Difference between revisions of "Concepts Covered in the Airline NLP Example"

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* '''Module 5''': Abstracted objectives; example of Time as an extrinsic index
 
* '''Module 5''': Abstracted objectives; example of Time as an extrinsic index
 
* '''Module 6''': Intrinsic decision arrays; example of Time as an intrinsic index<br />
 
* '''Module 6''': Intrinsic decision arrays; example of Time as an intrinsic index<br />
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<br />
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Embedded topics relevant only to Non-Linear Problems (NLPs) are:<br />
  
Embedded topics relevant only to Non-Linear Problems (NLPs) are:
 
 
* Improving efficiency using context variables ('''Modules 4 and 5''')
 
* Improving efficiency using context variables ('''Modules 4 and 5''')
 
* '''Module 7''': Embedding an NLP inside a dynamic loop<br />
 
* '''Module 7''': Embedding an NLP inside a dynamic loop<br />

Revision as of 14:42, 18 November 2015


Before reading this chapter, you should already be familiar with the basic parameters of DefineOptimization() and OptSolution() functions, as discussed in the Quick Start, and the roles of intrinsic and extrinsic indexes in optimization, as discussed in Arrays in Optimization Models and Array Abstraction.

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


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