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Optimal manpower recruitment by stochastic programming in graded manpower systems

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Abstract

Human resource is one of the most effective resources in the development of any organization or a Nation. Optimal utilization of manpower is a pivotal challenge to a manager of a dynamic management system. Achieving the objectives by meeting the constraints on various feasibilities is the core problem before statisticians, model developers and OR scientists. In this paper we develop a model with Stochastic Programming Problem of manpower recruitment for an organization where the graded manpower system is observed. The averages, variance of number of employees in initial and final grades before leaving/resigning organization are computed by a suitable bivariate probability mass function. The costs like setup cost, recruitment cost, salaries, Cost of Training, Promotion Costs etc., are considered while developing a model. Sensitivity analysis was also carried out and observed the model behaviour. This model is a suitable tool to serve the decision maker for making optimal decisions on Recruitment policies for determining the optimal recruitment cost, to decide number of employees to be recruited through various grades, to estimate the number of employees for Promotion and to Retirement etc.

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Authors and Affiliations

  1. Department of Statistics, S.V. University, Tirupati, 517 502, India

    P. Tirupathi Rao, P. R. S. Reddy & A. V. S. Suhasini

Authors
  1. P. Tirupathi Rao
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  2. P. R. S. Reddy
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  3. A. V. S. Suhasini
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Correspondence to P. R. S. Reddy.

Appendices

Appendix 1

See Table 1.

Table 1 The decision parameters Lambda and Delta; the optimum cost for various values of alpha, beta, gamma, N0, M0
Full size table

Appendix 2

2.1 Inputs of manpower requirements

See Figs. 1 and 2.

Fig. 1
figure 1

For the values of s1 = 15000; s2 = 25000; S = 7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; M0 = 110, α = 5; β = 10; γ = 25

Full size image
Fig. 2
figure 2

For the Values of s1 = 15000; s2 = 25000; S = 7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; α = 5; β = 10; γ = 25

Full size image

2.2 Inputs of decision parameters

See Figs. 3, 4 and 5.

Fig. 3
figure 3

For the Values of s1 = 15000; s2 = 25000; S =7000000; p1 = 7000; p2 =10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; M0 = 110, β = 10; γ = 25

Full size image
Fig. 4
figure 4

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; M0 = 110, α = 5; γ = 25

Full size image
Fig. 5
figure 5

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; M0 = 110, α = 5; β = 10

Full size image

2.3 Inputs of manpower requirements

See Figs. 6 and 7.

Fig. 6
figure 6

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; M0 = 110, α = 5; β = 10; γ = 25

Full size image
Fig. 7
figure 7

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; α = 5; β = 10; γ = 25

Full size image

2.4 Inputs of decision parameters

See Figs. 8, 9 and 10.

Fig. 8
figure 8

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; β = 10; γ = 25

Full size image
Fig. 9
figure 9

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; α = 5; γ = 25

Full size image
Fig. 10
figure 10

For the Values of s1 = 15000; s2 =25000; S =7000000; p1 = 7000; p2 = 10000; P = 300000; r1 = 500; r2 = 800; R = 100000; N0 = 150; α = 5; β = 10

Full size image

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Rao, P.T., Reddy, P.R.S. & Suhasini, A.V.S. Optimal manpower recruitment by stochastic programming in graded manpower systems. Int J Syst Assur Eng Manag 1, 178–185 (2010). https://doi.org/10.1007/s13198-010-0016-7

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