Manpower Planning with Annualized Hours Flexibility: a Fuzzy Mathematical Programming Approach

Hasan, M.; Hasan, S. Suhaib

doi:10.7494/dmms.2016.10.1-2.5

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…Multi-objective programming is a robust analytical method for the formulation of real-world problems in which two or more than two goals have to be optimized, simultaneously. It has been favourably implemented for various applications in the field of engineering, economics, machine learning, manpower planning etc., see Ull Hasan et al [1], Hasan and Hasan [2], Hasan et al [3,4], Ashraf et al [5,6], Muhuri et al [7], Suttorp and Igel [8], Marler and Arora [9], Alexandropoulos et al [10], Bora et al [11], Breen et al [12], Kumar et al [13], Palacios [14], Nepomuceno [15], Caqo et al [16], Ashraf et al [17], Zheng et al [18], Yang and Chou [19].…”

Section: Introductionmentioning

confidence: 99%

“…, K represents the k-th objective function of decision variable X and S is the set of feasible solutions. Equation (2) represents the constraints of the optimization problem where A and b are the m × n and n × 1 dimensional matrices, respectively. Goal programming (GP) is a regularly used methodology to overcome the multi-objective programming problems.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Goal Programming with an Imprecise Intuitionistic Fuzzy Preference Relations

Ghaffar

Hasan²,

Ashraf

et al. 2020

Symmetry

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Fuzzy goal programming (FGP) is applied to solve fuzzy multi-objective optimization problems. In FGP, the weights are associated with fuzzy goals for the preference among them. However, the hierarchy within the fuzzy goals depends on several uncertain criteria, decided by experts, so the preference relations are not always easy to associate with weight. Therefore, the preference relations are provided by the decision-makers in terms of linguistic relationships, i.e., goal A is slightly or moderately or significantly more important than goal B. Due to the vagueness and ambiguity associated with the linguistic preference relations, intuitionistic fuzzy sets (IFSs) are most efficient and suitable to handle them. Thus, in this paper, a new fuzzy goal programming with intuitionistic fuzzy preference relations (FGP-IFPR) approach is proposed. In the proposed FGP-IFPR model, an achievement function has been developed via the convex combination of the sum of individual grades of fuzzy objectives and amount of the score function of IFPRs among the fuzzy goals. As an extension, we presented the linear and non-linear, namely, exponential and hyperbolic functions for the intuitionistic fuzzy preference relations (IFPRs). A study has been made to compare and analyze the three FGP-IFPR models with intuitionistic fuzzy linear, exponential, and hyperbolic membership and non-membership functions. For solving all three FGP-IFPR models, the solution approach is developed that established the corresponding crisp formulations, and the optimal solution are obtained. The validations of the proposed FGP-IFPR models have been presented with an experimental investigation of a numerical problem and a banking financial statement problem. A newly developed distance measure is applied to compare the efficiency of proposed models. The minimum value of the distance function represents a better and efficient model. Finally, it has been found that for the first illustrative problem considered, the exponential FGP-IFPR model performs best, whereas for the second problem, the hyperbolic FGP-IFPR model performs best and the linear FGP-IFPR model shows worst in both cases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Goal Programming with an Imprecise Intuitionistic Fuzzy Preference Relations

Ghaffar

Hasan²,

Ashraf

et al. 2020

Symmetry

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Solving a staffing problem with annualized hours, multiskilling with 2-chaining, and overtime: A retail industry case

Porto

Henao

Lusa

et al. 2022

Computers & Industrial Engineering

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Optimal manpower recruitment and promotion policies for the finitely graded systems using dynamic programming

Assi

Effanga

2021

Heliyon

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Background: At the heart in the development of any organization or nation is human resource. Over the years the world over, a sharp increase in too many numbers of qualified persons is being experienced yearly. This has impacted on recruitment and promotion costs to increase immensely, thereby affecting negatively manpower system costs. Dynamic Programming (DP) approach to optimal manpower recruitment and promotion policies for the two grade system has been proposed. Methods: Considering the fact that contemporary manpower systems are not limited to just two grades -a kind of "switch-approach" to manpower systems, we first establish the link between a manpower planning problem and a dynamic decision-making process. This linkage resulted to a multistage real-life decision-making problem whose solution demands that decisions be made sequentially at different levels and at different points in time and space. Dynamic Programming is a mathematical technique well appropriate for the optimization of multistage decision problems. This allows us to give a generalization to manpower systems by modifying the model to finite grades which came out to be more robust and actionable, a constrained deterministic Dynamic Programming (CDDP) found to function computationally as the very well-known Wagner-Whitin Model in inventory/production management. Five cost variables associated with manpower planning were identified and used as inputs to the modified deterministic DP model. Results/Relevance: The data shows yearly recruitments and promotions totaling 507 and 266 staff respectively for a ten-period (year) planning horizon. Total manpower system cost (in ooo's of Nigerian Naira) occasioned by yearly recruitments and promotions exercises for the period is 11334 (7092 for recruiting, 4100 for promoting, and 142 for overstaffing). Our DP model minimizes the manpower system cost to 9462 making a significant reduction of 1872. The optimal policy for the planning period calls for recruiting and promoting respectively 79 and 41 in period 1 only, 86 and 24 in period 2 for periods 2 and 3, 86 and 46 in period 4 for periods 4 and 5, 89 and 29 in period 6 only, 85 and 70 in period 7 for periods 7 and 8, and 82 and 56 in period 9 for periods 9 and 10. The study will contribute to the growing literature on applications of OR models to problems in manpower planning. The model outcomes would provide the basis for evaluating decision policies aimed at conducting recruitment promptly and to eliminate over-stagnated promotions. Conclusion/Further research:We formulate decisions making in a finitely-graded manpower system as a multistage decision-making optimization problem which are best handled by dynamic programming. Five cost variables associated with manpower planning were identified and used as inputs to the modified deterministic DP model. Our model is resolute for minimizing manpower system costs occasioned by recruitments and promotions exercises in a wide range of multi-graded manpower systems instead of just two grades. The study's limit...

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Manpower Planning with Annualized Hours Flexibility: a Fuzzy Mathematical Programming Approach

Cited by 3 publications

References 43 publications

Fuzzy Goal Programming with an Imprecise Intuitionistic Fuzzy Preference Relations

Fuzzy Goal Programming with an Imprecise Intuitionistic Fuzzy Preference Relations

Solving a staffing problem with annualized hours, multiskilling with 2-chaining, and overtime: A retail industry case

Optimal manpower recruitment and promotion policies for the finitely graded systems using dynamic programming

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