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 macro-level measures (such as minimizing total system time or minimizing total number of tardy customers) and the atypical micro-level measure of reducing the incidents of excessively long waits at any workstation within the process. While work-conserving policies are optimal for macro-level measures, scheduling policies with strategic idleness (SI) might be helpful for micro-level measures. Using the empirical data obtained from the service provider, we provide statistical evidence that SI is used by its schedulers to manage the macroand micro-level 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 DSP and show that the resulting policies provide an efficient way to simultaneously address both macroand micro-level 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.