Introduction to Linear Programming for Knowledge Science

Introduction to Linear Programming for Knowledge Science

linear programming

Contributed by: Sarita Upadhya
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Knowledge Science helps companies to make knowledgeable and data-driven choices. Present enterprise conditions or enterprise eventualities could be explored additional utilizing obtainable information, and higher insights could be drawn utilizing Descriptive Analytics. Utilizing previous information, we are able to predict the long run. Additionally, the trigger and impact relationship between predictors and response variables could be extracted utilizing Predictive Analytics. Predictive fashions are additionally used to simulate a future possible end result by altering and assigning values of the predictors. As part of Prescriptive Analytics, enterprise begins wanting on the totally different options obtainable to finalise the choice. Together with the obtainable insights, useful resource constraints are taken into consideration to get an optimum resolution.

Linear Programming (LP) in Operations Analysis is without doubt one of the scientific strategies that’s used to get an optimum resolution to the given enterprise downside by taking useful resource shortage and constraints into consideration. When choice making pertains to a steady variable like gross sales, revenue, value and many others. which has a linear relationship with a number of enter variables, Linear Programming is utilized to take into consideration the restrictions or constraints related, and arrive at the very best resolution. For e.g. if a enterprise needs to maximise gross sales, there could also be a constraint by way of the quantum of product obtainable on the market; the enterprise might need to maximise the revenue by rising the manufacturing nevertheless might have a constraint by way of the capability of machines and many others. LP offers an strategy to the above issues to get an optimum resolution by contemplating the constraints.

Learn Extra – What’s Knowledge Science?

LP Software Necessities

To be able to apply Linear Programming for Knowledge Science, the under necessities need to be met:

  1. We must always have the ability to outline the target or the purpose clearly in mathematical phrases. 
  2. The enter variables that decide the target needs to be distinct and quantitative. 
  3. Limitations or constraints that need to be thought-about needs to be quantitative and measurable.
  4. Relationship between the target and the enter variables must be linear in nature.

Formulation of LP Issues

Beneath are the steps to be adopted to formulate LP Downside:

  1. Establish the choice variables which are used to formulate and obtain the target operate
  2. Outline the target operate: It defines the enterprise goal in mathematical kind
  3. Listing out the constraints or limitations utilizing the choice variables in mathematical kind
  4. Establish and record out the non-negative constraints 

Let’s perceive this with a number of examples. Additionally, we’ll take a look at the three instruments (Graphical methodology, Excel Solver, R) which can be utilized to resolve LP issues.


Downside Assertion 1

The XYZ firm through the competition season combines two elements A and B to kind a present pack which should weigh 5 kg. At the very least 2 kg of A and less than 4 kg of B needs to be used. The web revenue contribution to the corporate is Rs. 5 per kg for A and Rs. 6 per kg for B. Formulate LP Mannequin to search out the optimum issue combine.

LP Formulation:

Step 1: Goal Operate

Within the above downside, the target of the corporate is to maximise the revenue. We’re given the web revenue contribution for issue A and B.

  • Let x kg of issue A be used
  • Let y kg of issue B be used
  • Goal Operate ⬄ maximise z = 5x + 6y

Notice: x, y are choice variables and z is the target operate

Step 2: Formulate the constraints

Weight of present pack must be 5 kg

At the very least 2 kg of A and less than 4 kg of B must be used

Step 3: Non-negative constraints

The amount used for A and B must be optimistic

Summarise the LP Downside:

Goal: Maximise z = 5x+6y given the constraints

  1. x + y = 5
  2. x >= 2
  3. y <= 4
  4. x, y >= 0

Let’s attempt to clear up the above LP downside graphically

Graphical Resolution:

Allow us to think about a 2-dimension graph with x and y-axis:

Linear Programming
Fig 1: Graphical Resolution for LP downside
  • Plot the constraints formulated within the above downside.
    • x + y = 5 is a line that cuts x-axis at (5,0) and y-axis at (0,5). As we’ve a “=” constraint, the possible area lies on this line.
    • x >= 2 is the road that cuts the x-axis at (2,0). As we’ve a “>=” constraint, the possible area lies to the precise of this line
    • y <= 4 is the road that cuts the y-axis at (0,4). As we’ve a “<=” constraint, the possible area lies to the underside of this line
    • As x, y >= 0 we’ve thought-about solely the optimistic facet of the x and y-axis.
  • Combining all of the above constraints, from the above graph the road marked in yellow is the ultimate possible area the place all constraints are glad. 
  • The two excessive factors on this line are the attainable choices for consideration to maximise the revenue i.e. level A (2,3) and level B (5,0). 
  • Allow us to substitute these values within the goal operate to search out out which possibility provides increased revenue.
    • Choice A (2,3): z = 5x + 6y ⬄ z = (5*2) +(6*3) = 28
    • Choice B (5,0): z = 5x + 6y ⬄ z = (5*5) +(6*0) = 25
  • Therefore, the optimum resolution is Choice A i.e. 2kg of issue A and 3kg of issue B to be thought-about for the present pack.

Downside Assertion 2

A name centre requires a special variety of full-time staff on totally different days of the week. The variety of full-time staff required every day is given within the desk under. Employment guidelines state that every full-time worker should work 5 days consecutively after which obtain two days off. The decision centre needs to fulfill its each day necessities utilizing solely full-time staff. Formulate an LP that the decision centre can use to minimise the variety of full-time staff who should be employed.

Days of the Week # of full-time staff required
1=Monday 17
2=Tuesday 13
3=Wednesday 15
4=Thursday 19
5=Friday 14
6=Saturday 16
7=Sunday 11

LP Formulation:

Step 1: Goal Operate

Within the above downside, the target is to minimise the variety of full-time staff to rent.

  • Let x1 be variety of staff starting shifts from Monday
  • Let x2 be variety of staff starting shifts from Tuesday
  • Let x3 be variety of staff starting shifts from Wednesday
  • Let x4 be variety of staff starting shifts from Thursday
  • Let x5 be variety of staff starting shifts from Friday
  • Let x6 be variety of staff starting shifts from Saturday
  • Let x7 be variety of staff starting shifts from Sunday
  • Goal Operate ⬄ minimise z = x1+x2+x3+x4+x5+x6+x7

Notice: x1, x2…x7 are choice variables and z is the target operate

Step 2: Formulate the constraints

From the above desk, we all know the variety of full-time staff required every day. Constraint to be thought-about is {that a} full-time worker ought to work for five consecutive days and have 2 days off. 

  • x1+x4+x5+x6+x7 >=17 (On Monday, staff engaged on Tuesday & Wednesday get off)
  • x1+x2+x5+x6+x7 >=13 (On Tuesday, staff engaged on Wednesday & Thursday get off)
  • x1+x2+x3+x6+x7 >=15 (On Wednesday, staff engaged on Thursday & Friday get off)
  • x1+x2+x3+x4+x7 >=19 (On Thursday, staff engaged on Friday & Saturday get off)
  • x1+x2+x3+x4+x5 >=14 (On Friday, staff engaged on Saturday & Sunday get off)
  • x2+x3+x4+x5+x6 >=16 (On Saturday, staff engaged on Sunday & Monday get off)
  • x3+x4+x5+x6+x7 <=11 (On Sunday, staff engaged on Monday & Tuesday get off)

Step 3: Non-negative constraints

Variety of staff working every day must be 0 or larger than 0, therefore 

  • x1, x2, x3, x4, x5, x6, x7 >= 0

Summarise the LP Downside:

Goal: Minimise z = x1+x2+x3+x4+x5+x6+x7 topic to constraints

  1. x1+x4+x5+x6+x7 >=17 
  2. x1+x2+x5+x6+x7 >=13 
  3. x1+x2+x3+x6+x7 >=15 
  4. x1+x2+x3+x4+x7 >=19 
  5. x1+x2+x3+x4+x5 >=14 
  6. x2+x3+x4+x5+x6 >=16 
  7. x3+x4+x5+x6+x7 >=11
  8. x1, x2, x3, x4, x5, x6, x7 >= 0

As we’ve a number of choice variables, fixing this graphically shall be a tedious job. Allow us to attempt to clear up the above downside utilizing Excel Solver

Resolution utilizing Excel Solver:

The solver is a mathematical program or software program that takes required enter, applies the chosen algorithm and offers a mathematical resolution to the issue.

Guarantee you might have Solver add-in obtainable within the Knowledge tab of your Excel Workbook as proven within the determine under:

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Fig 2: Solver Add-in in Excel

Step 1: Listing Constraints in Excel Sheet 

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Fig 3: Constraints in Excel
  • Fig 3 reveals how the primary 7 constraints need to be up to date within the excel
  • The coefficients of every constraint are up to date under the respective variables

Step 2: Embrace the Goal Operate

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Fig 4: Goal Operate
  • Fig 4 reveals how the target operate must be included
  • Much like the constraints embody the coefficients of the target operate under the respective variables

Step 3: Embrace a row to get the very best estimate for our choice variables

Linear Programming
Fig 5: Row to get optimum worth for variables
  • Row 11 within the above figures is the row which can give us the very best estimate 
  • Initialise it to 0
  • As soon as the solver finds the answer the row shall be up to date with finest estimates

Step 4: Embrace sumproduct() of every constraint with output row (Row 11) to formulate constraints 

Linear Programming
Fig 6: Sumproduct() operate to outline the constraints
  • Observe the “fx” cell on the prime in Fig6
  • Column H has the sumproduct() of every row with the very best estimate row i.e. row 11
  • Cells marked in inexperienced will show the values of every constraint after solver finds an answer
  • The cell marked in pink is the output of the target operate i.e. the optimum output shall be displayed as soon as the answer is discovered

Step 5: Invoke Solver and add the constraints

Linear Programming
Fig 7: Invoke Solver and supply the inputs
  • From the determine above, observe the cell inputs to be given to the Solver
  • Choose “Simplex LP” because the “Fixing Technique”
  • Click on “Clear up”

Step 6: Get the optimum resolution

Click on on ‘Clear up’ to get the optimum resolution

Linear Programming
Fig 8: Resolution
  • From the above picture, we see that the optimum resolution is to get 23 full-time staff for the job contemplating the given constraints (cell H10)
  • Values in Row 11 that are marked in yellow are the very best estimates for every choice variable
  • Values in inexperienced verify to us that the constraints have been glad

Downside Assertion 3

John has a food plan chart which supplies energy, protein, carbohydrate and fats content material for 4 meals objects. John needs a food plan with minimal value. The food plan chart is as follows:

Item1 Item2 Item3 Item4 Required
Energy (gms) 400 200 150 500 500
Proteins (gms) 3 2 0 0 6
Carbohydrates (gms) 2 2 4 4 10
Fats (gms) 2 4 1 5 8
Value  0.5$ 0.2$ 0.3$ 0.8$

LP Formulation:

Step 1: Goal Operate

Within the above downside, the target is to purchase meals objects at minimal value however assembly the dietary wants.

  • Let x1 be variety of Item1 to be purchased
  • Let x2 be variety of Item2 to be purchased
  • Let x3 be variety of Item3 to be purchased
  • Let x4 be variety of Item4 to be purchased
  • Goal Operate ⬄ minimise z = 0.5×1+0.2×2+0.3×3+0.8×4

Notice: x1, x2, x3, x4 are choice variables and z is the target operate

Step 2: Formulate the constraints

Energy required 500

  • 400×1 + 200×2+ 150×3+ 500×4 >= 500

Proteins required 6

Carbohydrates required 10

  • 2×1 + 2×2 +4×3 + 4×4 >= 10

Fats required 8

  • 2×1 + 4×2 +x3 + 5×4 >= 8

Step 3: Non adverse constraints

Objects purchased need to be optimistic

Summarise the LP Downside:

Goal: Minimise z = 0.5×1+0.2×2+0.3×3+0.8×4 given the constraints

  1. 400×1 + 200×2+ 150×3+ 500×4 >= 500
  2. 3×1 + 2×2 >= 6
  3. 2×1 + 2×2 +4×3 + 4×4 >= 10
  4. 2×1 + 4×2 +x3 + 5×4 >= 8
  5. x1, x2, x3, x4 >= 0

Resolution utilizing R: 

R is a programming language which has a bundle and related operate outlined to resolve the LP downside. The bundle title is ‘lpSolve’ and this system to resolve the above downside is given under:

Linear Programming


Linear Programming

From the above we’ve bought the very best estimates, 3 – Item2 and 1- Item3 must be purchased to get required vitamins at a minimal value of 0.9$

Benefits of Linear Programming

  • LP is beneficial for the enterprise because the decision-maker can get hold of an optimum resolution by contemplating the efficient use of scarce assets
  • It’s a structured method and helps in making knowledgeable data-driven choices
  • It offers alternate options which the decision-maker can analyse additional and finalise primarily based on subjective issues that additionally have to be thought-about
  • LP will also be used for altering conditions. Modified constraints or further constraints could be included within the mannequin to get revised output.

Software of Linear Programming

  • LP is broadly utilized in Manufacturing to determine on Manufacturing combine, in Manufacturing Planning, Meeting line balancing and many others.
  • Oil industries use LP to get optimum high quality of oil by mixing the precise proportion of various crude oils, octane and many others.
  • In finance, choices need to be taken on the place the cash needs to be spent and from the place the corporate must get the cash from to make sure that they maximise the returns holding the dangers below acceptable management. Shopping for and promoting bonds, managing company funds, making monetary choices.
  • Media choice, plant location, decreasing travelling salesman’s value are a number of the areas the place LP is used within the gross sales and advertising and marketing house.
  • In call-centres and hospitals, LP is used for scheduling the shifts for workers.
  • Defence makes use of LP to determine on the optimum degree of pressure deployment to a specific place contemplating the totally different conditions and constraints.

Should you want to study extra ideas in Knowledge Science for a profitable profession within the area, join Nice Studying’s PGP Knowledge Science Programs.

Reference Books

  • ‘Quantitative Strategies for Resolution Making in Enterprise’ by Trueman, R.E. Half Saunders, New York, 1981 
  • ‘Quantitative Strategies for Resolution Making’ by Anand Sharma (IIT Delhi)



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