Operations & Supply Chain Management

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.

Background

Equitable protocols to triage life-saving resources must be specified prior to shortages in order to promote transparency, trust and consistency. How well proposed utilitarian protocols perform to maximize lives saved is unknown. We aimed to estimate the survival rates that would be associated with implementation of the New York State 2015 guidelines for ventilator triage, and to compare them to a first-come-first-served triage method.

Methods

Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now.

When an inventory manager attempts to construct probabilistic models of demand based on past data, demand samples are almost never available: only sales data can be used. This limitation, referred to as demand censoring, introduces an exploration-exploitation trade-off as the ordering decisions impact the information collected. Much of the literature has sought to understand how operational decisions should be modified to incorporate this trade-off. We ask an even more basic question: when does the exploration-exploitation trade-off matter?

Pricing is central to many industries and academic disciplines ranging from Operations Research to Computer Science and Economics. In the present paper, we study data-driven optimal pricing in low informational environments. We analyze the following fundamental problem: how should a decision-maker optimally price based on a single sample of the willingness-to-pay (WTP) of customers. The decision-maker's objective is to select a general pricing policy with maximum competitive ratio when the WTP distribution is only known to belong to some broad set.

We study the classical newsvendor problem in which the decision-maker must trade-off underage and overage costs. In contrast to the typical setting, we assume that the decision-maker does not know the underlying distribution driving uncertainty but has only access to historical data. In turn, the key questions are how to map existing data to a decision and what type of performance to expect as a function of the data size.

Using high-frequency transaction-level income, spending, balances, and credit limits data from an online financial service, we show that many consumers fail to stick to their self-set debt paydown plans and argue that this behavior is best explained by a model of present bias. Theoretically, we show that (i) a present-biased agent's sensitivity of consumption spending to paycheck receipt reflects his or her short-run impatience and that (ii) this sensitivity varies with available resources only for agents who are aware (sophisticated) rather than unaware (naive) of their future impatience.