Question 7 of 8: Eight Steps of a Simulation Study: Ambulance Supply Management
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
Notes on this paper
National Exams — December 2017 — 98-Ind-A6 Systems Simulation. Three-hour, closed-book exam; one of two permitted calculators (Sharp or Casio), two 8.5″×11.0″ aid sheets (both sides). Format: eight questions of equal value (10 marks each); candidates complete SIX of the EIGHT (only the first six as they appear in the answer book are marked). All eight questions are solved below for completeness (the paper's own Q6 is printed with an orphan leading "6." followed by "2. Consider an M/M/1 system…" and is answered as the paper's sixth question). Common Discrete/Continuous Distribution tables, Student-t and Chi-square tables were supplied with the exam; the values below are the same table values obtained by direct computation.
Reference texts: Banks, Carson, Nelson & Nicol, Discrete-Event System Simulation (5th ed., Pearson) — random-number/random-variate generation, input data analysis, output analysis (replications), comparing alternative systems, queueing simulation, and the simulation study life cycle.
Question 7 — Eight Steps of a Simulation Study: Ambulance Supply Management (10 marks)
The ambulance-supply problem is worked through the standard simulation-study life cycle (Banks et al.), condensed here to its eight essential stages:
1. Problem formulation. State the problem precisely with the sponsor (the ambulance service): supplies at some stations are apparently mismanaged — is that stock-outs of critical items, over-stocking that expires/wastes budget, or uneven distribution across stations? Formulation turns a vague complaint into an answerable question, e.g. "what re-order policy per station minimizes stock-out probability for critical items subject to a fixed total supply budget?"
2. Setting of objectives and project plan. Define measurable objectives (target stock-out probability per item per station, inventory-holding cost), the scope (which stations, which items), and a realistic timeline/budget/team for the study, agreed with the sponsor before any modelling begins.
3. Model conceptualization. Abstract the real system into entities (ambulances, supply items, restock deliveries), resources (station stock levels, a central warehouse), and events (an ambulance call consumes supplies, a scheduled/triggered restock delivery replenishes them) — simple enough to build and explain, rich enough to answer the objectives.
4. Data collection. Gather call/consumption-rate data per item per station (historical dispatch logs), restock lead times from the supplier/warehouse, and current re-order policies/stock levels; identify which inputs are the highest-priority unknowns for the model.
5. Model translation. Code the conceptual model in a simulation language/package (e.g. Arena, Simio, or a custom DES program), implementing the consumption and restock logic and the station-level inventory-tracking logic.
6. Verification. Confirm the CODE matches the CONCEPTUAL model — trace a handful of calls/restocks by hand and compare to the program's own event log; unit-test the restock-trigger logic in isolation.
7. Validation. Confirm the MODEL matches the REAL ambulance-supply system — compare simulated stock-out frequency against the service's own historical incident reports for face validity, and, once policy changes are simulated, pilot a small change at one real station to check the model's prediction before service-wide rollout.
8. Experimental design, production runs, and implementation. Design experiments across candidate re-order policies (e.g. varying re-order point/quantity per item), run enough replications for statistically defensible comparisons (per Question 3's replication-count method), analyse the results, and hand the recommended policy back to the ambulance service with a documented report for implementation.
