An uncertain workforce planning problem with job satisfaction

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...

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Vydáno v:International journal of machine learning and cybernetics Ročník 8; číslo 5; s. 1681 - 1693
Hlavní autoři: Yang, Guoqing, Tang, Wansheng, Zhao, Ruiqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Control
Costs
Design
Employees
Engineering
Integer programming
Job satisfaction
Labor market
Linear programming
Mechatronics
Nurses
Optimization
Original Article
Particle swarm optimization
Pattern Recognition
Random variables
Rationality
Robotics
Systems Biology
Work environment
Workforce
Workforce planning
Joint operations algorithm
Prospect theory
Job satisfaction
Workforce planning
Uncertainty theory
ISSN:1868-8071, 1868-808X
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Shrnutí: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.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-016-0539-6