View source for Concepts Covered in the Airline NLP Example

[[Category: Analytica Optimizer Guide]]
<breadcrumbs> Analytica Optimizer Guide > Optimizer key concepts: Airline Example> {{PAGENAME}}</breadcrumbs><br /> 

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 (see [[Statistics, Sensitivity, and Uncertainty Analysis]] in the Analytica User Guide). 

Module 7 assumes familiarity with the [[Dynamic]]() function (see [[Dynamic Simulation]] in the Analytica User Guide).

Topics relevant to all optimization types (LP, QP, and NLP) are:
* '''[[Airline_NLP_Module_1%3A_Base_Case|Module 1]]'''[[Airline_NLP_Module_1%3A_Base_Case|: Setting up basic Airline NLP example]]
* '''[[Using_Parametric_Analysis%3A_Airline_NLP_Module_2|Module 2]]'''[[Using_Parametric_Analysis%3A_Airline_NLP_Module_2|: Parametric Analysis]]
* Combining uncertainty with optimization:
** '''[[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|Module 3]]'''[[Optimizing_with_Uncertainty#Module_3:_Stochastic_Optimization_.28FAST.29|: Optimizing on Fractiles or Averages Stochastically (FAST)]]
** '''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Module 4]]'''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|: Multiple Optimizations of Separate Samples (MOSS) method]]

* '''[[Module_5%3A_Time_as_an_Extrinsic_index|Module 5]]'''[[Module_5%3A_Time_as_an_Extrinsic_index|: Abstracted objectives; example of Time as an extrinsic index]]
* '''[[Module_6%3A_Time_as_an_Intrinsic_Index|Module 6]]'''[[Module_6%3A_Time_as_an_Intrinsic_Index| : Intrinsic decision arrays; example of Time as an intrinsic index]]<br />
<br />
Embedded topics relevant only to Non-Linear Problems (NLPs) are:<br />

* Improving efficiency using context variables ('''[[Optimizing_with_Uncertainty#Module_4:_Multiple_Optimizations_of_Separate_Samples_.28MOSS.29|Modules 4]] and [[Module_5%3A_Time_as_an_Extrinsic_index|5]]''')
* '''[[Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop|Module 7]]'''[[Module_7%3A_Embedding_an_NLP_in_a_Dynamic_Loop|: Embedding an NLP inside a dynamic loop]]


<footer> Optimizing with Arrays / {{PAGENAME}} /  NLP Characteristics</footer>