Strategic Idleness and Dynamic Scheduling in an Open-Shop Service Network: Case Study and Analysis

This paper, motivated by a collaboration with a healthcare service provider, focuses on stochastic open-shop service networks with two objectives: more traditional macrolevel measures (such as minimizing total system time or minimizing total number of tardy customers) and the atypical microlevel measure of reducing the incidents of excessively long waits at any workstation within the process. While work-conserving policies are optimal for macrolevel measures, scheduling policies with strategic idleness (SI) might be helpful for microlevel measures. Using the empirical data obtained from the service provider, we provide statistical evidence that SI is used by its schedulers to manage the macro- and microlevel measures. However, the company has no specific rules on implementing SI and the schedulers make decisions based on their own experience. Our primary goal is to develop a systematic framework for the joint usage of SI with dynamic scheduling policies (DSPs). We suggest to use threshold-based policies to intelligently combine SI and DSPs and show that the resulting policies provide an efficient way to simultaneously address both macro- and microlevel measures. We build two simulation models: one based on empirical data and one based on a randomly generated open-shop network. We use both models to demonstrate that an open-shop service network can be systematically and effectively managed to deliver improved service level by using SI.