Accurate and timely admission prediction in emergency departments is essential for improving resource allocation, enhancing patient outcomes, and mitigating overcrowding. However, the progression of emergency patients often exhibits strong temporal dynamics, and clinical decisions typically involve not only admission and non-admission but also an intermediate state of wait-and-see. To address this challenge, this study proposes a novel temporal three-way decision-making method that integrates Temporal Feature-based Multigranulation Neighborhood Rough Set (TMNRS) with Gaussian Mixture–Hidden Markov Model (GMM-HMM). Specifically, TMNRS is utilized to quantify and characterize the initial distribution of patient states from both theoretical and data-driven perspectives, thereby providing parameter support for subsequent modeling. Building on this foundation, GMM-HMM is employed to capture the dynamic evolution of patients’ conditions across three states over time. This integration facilitates interpretable state representation of the model. Finally, comprehensive experiments conducted on real-world clinical data, including comparisons with multiple benchmark models, demonstrate competitive and robust performance of the proposed approach in supporting temporal three-way admission decision-making for emergency patients.

We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0333 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0333 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

In this article, the issue of multicenter and single-area humanitarian relief allocation with uncertain travel time and imprecise transportation information is investigated. Expert human knowledge using fuzzy control is employed to select rescue paths, and the considered problem is formulated as a fuzzy chance-constrained model to ensure that the allocated goods can be delivered to the disaster area on time within a desired probability. A new method is presented to transform chance-constrained programming into a mixed-integer model utilizing triangle fuzzy numbers and the robust optimization problem. A practical example is used to test the validity of the theoretic results obtained.