Prescriptive Analytics: Optimization and Simulation - Mathematical Programming Optimization
6 important questions on Prescriptive Analytics: Optimization and Simulation - Mathematical Programming Optimization
What is mathematical programming?
In Linear programming, all relationships among the variables are linear. It is extensively used in DSS.
Important applications:
- supply chain management
- product mix decisions
- routing
What are the major parts of an LP model?
- the objective function
- the decision variables
- the constraints
List and explain the assumptions involved in LP
The return from any allocation in independent of other allocations
The total return is the sum of the returns yielded by the different activities
All data are known with certainty The resources are to be used in the most economic manner
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List and explain the characteristics of LP
the mathematical relationships are all linear equations and inequations
Describe the allocation problem
List several common optimization models.
Dynamic programming
Goal programming
Investment (maximize rate of return)
Linear and integer programming
Network models for planning and scheduling
nonlinear proggramming
Replacement (capital budgetting)
Simple inventory models (e.g. economic order quantity)
Transportation (minimize cost of shipments)
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