A mixed-integer model for skill development in a multi-skilled workforce

Abstract

Workforce planning is of strategic importance to most organisations. It is in particular challenging when tasks may require employees with specialised skills and various training activities may be proposed. In such environments, the value of dynamic skill development within a workforce cannot be ignored. We are in particular interested in increasing our understanding of how multiple skills and their development should be distributed among a pool of technicians, and how this may depend on the operational environment.

This research develops a novel approach based on mixed integer linear programming for determining an optimal strategic plan of skill development of a multi-skilled workforce when there are multiple training regimes that may be selected and various constraints on the operations of the training. For instance,additional constraints are required for skill development given uncertainty in the future demand for each type of task.

In this thesis we focus on the development of the model and its application in organisations through the development of a decision support tool. This tool provides training recommendation for employees under different training options. Each model is analysed to determine the impact of the different policies on the resultant skill gap and the run time of the model.

It is determined that the solution of these models cannot be found where the solver has difficulties proving optimality. Thus, a heuristic approach is recommended to approximate the solution. Both the exact and heuristic method are applied to a case study at Boeing for a complex maintenance line operated by a multi-skilled workforce. The problem calls for determining effective and efficient strategies for training and operational allocation of technicians.

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