  The Human Resource Planning Society.

xxxix.            Wang, J. (2005). A Review of Operations Research, DSTO Systems Sciences Laboratory. Edinburgh Australia.

     xl.            Yang, T.I. & Chou, J.S. (2011). Multi-objective optimization for manpower assignment in consulting engineering firms. Applied Soft Computing, 11 (2011) 1183–1190.

    xli.            Yu, M. Ding, Y. Lindsey, R. & Shi, C. (2016). A data-driven approach to manpower planning at U.S.–Canada border crossings. Transportation Research Part A 91 (2016) 34–47.



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