Optimizer key concepts: Airline Example

Analytica Optimizer GuideOptimizer key concepts: Airline Example

This chapter shows you how to:

Apply parametric variations to Variable and Decision nodes in optimizations
Use a parametric array of Initial Guesses
Combine uncertainty with optimization using
Fractile or Average Stochastic method (FAST)
Multiple Optimizations of Separate Samples (MOSS) method
Optimize using reduced objectives
Use Time as an intrinsic or extrinsic index
Understand special characteristics of NLPs
Improve efficiency of NLPs using the SetContext parameter of DefineOptimization()
Plane an Optimization inside a Dynamic Loop

Concepts covered in the Airline NLP example

The Airline NLP demonstrates a set of key concepts that Analytica Optimizer modelers should be familiar with. Although it includes some topics that apply only to NLP models, each module includes content that is relevant to all optimization types.

Before reading this chapter, you should already be familiar with the basic parameters of DefineOptimization() and OptSolution() functions (Chapter 1 on page 5-7), and roles of intrinsic and extrinsic indexes in optimization (Chapter 3 of this guide).

Additionally, Modules 3 and 4 of the Airline NLP example assume familiarity with Monte Carlo simulation and Probability Distributions (User Guide Chapter 16). Module 7 assumes familiarity with the Dynamic() function (User Guide Chapter 18).

Topics relevant to all optimization types (LP, QP, and NLP) are:

Module 1: Setting up basic Airline NLP example
Module 2: Parametric Analysis
Combining uncertainty with optimization:
- Module 3: Optimizing on Fractiles or Averages Stochastically (FAST)
- Module 4: Multiple Optimizations of Separate Samples (MOSS) method

Module 5: Abstracted objectives; example of Time as an extrinsic index
Module 6: Intrinsic decision arrays; example of Time as an intrinsic index

Embedded topics relevant only to Non-Linear Problems (NLPs) are:

Improving efficiency using context variables (Modules 4 and 5)
Module 7: Embedding an NLP inside a dynamic loop