# Prescriptive Analytics: Optimization and Simulation - Mathematical Programming Optimization

## 6 important questions on Prescriptive Analytics: Optimization and Simulation - Mathematical Programming Optimization

### What is mathematical programming?

is a family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal?
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

Returns from different allocations can be compared (common measure unit)
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

### List and explain the characteristics of LP

all relationships among the variables are linear
the mathematical relationships are all linear equations and inequations

### Describe the allocation problem

The allocation is usually restricted by several limitations and requirements, called constraints

### List several common optimization models.

Assignment (best matches off objects)
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)

The question on the page originate from the summary of the following study material: • A unique study and practice tool
• Never study anything twice again
• Get the grades you hope for
• 100% sure, 100% understanding
Remember faster, study better. Scientifically proven. 