# Discrete-Event Simulation

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arXiv Nov 6, 2026
ICU planning no longer rebuilds states per question: one frozen patient world model drives admission, care and discharge simulations
ICU resource planning has long relied on compact state-transition models hand-specified for a single policy question, so a new question means redefining states. A patient world model trained on MIMIC-IV and then frozen encodes irregular electronic health records as state-event sequences, returns predictions at any interval the simulator requests, and connects through scenario-specific mappings to shared bed and staff queues. Across admission, care-dispatch and discharge simulations it agrees with task-fitted Markov and semi-Markov comparators on directional responses to capacity and dispatch policies, and reproduces the dispatch gains obtained by replaying observed trajectories.
ICU resource planning has long relied on compact state-transition models hand-specified for a single policy question, so a new question means redefining states. A patient world model trained on MIMIC-IV and then frozen encodes irregular electronic health records as state-event sequences, returns predictions at any interval the simulator requests, and connects through scenario-specific mappings to shared bed and staff queues. Across admission, care-dispatch and discharge simulations it agrees with task-fitted Markov and semi-Markov comparators on directional responses to capacity and dispatch policies, and reproduces the dispatch gains obtained by replaying observed trajectories.
Yingtai Li（First author）
World Model Discrete-Event Simulation MIMIC-IV Clinical Prediction
researchsquare.com Oct 20, 2026
Emergency crowding gets a six-hour warning: proactive capacity protocols cut time over threshold by up to about a fifth in simulation
Full Capacity Protocols in emergency departments usually start only after crowding thresholds are crossed, yet hallway beds, temporary nurses and transfer restrictions take time to arrange. A discrete-event simulation evaluation embedded a machine learning model forecasting waiting-room counts six hours ahead into the emergency flow and used those forecasts to trigger the protocol early. Over a simulated year, early activation significantly shortened time above the crowding threshold and average protocol duration at all three threshold settings, while changes in average emergency length of stay and wait time were not statistically significant.
Full Capacity Protocols in emergency departments usually start only after crowding thresholds are crossed, yet hallway beds, temporary nurses and transfer restrictions take time to arrange. A discrete-event simulation evaluation embedded a machine learning model forecasting waiting-room counts six hours ahead into the emergency flow and used those forecasts to trigger the protocol early. Over a simulated year, early activation significantly shortened time above the crowding threshold and average protocol duration at all three threshold settings, while changes in average emergency length of stay and wait time were not statistically significant.
University of Alabama at Birmingham Emporia State University
khalid Aram（First author）Abdulaziz Ahmed（Corresponding author）
Full Capacity Protocol ML-guided proactive FCP activation Discrete-Event Simulation Support Vector Regression Emergency department overcrowding
Lecture notes in computer science Oct 17, 2026
Simulating stochastic hybrid systems no longer stops at discrete choices: discretized time intervals let Q-learning pick optimal schedulers on continuous models
Simulation of stochastic hybrid systems previously handled only discrete nondeterministic choices, while an action allowed to fire anywhere in a time interval gives a single state infinitely many options. The new method discretizes each action's time interval separately, learns a scheduler with Q-learning over the finite abstraction, and uses precomputed symbolic reachable states to steer simulation and prune unreachable regions in a correct-by-construction way. On the water tank benchmark the simulation reproduces the maximum reachability probabilities reported by an existing method; on a water tank variant with a low-probability underflow objective, guidance makes learning clearly faster than unguided simulation and yields a reachability probability of at least 45%.
Simulation of stochastic hybrid systems previously handled only discrete nondeterministic choices, while an action allowed to fire anywhere in a time interval gives a single state infinitely many options. The new method discretizes each action's time interval separately, learns a scheduler with Q-learning over the finite abstraction, and uses precomputed symbolic reachable states to steer simulation and prune unreachable regions in a correct-by-construction way. On the water tank benchmark the simulation reproduces the maximum reachability probabilities reported by an existing method; on a water tank variant with a low-probability underflow objective, guidance makes learning clearly faster than unguided simulation and yields a reachability probability of at least 45%.
University of Münster
J. Stubbe（First author, Corresponding author）
Q-learning Flowpipe Construction Discrete-Event Simulation Stochastic Hybrid Model Singular Automata with Random Clocks
arXiv Oct 14, 2026
No GPUs needed to plan LLM serving: BLIS simulates the full pipeline on CPU and holds a 300 ms TTFT SLO on four H100s
BLIS is a deterministic, CPU-only discrete-event simulator that models the whole LLM inference serving pipeline, including admission, routing, scheduling, prefill/decode execution, KV-cache management, and preemption, reaching 6.7% median end-to-end error across 36 experiments while running about 200 times faster than real execution.
IBM (United States)
Dipanwita Guhathakurta（First author）
BLIS Discrete-Event Simulation LLM Inference Inference Optimization KV Cache

You’re all caught up

ICU planning no longer rebuilds states per question: one frozen patient world model drives admission, care and discharge simulations

Emergency crowding gets a six-hour warning: proactive capacity protocols cut time over threshold by up to about a fifth in simulation

Simulating stochastic hybrid systems no longer stops at discrete choices: discretized time intervals let Q-learning pick optimal schedulers on continuous models

No GPUs needed to plan LLM serving: BLIS simulates the full pipeline on CPU and holds a 300 ms TTFT SLO on four H100s