Difference between revisions of "Optimizer key concepts: Airline Example"

Latest revision as of 17:40, 7 June 2016

Analytica Optimizer GuideOptimizer key concepts: Airline Example

The Airline Non-Linear Program (NLP) Example 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.

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 [[Category: Analytica Optimizer Guide]]
-<breadcrumbs> Analytica Optimizer Guide > {{PAGENAME}}</breadcrumbs><br />
+<breadcrumbs> Analytica Optimizer Guide > {{PAGENAME}}</breadcrumbs><br />The Airline Non-Linear Program (NLP) Example 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.
-This chapter shows you how to:
+== Sections ==
-* Apply parametric variations to Variable and Decision nodes in optimizations
+* [[Concepts Covered in the Airline NLP Example]]
-* Use a parametric array of Initial Guesses
+* [[NLP Characteristics]]
-* Combine uncertainty with optimization using
+* [[Airline NLP Module 1: Base Case]]
-* Fractile or Average Stochastic method (FAST)
+* [[Using Parametric Analysis: Airline NLP Module 2]]
-* Multiple Optimizations of Separate Samples (MOSS) method
+* [[Optimizing with Uncertainty]]
-* Optimize using reduced objectives
+** [[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|Module 3: Stochastic Optimization (FAST)]]
-* Use Time as an intrinsic or extrinsic index
+** [[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Module 4: Multiple Optimizations of Separate Samples (MOSS)]]
-* Understand special characteristics of NLPs
+* [[Improving Computational Efficiency of NLPs]]
-* Improve efficiency of NLPs using the SetContext parameter of DefineOptimization()
+* [[Module 5: Time as an Extrinsic index]]
-* Plane an Optimization inside a Dynamic Loop
+* [[Identifying the Source of an Extrinsic Index]]
+* [[Module 6: Time as an Intrinsic Index]]
-==Concepts covered in the Airline NLP example==
+* [[Module 7: Embedding an NLP in a Dynamic Loop]]
+* [[Controlling Engine Selection and Setting]]
-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.
+<br />
+<footer> Optimizing with Arrays / {{PAGENAME}} / Optimizer Attributes</footer>
-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
-<footer> Optimizing with Arrays / {{PAGENAME}} / Optimizer Attribute Reference</footer>