Short-Term Simulation Approach

• Short-term simulation approach is defined as a method that uses real-time event logs to reinitialize simulation from the current state, enabling near-future system forecasts.
• It reconstructs process states—including ongoing cases, resource allocations, and task timings—using techniques like n-gram replay to capture concurrent and bursty behaviors.
• The approach supports real-time decision making by providing accurate operational forecasts and performance metrics that address concept drift and dynamic workloads.

A short-term simulation approach refers to the use of data-driven or algorithmic techniques to predict, analyze, or evaluate system behavior over an immediate or operationally relevant time horizon, explicitly starting from the current state of the system rather than a reset or empty initial condition. This paradigm is particularly relevant in organizational, medical, industrial, and engineering contexts where operational decisions depend on reliable forecasts of near-future process performance, workload, or risk, incorporating ongoing cases, dynamic resources, and exogenous changes. The approach sharply contrasts with classical steady-state or warm-up simulations that seek long-term averages or canonical stationary distributions. Recent research formalizes short-term simulation not simply as truncated long-term simulation, but as an explicit initialization and roll-forward from a contemporaneous, event-log-derived, and resource-marked system state, thereby enabling accurate short-horizon forecasts in the presence of drift, variance, and burstiness (Avramenko et al., 9 Sep 2025).

1. Formalization and Rationale

A short-term simulation approach in business processes is defined as a procedure to simulate system performance over a forecast window $[\tau_{st}, \tau_{st} + h]$, explicitly conditioned on the current process state at time $\tau_{st}$ rather than an artificial empty or equilibrium initial state. The state at $\tau_{st}$ is represented by

$$\Xi_{\tau_{st}} = \left\{ (\varphi, arr_{\varphi}, F_{\varphi}, A_{\varphi}, en_{\varphi}, st_{\varphi}, res_{\varphi}) \mid \varphi \in \Phi \right\}$$

where $\varphi$ denotes an ongoing case, $arr_{\varphi}$ its first arrival, $F_{\varphi}$ the current marking (set of tokens or control-flow position), $A_{\varphi}$ the set of activities in progress, $en_{\varphi}$ the enablement timestamp function, $st_{\varphi}$ the (possibly partially completed) activity start times, and $res_{\varphi}$ the resource assignments. This state is not otherwise available from model parameters alone but must be reconstructed from a structured event log recording the recent history of all $|\Phi|$ cases.

The motivation for such initialization is multi-fold:

• Capturing the true system workload (ongoing cases and their exact locations, resource assignments, elapsed durations) at $\tau_{st}$ enables forecasts that reflect operationally relevant volatility, non-stationarity, and temporary disruptions (e.g., concept drift, resource outages).
• Unlike warm-up based simulations which run long-term simulations up to a workload “similar” to the real one, direct current-state initialization guarantees no mismatch between simulated and real workloads at the forecast start.
• Short-term simulation is critical for operational (as opposed to strategic or tactical) decision making, where time-sensitive interventions (e.g., staff reallocation or rerouting) are required.

2. Event Log Extraction and State Reconstruction

The current system state required for short-term simulation initialization cannot be inferred from static process models. Instead, the event log—recorded as a sequence of events containing case identifiers, activity labels, timestamps (start and possibly end), and resource allocations—serves as the canonical data source. Key technical procedures include:

• Replay of trace-prefixes to reconstruct the control-flow marking (token position) of every ongoing case, using algorithms such as n-gram index techniques for mapping the last $m$ events in the trace to possible markings (especially in the presence of parallelism and loops).
• Extraction of enablement times, by locating in the log when each activity or event became “ready” for execution.
• Identification of in-progress activities (activities for which a start event exists in the log but not a completion event at $\tau_{st}$), and association with their resources.
• Calculation of the elapsed processing time for ongoing activities and the elapsed waiting time for enabled, yet-unstarted activities.

This explicit reconstruction ensures the simulated system state matches the real workload, including the handling of concurrency, resource contention, and elapsed durations at the forecast horizon start.

3. Simulation Model Augmentation and Requirements

Short-term simulation approaches impose substantial requirements on the underlying simulation model. Beyond a canonical process/workflow description, the Business Process Simulation Model (BPSM) must support reinitialization from arbitrary (possibly high-dimensional, partially completed) states. Formally, the BPSM comprises:

$BPSM = (W, R, D, P, T, I)$

where:

• $W$ is the workflow model (WF-graph),
• $R$ is the resource set (with concurrent capacities and attributes),
• $D$ is the task processing time distribution mapping,
• $P$ assigns probability to control-flow branches,
• $T$ specifies waiting time distributions for events,
• $I$ characterizes new case inter-arrival times.

In addition, the engine must accept as input the full short-term state $\Xi_{\tau_{st}}$ detailing the marking, enablement, in-progress activities, elapsed durations, resource busy/idle status, and arrival positioning for each ongoing or scheduled case. The simulation initialization algorithm then sets up event queues, resource locks, and completion timers in accordance with this reconstructed state before resuming event-driven simulation for the forecast period.

4. Forecasting Performance and Comparative Evaluation

Empirical evaluation demonstrates that short-term simulation approaches significantly outperform warm-up based (long-term) simulations in reproducing observable process realities on short horizons. Performance metrics include:

Metric	Definition	Behavior in Short-Term Simulation
Ongoing Cases Difference	$|n_{sim, t} - n_{real, t}|$; difference in number of active cases	Closer tracking of real workload
N-gram Distance (NGD)	Distance between simulated and log-derived n-gram control-flow sequences	Lower in short-term approach
Remaining Cycle Time Dist.	Distributional difference in predicted vs. real remaining cycle times for open cases	Lower, especially in burst/drift

In both synthetic and real-world log experiments under conditions of burstiness and concept drift (workload spikes, rapidly evolving distributions), initializing simulation from the extracted current state yields more accurate ongoing-case counts, control-flow sequence coverage, and remaining cycle time predictions than simulations relying on a warmed-up long-run state (Avramenko et al., 9 Sep 2025).

5. Adaptivity to Dynamics: Concept Drift and Bursty Patterns

A central advantage of the current-state short-term simulation is its inherent adaptivity to dynamic workloads and process drift. Specifically:

• By grounding initialization on real-time event logs, the approach reacts instantly to burst patterns or exogenous shocks (e.g., sudden resource shortages, special campaigns) without requiring lengthy simulation warm-up.
• The use of n-gram-based control-flow replay and concurrency oracles enables accurate marking inference even when process traces include loops, concurrent branches, or previously unseen behavioral deviations.
• The method naturally accommodates process concept drift, since time $t$ is always aligned with the observed (possibly non-stationary) workload, and parameter recalibration (e.g., for processing times or routing probabilities) can be performed on the recent log fragment, rather than on “historic” averages.

This adaptivity is especially valuable in domains such as service operations, where workload intensity and mix fluctuate significantly over diurnal, weekly, or event-driven cycles.

6. Practical Business Implications

The operational utility of the short-term simulation approach is multifaceted:

• It enables accurate, actionable operational forecasts (cycle times, Work-In-Progress, resource utilization) over short horizons, grounded in the actual state of work rather than statistical steady-state averages.
• Such forecasts support real-time or near-real-time decision support under transient disturbances—enabling interventions such as workload redistribution, ad-hoc resourcing, and targeted escalation.
• The simulation can serve as the basis for real-time “what-if” analysis, by reinitializing either from the as-is state for current forecast or with modified model parameters/hypothetical changes for scenario exploration, all while retaining the correct context of current workload, marking, and partial completions.
• The approach’s superior alignment with current workload also improves the accuracy of performance guarantee monitoring (e.g., SLAs), exception handling, and process compliance measurement.

7. Limitations and Extensions

While initialization from current state represents a major methodological advance, some limitations and technical issues remain:

• Reliable state reconstruction depends on the fidelity of the event log; missing or delayed events can introduce errors in marking or resource status inference.
• The n-gram replay technique must account for the process’ control-flow complexity; in high-concurrency or highly variable environments, ambiguities may remain.
• The approach assumes that future process behavior (post-initialization) remains governed by stationary model parameters (e.g., task duration distributions), although these may themselves be drifting.
• Cases with extremely long or outlier delays may cause the short-term simulation to diverge from ground truth, highlighting the need for enhanced long-tail modeling (as noted in experimental results).

Potential extensions include dynamic parameter estimation from recent log windows, hybridization with discriminative or reinforcement learning models for greater accuracy in settings marked by extreme process variability, and tighter integration of resource calendars and skill constraints into the initialization phase.

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This comprehensive treatment synthesizes the state, log extraction, modeling, evaluation, adaptivity, and practical import of short-term simulation approaches in contemporary business process and performance forecasting contexts (Avramenko et al., 9 Sep 2025).