Operations & Supply Chain Management

Lori Qingyuan Yue and Jue Wang reflect on their article, “Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft Systems (UAS) Industry,” which was recently published in Business & Society.

Problem definition: Motivated by ride-hailing platforms such as Uber, Lyft and Didi, we study the problem of matching riders with self-interested drivers over a spatial network.

There is a consensus that global value chains have aided developing countries' growth. This essay highlights the governance complexities arising from participating in such chains, drawing from lessons we have learned conducting research in the coffee and garment supply chains. Market power of international buyers can lead to inefficiently low wages, prices, quality standards, and poor working conditions. At the same time, some degree of market power might be needed to sustain long-term supply relationships that are beneficial in a world with incomplete contracts.

In the landscape of low carbon sources of energy, nuclear power stands out as an option that has generated increasing attention and capital investment. Since 2001, nuclear energy production has been in decline, though Clean Core Thorium Energy, led by Mehul Shah, is one of the companies that is bringing nuclear back to the forefront. Founded in 2017, Clean Core is a nascent player in an industry that has a poor reputation among investors.

Should an incumbent metal manufacturing company make the change to green steel and how should it address its business model to be successful in its move to carbon neutrality?

We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which, at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action.

Emergency Departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8-12-hour shifts, providing partial flexibility in adjusting staffing levels.