DocumentCode
2549430
Title
Human resource planning under stochastic capacity demand — The case of data entry service providers
Author
Li, Hua ; Liu, Qiang ; Dong, Ming
Author_Institution
Sch. of Econ. & Manage., Xidian Univ., Xi´´an, China
fYear
2009
fDate
21-23 Oct. 2009
Firstpage
1349
Lastpage
1353
Abstract
There is always uncertainty associated with human resource planning that are caused by the uncertainties in outsourcing projects, resulting in lack of or surplus human resources in the production process. This paper draws upon the principles and methods used in manufacturing capacity planning, to build a human resource planning model in stochastic demands. The model takes cost as an objective function in stochastic conditions. The stochastic marketing constraint was built through random capacity requirement variables. Based on a two-phase model of stochastic linear constraint, the certainty equation of stochastic human resource planning model is obtained. An algorithm that integrates immune clonal selection algorithm (ICSA) with linear programming (LP) is proposed. Treating human resource variables as preconditions, the LP is used to find solutions to the problem of entry capacity distribution strategy. The solution to entry capacity distribution strategy is then used as affinity function for ICSA. Through immune gene and clonal selection operation, the human resource integral variables are searched until the optimum solution achieved.
Keywords
human resource management; linear programming; outsourcing; stochastic processes; data entry service providers; human resource integral variables; human resource planning; immune clonal selection algorithm; linear programming; outsourcing projects; stochastic capacity demand; stochastic linear constraint two-phase model; Capacity planning; Distribution strategy; Humans; Immune system; Outsourcing; Process planning; Production planning; Pulp manufacturing; Stochastic processes; Uncertainty; Human Resource Planning (HRP); Immune Clonal Selection Algorithm (ICSA); Stochastic Model;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Engineering and Engineering Management, 2009. IE&EM '09. 16th International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-3671-2
Electronic_ISBN
978-1-4244-3672-9
Type
conf
DOI
10.1109/ICIEEM.2009.5344430
Filename
5344430
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