Optimizing Manpower Planning: A Goal Programming Approach.

AuthorChowdhury, Arup Roy

Objective of the Study

To develop a manpower planning policy, vide Goal Programming approach for manpower projection and distribution of executives at entry, middle and senior manager levels in the organization. Accordingly, a Goal Programming Model will be developed by taking an illustration from a business unit of a manufacturing organization 'M' having three job classifications.

Literature Review

Goal Programming is a goal-oriented optimization technique which is aimed to solve problems having multiplicity of objectives in a decision- making skyline/perspective. It's a multiple criteria decision making (MCDM) technique. Wikipedia definition suggests that Goal Programming can be thought of as an extension/generalization of linear programming to handle multiple objective measures. Each of these measures is given a goal/target value to be attained. The unwanted deviations from this set of target values are then minimized in an achievement function. As satisfaction of the target is deemed to satisfy the decision maker(s), an underlying satisfying philosophy is assumed. Goal Programming is used for following analysis: (a) Determine the required resources to achieve a desired set of objectives; (b) Determine the degree of attainment of the goals with the available resources; (c) Providing the best satisfying solution under a varying amount of resources and priorities of the goals.

Goal Programming, was conceived by Charnes and Cooper (1961). Later on Ijiri (1965), Jaaskelanen (1969), Lee and Clayton (1970), Ignizio (1976), Gass (1986), Romero (1991), Tamiz and Jones (1996) extended and enhanced the tool. Since then researchers like Schniederjans and Hoffman (1992) have worked on the extensions of Goal Programming methodology such as preemptive/lexicographic Linear Goal Programming, Integer Goal Programming, Zeroone Goal Programming; Romero (2001) on extended Lexicographic Goal Programming; Lee (1972) on extensive surveys of fields and its applications; Schniederjans (1995), Tamiz et al. (1998) worked on production planning, financial planning, capital budgeting planning etc.

Baran et al (2013) formulated a Goal Programming model by using genetic algorithm to solve economic environmental electric power generation problem with interval valued target goals. Dean and Schniederjans (1990) applied a Goal Programming approach to production planning for flexible manufacturing systems. Ghosh et al (2005) formulated a Goal Programming in nutrient management for rice production in West Bengal.

Golany et al (1991) proposed a Goal Programming inventory control model which was being applied in a large chemical plant. The proposed model came out with an efficient solution which impacted the overall levels of decision -making satisfaction with the multiple fuzzy goal values.

Larbani and Aouni (2011) suggested a new approach for generating efficient solutions within the Goal Programming Model followed by the efficient test for the Goal Programming solution. Leung and Ng (2007) suggested a Goal Programming Model for production planning of perishable products. Mukherjee and Bera (1995) discussed the solution of a project selection by applying Goal Programming technique. Sen and Nandi (2012a) applied a Goal Programming approach to rubber plantation planning in Tripura. Sen and Nandi (2012b) formulated an optimal model by using Goal Programming for a rubber wood door manufacturing factory in Tripura. Sen and Nandi (2012c) reviewed the Goal Programming and its application in plantation management.

Sinha and Sen (2011) formulated a strategic planning by using the Goal Programming approach to maximize production quantity of tea, profit and demand and minimize expenditure and processing time in different machines installed in tea industry of Barak Valley of Assam in order to flourish the tea industries.

Tamiz et al (1996) formulated an exploration of linear and Goal Programming Models in the downstream oil industry. Leung and Chan (2009) developed a preemptive Goal Programming model for aggregate production planning problem with different operational constraints. Sharma (1995) studied lexicographic Goal Programming to solve a product mix problem in a large steel manufacturing unit.

Ghiani et al (2003) proposed a mixed integer linear Goal Programming Model for allocation of production batches to subcontractors through fuzzy set theory in an Italian textile company which resulted to outperform the hand-made solutions put to use by the management so far. Lee et al (1989) formulated industrial development policies by a zero-one Goal Programming approach.

Nja and Udofia (2009) formulated the mixed integer Goal Programming Model for flour producing companies. Pati et al. (2008) formulated mixed integer Goal Programming Model to assist in proper management of the paper recycling logistics system. Belmokaddem et al (2009) proposed a model to minimize total production and work force costs, carrying inventory costs and rates of changes in work force using fuzzy Goal Programming approach with different importance and priorities to aggregate production planning.

Fazlollahtabar et al (2013) formulated a fuzzy Goal Programming for optimizing service industry market using virtual intelligent agent. Mekidiche et al (2013) applied weighted additive fuzzy Goal Programming approach to aggregate production planning. Petrovic and Akoz (2008) proposed a fuzzy Goal Programming Model for solving the problem of loading and scheduling of a batch processing machine. Yimmee and Phruksaphanrat (2011) proposed a fuzzy Goal Programming Model for aggregate production and logistics planning to increase profit and reduce change of workforce level.

Case Study

In a business unit of a manufacturing organization 'M', there are three managerial levels--Entry level (X1), Middle level (X2) and Senior Level (X3). The various levels like U1 & U2, U3 & U4, U5 & U6 are categorized in X3, X2 and X1 respectively. The present distribution of executives in various levels are as in Table 1.

The manning policy of the organization is that executives either move from one job impact level to next higher level or stay at the same impact level, or leave the business unit/organization due to superannuation/ resignation.


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