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09-05-2016 | Original Article | Issue 5/2017

International Journal of Machine Learning and Cybernetics 5/2017

An uncertain workforce planning problem with job satisfaction

Journal:
International Journal of Machine Learning and Cybernetics > Issue 5/2017
Authors:
Guoqing Yang, Wansheng Tang, Ruiqing Zhao

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.

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