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Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analytical approximation of the true value of input uncertainty.

Input models that drive stochastic simulations are often estimated from real-world samples of data. This leads to uncertainty in the input models that propagates through to the simulation outputs. Input uncertainty typically refers to the variance of the output performance measure due to the estimated input models. Many methods exist for quantifying input uncertainty when the performance measure is the sample mean of the simulation outputs, however quantiles that are frequently used to evaluate simulation output risk cannot be incorporated into this framework. Here we adapt two input uncertainty quantification techniques for when the performance measure is a quantile of the simulation outputs rather than the sample mean. We implement the methods on two examples and show that both methods accurately estimate an analytical approximation of the true value of input uncertainty.

The 2020 Congress of the International Society of Thrombosis and Haemostasis (ISTH) was held virtually July 12-15, 2019, due to the coronavirus disease 2019 pandemic. The congress convenes annually to discuss clinical and basic topics in hemostasis and thrombosis. Each year, the program includes State of Art (SOA) lectures given by prominent scientists. Presenters are asked to create Illustrated Capsules of their talks, which are concise illustrations with minimal explanatory text. Capsules cover major themes of the presentation, and these undergo formal peer review for inclusion in this article. Owing to the shift to a virtual congress this year, organizers reduced the program size. There were 39 SOA lectures virtually presented, and 29 capsules (9 from talks omitted from the virtual congress) were both submitted and successful in peer review, and are included in this article. Topics include the roles of the hemostatic system in inflammation, infection, immunity, and cancer, platelet function and signaling, platelet function disorders, megakaryocyte biology, hemophilia including gene therapy, phenotype tests in hemostasis, von Willebrand factor, anticoagulant factor V, computational driven discovery, endothelium, clinical and basic aspects of thrombotic microangiopathies, fibrinolysis and thrombolysis, antithrombotics in pediatrics, direct oral anticoagulant management, and thrombosis and hemostasis in pregnancy. Capsule authors invite virtual congress attendees to refer to these capsules during the live presentations and participate on Twitter in discussion. Research and Practice in Haemostasis and Thrombosis will release 2 tweets from @RPTHJournal during each presentation, using #IllustratedReview, #CoagCapsule and #ISTH2020. Readers are also welcome to utilize capsules for teaching and ongoing education.

This paper studies joint decisions on pricing and empty container repositioning in two- depot shipping services with stochastic shipping demand. We formulate the problem as a stochastic dynamic programming (DP) model. The exact DP may have a high-dimensional state space due to in-transit containers. To cope with the curse of dimensionality, we develop an approximate model where the number of in-transit containers on each vessel is approxi- mated with a fixed container flow predetermined by solving a static version of the problem. Moreover, we show that the approximate value function is L♮-concave, thereby characterizing the structure of the optimal control policy for the approximate model. With the upper bound obtained by solving the information relaxation-based dual of the exact DP, we numerically show that the control policies generated from our approximate model are close to optimal when transit times span multiple periods.