Performance improvements through flexible workforce

This thesis focuses on increasing the efficiency of systems with cross-trained workforce and finite storage spaces. Our objective is to maximize the throughput and minimize the setup costs (if they exist). More specifically, we are interested in determining effective cross-training strategies and dynamic server assignment policies for flexible servers in production lines with finite buffers. In the first part of this thesis, we study non-Markovian systems and support the conjecture that effective server assignment policies are robust to service time distributions. Next, we consider understaffed tandem lines with partially or fully flexible servers, determine optimal and heuristic server assignment policies, and show that most of the benefits of full flexibility can be achieved with limited flexibility. Finally, we incorporate the setups to our model, determine the optimal server assignment policy for some systems and show how the effective assignment of servers depends on the magnitude of the setup costs.