A Simulation Example Model

Below is a simple Monte Carlo simulation / risk analysis model that determines the optimal number of tickets to sell in an upcoming flight. The example takes into account "no-shows" as well as compensation fees if the flight sells out. The uncertainVariables section defines one uncertain (random) variable (no_shows which uses a PsiLogNormal distribution to model the amount of customers who will not show up for a flight), and the uncertainFunctions section defines one uncertain function (revenue). Our goal is to find the expected mean (or average) revenue.

{
 modelName: "ExampleSimulation",
 modelType: "simulation",
 modelSettings : { numSimulations: 1, numTrials: 1000, randomSeed: 1 },
 data : {
    price: { value: 200 },
    capacity: { value: 100 },
    sold: { value: 110 },
    refund_no_shows: { value: 0.5 },
    refund_overbook: { value: 1.25 }
 },
 uncertainVariables : {
    no_shows: { formula: "PsiLogNormal(0.1*sold, 0.06*sold)", mean: [] }
 },
 formulas : {
    show_ups: { formula: "sold - Round(no_shows, 0)" }, 
    overbook: { formula: "Max(0, show_ups - capacity)" }
 },
 uncertainFunctions : {
   revenue: { formula: "price* (sold - refund_no_shows * ROUND(no_shows,0) - refund_overbook - overbook)", mean: [] }
 }
}

By default, the JSON result of evaluating this model contains a set of summary statistics for the output "revenue", across all Monte Carlo trials.

  {
  "status": {
  "code": 0,
  "id": "2590+ExampleSimulation+2020-01-02-07-30-10-477802",
  "codeText": "Solver has completed the simulation."
  },
  "uncertainFunctions": {
  "revenue": {
  "mean": 20286.4}
  },
  "uncertainVariables": {
  "no_shows": {
  "mean": 10.9922}
  }
  }