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Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients' overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023.1210. [ABSTRACT FROM AUTHOR]

We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal location decision for a network of alternative fuel stations (AFS) servicing a new market where the demand rate for the refueling service can be learned over time. In the presence of demand learning, the firm needs to make a decision, whether to actively learn the market through a greater initial investment in the AFS network or defer the commitment since an overly-aggressive investment often results in sub-optimal AFS locations. To illustrate this trade-off, we introduce a two-stage location model, in which the service provider enters the market by deploying a set of stations in the first stage under uncertainty, and has the option to add more stations in the second stage after it learns the demand. The demand learning time (length of the first stage) is endogenously determined by the service provider's action in the first stage. To solve this problem, we develop an efficient solution method that provides a framework to achieve a desired error rate of accuracy in the optimal solution. Using numerical experiment, we study the trade-off between active learning and deferred commitment in AFS deployment strategy under different market characteristics. Further, we find that the lack of planning foresight typically results in an over-commitment in facility investment while the service provider earns a lower expected profit. [ABSTRACT FROM AUTHOR]