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An uncertain workforce planning problem with job satisfaction

  • Guoqing Yang
  • Wansheng TangEmail author
  • Ruiqing Zhao
Original Article

Abstract

To investigate the effect of employees’ job satisfaction on the firm’s workforce planning, this paper builds a multi-period uncertain workforce planning model with job satisfaction level, where the labor demands and operation costs are characterized as uncertain variables. The job satisfaction level is defined as the employees’ psychological satisfaction about overtime through prospect theory. The proposed uncertain model can be transformed into an equivalent deterministic form, which contains complex nonlinear constraints and cannot be solved by conventional optimization methods. Thus, a hybrid joint operations algorithm (JOA) integrated with LINGO software is designed to solve the proposed workforce planning problem. Consequently, several numerical experiments are conducted to compare our proposed JOA with a hybrid particle swarm optimization algorithm to verify the effectiveness of the JOA algorithm. The results demonstrate that the firm’s total operation cost increases with the employees’ job satisfaction level, the loss averse degree and outside firms’ overtime level, respectively. Meanwhile, the firm would overpay in bounded rational cases with job satisfaction, and the overpayment can be seen as the value of bounded rationality, which ensures the firm’s normal operation.

Keywords

Workforce planning Prospect theory Job satisfaction Uncertainty theory Joint operations algorithm 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 71371133, and supported partially by Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20120032110071.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Systems EngineeringTianjin UniversityTianjinChina

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