Labor Optimization Under Uncertainty Of Workers’ Absenteeism And Workforce Operation & Management System Design

Abstract

Workforce planning is a major concern in manufacturing business for running the plants smoothly and efficiently, particularly with the challenge of workers’ absenteeism. Unforeseen labor shortage because of the worker absenteeism severely hinders the maintenance of production lines and negatively impact product quality. Business managers often resort to over-hiring; however, in the long run, overstaffing leads to labor waste and excessive cost. This research project designs a workforce operations and management system, with the aim to most efficiently utilize labor effort to meet the production demand under the uncertainty of workers’ absenteeism. It accomplishes the following tasks: (i) provides an optimal policy of daily labor force assignment; (ii) recommends a cross-training strategy to improve employees’ versatilities; (iii) makes long-term workforce planning to find an optimal quantity of employees for minimizing staff cost and controlling the risk that staff headcounts cannot meet production demand duo to absenteeism. Task (i) is achieved by developing linear programming assignment and adjustment models. The assignment model allocates show-up employees to appropriate jobs considering their individual skills and preferences, whereas the adjustment model optimally and dynamically adjusts job assignment on account of employees’ coming late or leaving early. In Task (ii), a two-stage stochastic cross-training optimization model is proposed to select trainees and decide which jobs they should be trained for. Task (iii) incorporates the models developed in Tasks (i) and (ii) in a dynamic optimization model to decide staffing levels for a long-time horizon with the help of the prediction of absenteeism rate.