Geradlinig Programming: Class Notes

 Linear Coding: Class Paperwork Essay

п»їLinear coding

Introduction

Formulign the objective

Case in point: maximise the whole weekly benefit from factory A and W

You need to be spefic and give products

Define the terms

Manufacturing plant A profit = 300*x

Manufacturing plant B revenue = 100*y

Formulate limitations

Physical restrictions

Non-negativity restriction

Implied romance restrictions

thirty percent of total has to be via factory A

Bulidn linear coding models

Ratio's need to be changed from non-linear to a geradlinig format. Take the denominator make it on top. You can also associated with demonintor known as D and reaggrange it then reveal what D is. Solving linear pgoramms

Fesible can be when the answer is possible

Slacks and surpluses

3x1 + 5x2 > sama dengan 10

3x1 & 5x2 = 10 + y1

Y1> =0

Y1 represetns slack

Non-zero varalbes are called basic factors

Zero varalbes are called non-basic variables

Simplex algorithm

????

Snesivty and paramtric anlysis

What happens if the object and cosntta modify

The constants following to the decision variables these are known as object ceoefficnet

Constiants are called right side

Constant*F

Final Value: there are zero F's

Precisely what is reduced cost??

Goal coefficnet: is a constant next to Farrenheit

Allowable increase: this is the amount the aim coefficnet can increase without having to resolve the program, Allowable decrease: this is the sum the objective coefficnet can decrease without having to handle the program Remember if goal coeffeint is still with in the product range it the perfect solution is is opimial, then we don't need to solve. But as the objective coeffient has changed then this Constant*F has evolved value and wishes to be reculated. This can affect the objective function If two objective coeffient change then simply we have to resolve but if they change by the same amount then the still opitmial as they are a similar xontours pertaining to the object function?? in examination The constraint coeffient aren't change with out resolve the machine, 2x & 1y For example x= 1 . 5, x2

Thes psobliear exhuasive as they usually do not exlude any possible vlues for X1.

This kind of creates two new geradlinig programs, the are nodes on a search tree

All the nodes are then solved as well as the opimital answer is found

Bouding is used to identify optimality

Staffing problem

Remember to consider when people can start

Remember people are interger

Cutting share problem

The solution is fesiable if the number ordered is definitely 0, interger

If we could make money via surplus rolls then

5 cms rolls sama dengan 2*x2 & 5x3 – y1 sama dengan 150

150 may be the order

All of us then add into the aim function

2x1 -. 25*y1

Interger is employed, upper and lower bound

Xss

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