Skip to content

Teaching with Quisim ​

This page is for lecturers who plan a full-semester course on discrete-event simulation (DES) with Quisim, about 14 weeks with one lecture and one lab session per week. It is an outline, not lecture material: it suggests an order of topics, points to the handbook sections that cover each one, and names an exercise built on models that already exist. You write your own slides and set your own exercises.

How to use it. Read the row for a week, open the linked sections, and pick what fits your students. The handbook pages are the single source of truth for block names, attributes and numbers, so link students to them instead of copying the content into slides.

Ready-made decks. These slide decks are built around models students run themselves, most of them in the free demo:

Reference pages used below: Handbook, Block API Reference, Cookbook, Examples, Hospital and Extrusion Line case studies, User Interface Guide.

Week-by-week plan ​

WeekTopicReadSuggested exercise
1Introduction and DES conceptsWhy simulate?, A simulation study, step by step, How a discrete-event simulation runs, Open an example and run it, The Monitor tab, Event ListRun the built-in M/M/1 example with Animate on, pause, press Step a few times in Monitor > Events and explain each event. Deck: Introduction to Quisim.
2Queueing review, M/M/1The M/M/1 Queue, Verifying the M/M/1 model against theory, Beyond M/M/1: G/G/1, Kendall notation in QuisimVary the utilization of the M/M/1 model from 0.5 to 0.95 and compare simulated waiting times with the formulas.
3Building modelsCore Concepts, Entity Blocks, Building a model on the canvas, How to Read an EntryBuild the Call Center from scratch; extend it with the recipes on routing by probability and priorities.
4Random variates and input modelingRandom Number Blocks, Choosing a distribution, Input modeling: from data to parameters, When data is missing, Time-dependent arrival rates, API: Random Number BlocksGive students about 100 measured service times (or let them time a real process): fit a distribution, then replay the data with a ContinuousDistribution block and compare EV, ESD and a Histogram. Model the four arrival rates of the hospital data with the shifts recipe.
5Output analysis I: terminating systems, replications, confidence intervalsTerminating vs. non-terminating simulations, Which mean?, Reading the confidence interval, API: Statistics Block, Steering BlockRecipe replications for a day-long simulation; CI vs. number of replications in the flow shop.
6Output analysis II: warm-up, batch meansRecipe warm-up, API: EnsemblePlot Block, batch meansFind the warm-up of the M/M/1 model at high load with EnsemblePlot, then compare replications with one long run using numBatches; Extrusion Exercise 4.
7Verification and validationModel Testing: Verification and Validation, Verification checklist, Validation in three stepsHand students a model with planted faults (missing Queue, closed Decide) to find with the checklist.
8From state diagrams to modelsFrom State Diagram to Model, UML state diagrams, Virus SpreadDraw the state diagram of a machine with breakdowns, then build it (Machine state diagram, recipe breakdowns). Projects start.
9Production systems: flow shop and job shopProduction Simulation, Flow Shop Production, Job Shop Production, Two-Step Production Line, Extrusion Line case studyExtrusion Step 1 to Step 3; Exercise 1: Bottleneck by Hand.
10Services and healthcareHospital case study, Measuring Service Level, recipes impatient customers and shared workersHospital Exercise 1: Morning Shift and Exercise 3: Triage + Treatment. Deck: How many doctors, and when?.
11Comparing systems, common random numbersSystem Comparison with CRN, Getting CRN right, recipe comparing two configurationsRun the M/D/2 vs. M/D/1 example with and without CRN and report how much narrower the interval of the difference gets. Then compare lane layouts and lane-choice policies with the recipe several lanes or one line: which differences are real, and which are noise?
12Design of experimentsDesign of Experiments (DoE), Implementing DoE in Quisim, Explorer BlockRun a 232^3 factorial design on the extrusion line (campaign size 30/40, press MTTR 15/30 min, 2/3 aging ovens) and estimate main and interaction effects on the lead time.
13Simulation optimizationSimulation Optimization, Grid Search: M/M/10, Kiefer-Wolfowitz, BrentOptimize BlockRecipe best value of a parameter; hospital grid search with Explorer; extrusion campaign size.
14Project presentationsA simulation study, step by step as the report structureTeams present their project (see Assessment).

Project work runs alongside from week 8: teams choose a system, collect or estimate data, and use weeks 9 to 13 to apply each new topic to their own model. Lab time in those weeks can be split between the exercise and the project.

Shorter formats ​

  • Three-week block course on the free demo. Use the 3-week course with its Week 1, Week 2 and Week 3 decks.
  • Three-week block course with the full version. Week 1: weeks 1 to 4 of the table (concepts, M/M/1, building models, distributions). Week 2: output analysis, V&V and one case study (hospital or extrusion), weeks 5 to 7 and 9 or 10. Week 3: CRN and optimization in brief (weeks 11 and 13) and a small project presented on the last day.
  • Single workshop (half a day to a day). The Introduction to Quisim deck, the M/M/1 model built and checked against theory, then the hospital case study Steps 1 to 3 with the How many doctors, and when? deck.
  • Module inside another course (operations management, queueing theory, healthcare management): pick the weeks you need; weeks 2, 5 and 10 work as a self-contained unit.

Assessment ​

A project fits the subject better than a written exam. Suggestions:

  • Team project (2 to 4 students) on a system of their choice, following the steps of a simulation study: problem, conceptual model, data, model, verification and validation, experiments, recommendation.
  • Deliverables: the .qui model, a short report and a presentation. Grade the reasoning (assumptions, choice of terminating or non-terminating analysis, confidence intervals, comparison method) more than the size of the model.
  • Small graded exercises along the way, for example the CRN comparison (week 11) or one case-study exercise, to check the methods before the project relies on them.