Difference between revisions of "Concepts Covered in the Airline NLP Example"
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Before reading this chapter, you should already be familiar with the basic parameters of [http://wiki.analytica.com/index.php?title=DefineOptimization DefineOptimization]() and [http://wiki.analytica.com/index.php?title=OptSolution OptSolution]() functions, as discussed in the [http://wiki.analytica.com/index.php?title=Quick_Start Quick Start], and the roles of intrinsic and extrinsic indexes in optimization, as discussed in [http://wiki.analytica.com/index.php?title=Arrays_in_Optimization_Models_and_Array_Abstraction Arrays in Optimization Models and Array Abstraction]. | Before reading this chapter, you should already be familiar with the basic parameters of [http://wiki.analytica.com/index.php?title=DefineOptimization DefineOptimization]() and [http://wiki.analytica.com/index.php?title=OptSolution OptSolution]() functions, as discussed in the [http://wiki.analytica.com/index.php?title=Quick_Start Quick Start], and the roles of intrinsic and extrinsic indexes in optimization, as discussed in [http://wiki.analytica.com/index.php?title=Arrays_in_Optimization_Models_and_Array_Abstraction 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 | + | Additionally, Modules 3 and 4 of the Airline NLP example assume familiarity with Monte Carlo simulation and Probability Distributions (see Probability Distributions section in the Analytica User Guide). Module 7 assumes familiarity with the [http://wiki.analytica.com/index.php?title=Dynamic Dynamic]() function (see Dynamic Simulation section in the Analytica User Guide). |
Topics relevant to all optimization types (LP, QP, and NLP) are: | Topics relevant to all optimization types (LP, QP, and NLP) are: |
Revision as of 18:37, 20 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 (see Probability Distributions section in the Analytica User Guide). Module 7 assumes familiarity with the Dynamic() function (see Dynamic Simulation section in the Analytica User Guide).
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 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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