| Section | Typical Content | Tips & Tricks | |---------|----------------|---------------| | | • Report title (e.g., “Discrete‑Event Simulation of a Hospital Emergency Department Using Arena”) • Your name, ID, course, professor, date | Use a clear, descriptive title; avoid generic “Simulation Project.” | | Executive Summary / Abstract (150‑250 words) | • Problem context • Key objectives • Main findings (e.g., “average patient wait reduced by 22 %”) • Primary recommendation | Write this last ; it should be readable on its own. | | Table of Contents | Auto‑generated in Word/LaTeX | Include page numbers for each major heading. | | 1. Introduction | • Background (why the system matters) • Scope & limits of the study • Research questions / performance measures | Keep it concise; cite any real‑world data sources. | | 2. Literature Review (optional but recommended) | • Prior simulation studies of similar systems • Theoretical foundations (e.g., queuing theory) | Shows you understand the state of the art; limit to 2‑3 key references. | | 3. Model Description | • 3.1 System Overview (process flow diagram) • 3.2 Assumptions (e.g., “inter‑arrival times are exponential”) • 3.3 Arena Implementation – screenshots of the Model Window , Entity , Resource , Queue , Logic modules, and any sub‑models • 3.4 Input Data – tables of distributions, sources, and any calibration steps | Use high‑resolution screenshots (≥300 dpi) and label each component. Add a process‑flow chart (draw.io, Visio) before the Arena screenshot for readability. | | 4. Verification & Validation | • Verification – logic checks, trace runs, dead‑lock detection • Validation – compare model output to real system data (e.g., average wait time) • Statistical tests (e.g., two‑sample t‑test) with confidence intervals | Show a validation table : “Metric – Real System vs. Model – % Error.” | | 5. Experiment Design | • Run length , warm‑up period , number of replications , confidence level (e.g., 95 %) • Design of Experiments (DOE) – factorial, Taguchi, or one‑factor‑at‑a‑time • What‑if scenarios (e.g., “Add a second triage nurse”) | Provide a design matrix (Excel screenshot) and explain why you chose the number of replications (e.g., target half‑width ≤ 5 % of the mean). | | 6. Results | • Descriptive statistics (mean, std., 95 % CI) for each performance measure • Graphs – histograms, box‑plots, time‑series, comparative bar charts • Scenario comparison – tables showing % change vs. baseline | Use consistent colors and label axes with units. Export plots from Arena as EMF or PNG and embed them directly (not as screenshots of the screen). | | 7. Analysis & Discussion | • Interpretation of results (why did wait time drop?) • Sensitivity analysis (which input variables drive output variance?) • Limitations of the model (e.g., “no pre‑emptive priority”) | Reference the output analysis chapter of Simulation Modeling and Analysis (Law & Kelton) for statistical language. | | 8. Recommendations | • Practical actions for the real system (e.g., “Hire one additional nurse during peak hours”) • Suggested further studies (e.g., “Incorporate patient acuity levels”) | Tie each recommendation back to a specific performance metric. | | 9. Conclusions | • Recap the main findings in 2‑3 sentences • Emphasize the value of the simulation approach | Keep it short; avoid new data. | | 10. Appendices | • Full Arena model file listing (or a hyperlink if using a repository) • Detailed input tables • Full statistical output (ANOVA tables, confidence‑interval calculations) • Code snippets (if you used VBA, Simul8, or Python to post‑process) | Label each appendix (A, B, C…) and refer to them in the text. | | References | • Textbooks (e.g., Law & Kelton, 2022) • Journal articles • Arena User Manual (v15.0) • Any data sources | Use APA, IEEE, or the style required by your department . | 3. Formatting & Presentation Tips | Aspect | Recommendation | |--------|----------------| | Page layout | 1‑in. margins, 12‑pt Times New Roman (or Arial), 1.5 line spacing, page numbers bottom‑center. | | Figures & Tables | Number sequentially (Figure 1, Table 2). Caption above tables, below figures. Cite the source if you reuse a diagram. | | Units | Always include units (e.g., “minutes”, “patients/hour”). Use SI where possible. | | Statistical notation | Use proper symbols: μ̂ (sample mean), σ̂ (sample std), CI₉₅ (95 % confidence interval). | | Software version | State the exact Arena version (e.g., “Arena Simulation 15.0 (2024)”). | | File naming | “Lastname_Firstname_ArenaProject.pdf”. | | Plagiarism check | Run the final PDF through your institution’s Turnitin or similar service before submission. | 4. Example Excerpts (Illustrative Only) Below are short snippets that you can adapt for your own report. 4.1 Executive Summary (sample) Executive Summary The emergency department (ED) of City Hospital experiences average patient wait times of 78 min, exceeding the target of 45 min. A discrete‑event simulation model was built in Arena 15.0 to evaluate three staffing scenarios: (1) baseline, (2) one additional triage nurse, and (3) two additional triage nurses. After a 30‑day warm‑up and 30 replications per scenario, the model predicts a 22 % reduction in average wait time (61 min) with one extra nurse and a 38 % reduction (48 min) with two extra nurses. The 95 % confidence intervals for the two‑nurse scenario (46–50 min) do not overlap the baseline interval (75–81 min), confirming statistical significance (p < 0.001). It is recommended that the ED adopt the two‑nurse configuration during peak hours, which yields the desired performance while incurring a modest labor cost increase of 12 %. Further work should incorporate patient acuity levels to refine resource allocation. 4.2 Model Description (text + figure reference) Figure 1 shows the high‑level process flow of the ED model. Patients arrive according to a non‑homogeneous Poisson process (λ(t) varying by hour). After registration (Resource: Registrar , Tri‑Exponential service time), they join the Triage Queue . The triage module (Resource: Triage Nurse ) follows an Erlang‑2 distribution (mean = 4 min). Figure 2 presents the corresponding Arena logic diagram , where the Create , Process , Decide , and Dispose modules implement the flow described above. All random variates are generated using the Arena Random Number Generator (Mersenne‑Twister, seed = 12345) to ensure reproducibility. (Insert Figure 1 – hand‑drawn flowchart; Figure 2 – Arena screenshot with numbered modules.) 4.3 Validation Table | Metric | Real‑World Observation (Mean ± SD) | Model Output (Mean ± SD) | % Error | Validation Verdict | |--------|-----------------------------------|--------------------------|---------|--------------------| | Avg. wait time (min) | 78 ± 12 | 80 ± 11 | +2.6 % | Pass (|error| < 5 %) | | % patients leaving without being seen | 4.5 % | 4.7 % | +4.4 % | Pass | | Avg. staff utilization | 0.86 | 0.88 | +2.3 % | Pass |

Validation criteria: error < 5 % for all key metrics (Law & Kelton, 2022). A one‑way ANOVA was performed to compare average patient wait time across the three staffing scenarios. The overall F‑statistic was F(2,87) = 41.2 , p < 0.0001, indicating at least one scenario differs significantly. Post‑hoc Tukey HSD tests yielded the following pairwise differences (all p < 0.01): • Baseline vs. 1‑nurse: Δ = ‑17 min (95 % CI: ‑22 to ‑12) • Baseline vs. 2‑nurse: Δ = ‑30 min (95 % CI: ‑36 to ‑24) • 1‑nurse vs. 2‑nurse: Δ = ‑13 min (95 % CI: ‑18 to ‑8) 5. Checklist Before Submission | ✔ | Item | |---|------| | ☐ Title, abstract, and table of contents are present. | | ☐ All figures/tables are numbered, captioned, and referenced in the text. | | ☐ Model screenshots are clear; each Arena module is labeled. | | ☐ Verification & validation evidence is