SlideShare a Scribd company logo
1 of 9
Download to read offline
Course Instructor: Dr. Swati Singh
                 Course: BBA III
             Amity Business School
Linear Programming Problem
 LPP is a mathematical modeling technique, used to
 determine a level of operational activity in order to
 achieve an objective, subject to restrictions.

 It is a mathematical modeling technique, useful for
  economic allocation of ‘scarce’ or ‘limited’ resources
  like labor, material, machine, time, space, energy etc. to
  several competing activities like product, services, jobs
  etc. on the basis of a given criterion of optimality.
LPP Consists of:
 Decision Variables: Decision to produce no. of units of
  different items.
 Objective Function: Linear mathematical relationship
  used to describe objective of an operation in terms of
  decision variables.
 Constraints: Restrictions placed on decision situation
  by operating environment.
 Feasible Solution: Any solution of general LPP which
  also satisfies non negative restrictions.
 Optimum Solution : The feasible solution which
  optimizes the objective function.
General Structure of LPP
 Maximize (or Minimize) Z = c1x1 + c2x2 + ---- + cnxn
 Subject to,
     a11x1 + a12x2 + -------- + a1nxn   (≤, =, ≥ ) b1

     a11x1 + a12x2 + -------- + a1nxn   (≤, =, ≥ ) b2




     an1x1 + an2x2 + -------- + annxn     (≤, =, ≥ ) bn


     where, x1 ≥ 0, x2 ≥ 0 ---- xn ≥ 0
Question 1.
A dealer wishes to purchase a no. of fans and Air
Conditioners. He has only Rs. 5760 to invest & space for
at most 20 items.
      A fan costs him Rs. 360 & AC Rs. 240. His
expectation is that he can sell a fan at a profit of Rs. 22 &
AC at profit of Rs. 18.
      Assuming he can sell all items he can buy, how
should he invest money in order to maximize his profits?
Solution 1. purchases x1 Fans & x2 ACs.
 Let us suppose, dealer
        Since no. of fans & ACs can’t be negative
        So, x1 ≥ 0, x2 ≥ 0
 Since cost of fan = Rs. 360 & AC = Rs. 240
        & Total money to be invested = Rs. 5760
        Thus, 360 x1 + 240 x2 ≤ 5760
 Also, space is for at most 20 items
        So, x1 + x2 ≤ 20
 Again, if dealer can sell all his items
        Profit is Z = 22 x1 + 18 x2, which is to be maximized
Thus, the required LPP is:
        Maximize Z = 22 x1 + 18 x2
Subject to Constraints,
       360 x1 + 240 x2 ≤ 5760
       x1 + x2 ≤ 20
      & x1 ≥ 0, x2 ≥ 0
Question 2.
       A company produces two articles R & S. Processing
is done through assembly & finishing departments. The
potential capacity of the assembly department is 60 hrs. a
week & that of finishing department is 48 hrs. a week.
       Production of one unit of R requires 4 hrs. in
assembly & 2hrs. in finishing.
       Each of the unit S requires 2 hrs. in assembly & 4hrs.
in finishing.
       If profit is Rs. 8 for each unit of R & Rs. 6 for each
unit of S. Find out the no. of units of R & S to be produced
each week to give maximum profit.
Solution 2.
       Products       Time Required for Producing One         Total hrs.
                                   Unit                       available

                           x1                x2
  Assembly Dept.
                           4                 2                   60
  Finishing Dept.
                           2                 4                   48
        Profit
                          Rs. 8             Rs. 6

      Objective Function: Max. Z = 8x1 + 6x2
Subject to Constraints,
       4 x1 + 2 x2 ≤ 60 (Time available in assembly dept.)
       2 x1 + 4 x2 ≤ 48 (Time available in finishing dept.)
where, x1 ≥ 0, x2 ≥ 0

More Related Content

What's hot

An Introduction to Linear Programming
An Introduction to Linear ProgrammingAn Introduction to Linear Programming
An Introduction to Linear ProgrammingMinh-Tri Pham
 
Applications of linear programming
Applications of linear programmingApplications of linear programming
Applications of linear programmingZenblade 93
 
Linear Programming Feasible Region
Linear Programming Feasible RegionLinear Programming Feasible Region
Linear Programming Feasible RegionVARUN MODI
 
Linear programming
Linear programmingLinear programming
Linear programmingBiplob Deb
 
beyond linear programming: mathematical programming extensions
beyond linear programming: mathematical programming extensionsbeyond linear programming: mathematical programming extensions
beyond linear programming: mathematical programming extensionsAngelica Angelo Ocon
 
Linear Programming Quiz Solution
Linear Programming Quiz SolutionLinear Programming Quiz Solution
Linear Programming Quiz SolutionEd Dansereau
 
Chapter 3 linear programming
Chapter 3   linear programmingChapter 3   linear programming
Chapter 3 linear programmingsarkissk
 
Integer Programming, Gomory
Integer Programming, GomoryInteger Programming, Gomory
Integer Programming, GomoryAVINASH JURIANI
 
Linear programming graphical method (feasibility)
Linear programming   graphical method (feasibility)Linear programming   graphical method (feasibility)
Linear programming graphical method (feasibility)Rajesh Timane, PhD
 
Linear programming - Model formulation, Graphical Method
Linear programming  - Model formulation, Graphical MethodLinear programming  - Model formulation, Graphical Method
Linear programming - Model formulation, Graphical MethodJoseph Konnully
 
Linear programming
Linear programmingLinear programming
Linear programmingTarun Gehlot
 
DAM assignment - LPP formulation, Graphical solution and Simplex Method
DAM assignment - LPP formulation, Graphical solution and Simplex MethodDAM assignment - LPP formulation, Graphical solution and Simplex Method
DAM assignment - LPP formulation, Graphical solution and Simplex MethodNeha Kumar
 

What's hot (20)

An Introduction to Linear Programming
An Introduction to Linear ProgrammingAn Introduction to Linear Programming
An Introduction to Linear Programming
 
Applications of linear programming
Applications of linear programmingApplications of linear programming
Applications of linear programming
 
Linear Programming Feasible Region
Linear Programming Feasible RegionLinear Programming Feasible Region
Linear Programming Feasible Region
 
Workload balancing
Workload balancingWorkload balancing
Workload balancing
 
Palash badal
Palash badalPalash badal
Palash badal
 
Lenier Equation
Lenier EquationLenier Equation
Lenier Equation
 
Lp graphical and simplexx892
Lp graphical and simplexx892Lp graphical and simplexx892
Lp graphical and simplexx892
 
Decision making
Decision makingDecision making
Decision making
 
Lect or1 (2)
Lect or1 (2)Lect or1 (2)
Lect or1 (2)
 
Linear programming
Linear programmingLinear programming
Linear programming
 
beyond linear programming: mathematical programming extensions
beyond linear programming: mathematical programming extensionsbeyond linear programming: mathematical programming extensions
beyond linear programming: mathematical programming extensions
 
Linear Programming Quiz Solution
Linear Programming Quiz SolutionLinear Programming Quiz Solution
Linear Programming Quiz Solution
 
Chapter 3 linear programming
Chapter 3   linear programmingChapter 3   linear programming
Chapter 3 linear programming
 
Integer Programming, Gomory
Integer Programming, GomoryInteger Programming, Gomory
Integer Programming, Gomory
 
Linear programming graphical method (feasibility)
Linear programming   graphical method (feasibility)Linear programming   graphical method (feasibility)
Linear programming graphical method (feasibility)
 
Linear programming - Model formulation, Graphical Method
Linear programming  - Model formulation, Graphical MethodLinear programming  - Model formulation, Graphical Method
Linear programming - Model formulation, Graphical Method
 
LINEAR PROGRAMING
LINEAR PROGRAMINGLINEAR PROGRAMING
LINEAR PROGRAMING
 
Linear programming
Linear programmingLinear programming
Linear programming
 
Flair furniture LPP problem
Flair furniture LPP problemFlair furniture LPP problem
Flair furniture LPP problem
 
DAM assignment - LPP formulation, Graphical solution and Simplex Method
DAM assignment - LPP formulation, Graphical solution and Simplex MethodDAM assignment - LPP formulation, Graphical solution and Simplex Method
DAM assignment - LPP formulation, Graphical solution and Simplex Method
 

Similar to 7b3ba module ii lpp_part i- formulation (a)

Mathematics For Management CHAPTER THREE PART I.PPT
Mathematics For Management  CHAPTER THREE PART I.PPTMathematics For Management  CHAPTER THREE PART I.PPT
Mathematics For Management CHAPTER THREE PART I.PPTAYNETUTEREFE1
 
LPP Graphical.ppt
LPP Graphical.pptLPP Graphical.ppt
LPP Graphical.pptcutmolp
 
Operational Research
Operational ResearchOperational Research
Operational ResearchBrendaGaytan6
 
Lecture - Linear Programming.pdf
Lecture - Linear Programming.pdfLecture - Linear Programming.pdf
Lecture - Linear Programming.pdflucky141651
 
Mathematical formulation of lpp- properties and example
Mathematical formulation of lpp- properties and exampleMathematical formulation of lpp- properties and example
Mathematical formulation of lpp- properties and exampleSundar B N
 
Linear programming
Linear programmingLinear programming
Linear programminggoogle
 
Bba 3274 qm week 10 integer programming
Bba 3274 qm week 10 integer programmingBba 3274 qm week 10 integer programming
Bba 3274 qm week 10 integer programmingStephen Ong
 
Integer Programming PPt.ernxzamnbmbmspdf
Integer Programming PPt.ernxzamnbmbmspdfInteger Programming PPt.ernxzamnbmbmspdf
Integer Programming PPt.ernxzamnbmbmspdfRaja Manyam
 
introduction to Operation Research
introduction to Operation Research introduction to Operation Research
introduction to Operation Research amanyosama12
 
OR PPT 4 (Sensitivity Analysis) 1.pdf
OR PPT 4 (Sensitivity Analysis) 1.pdfOR PPT 4 (Sensitivity Analysis) 1.pdf
OR PPT 4 (Sensitivity Analysis) 1.pdfishikaSharma576762
 
Chapter 2 Linear Programming for business (1).pptx
Chapter 2 Linear Programming for business (1).pptxChapter 2 Linear Programming for business (1).pptx
Chapter 2 Linear Programming for business (1).pptxanimutsileshe1
 

Similar to 7b3ba module ii lpp_part i- formulation (a) (20)

Mathematics For Management CHAPTER THREE PART I.PPT
Mathematics For Management  CHAPTER THREE PART I.PPTMathematics For Management  CHAPTER THREE PART I.PPT
Mathematics For Management CHAPTER THREE PART I.PPT
 
Chapter two
Chapter twoChapter two
Chapter two
 
LPP Graphical.ppt
LPP Graphical.pptLPP Graphical.ppt
LPP Graphical.ppt
 
Operational Research
Operational ResearchOperational Research
Operational Research
 
Lecture - Linear Programming.pdf
Lecture - Linear Programming.pdfLecture - Linear Programming.pdf
Lecture - Linear Programming.pdf
 
P
PP
P
 
Mathematical formulation of lpp- properties and example
Mathematical formulation of lpp- properties and exampleMathematical formulation of lpp- properties and example
Mathematical formulation of lpp- properties and example
 
Linear programming
Linear programmingLinear programming
Linear programming
 
Integer programming
Integer programmingInteger programming
Integer programming
 
Bba 3274 qm week 10 integer programming
Bba 3274 qm week 10 integer programmingBba 3274 qm week 10 integer programming
Bba 3274 qm week 10 integer programming
 
Integer Programming PPt.ernxzamnbmbmspdf
Integer Programming PPt.ernxzamnbmbmspdfInteger Programming PPt.ernxzamnbmbmspdf
Integer Programming PPt.ernxzamnbmbmspdf
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
 
Linear Programming
Linear ProgrammingLinear Programming
Linear Programming
 
introduction to Operation Research
introduction to Operation Research introduction to Operation Research
introduction to Operation Research
 
Operations research
Operations researchOperations research
Operations research
 
OR PPT 4 (Sensitivity Analysis) 1.pdf
OR PPT 4 (Sensitivity Analysis) 1.pdfOR PPT 4 (Sensitivity Analysis) 1.pdf
OR PPT 4 (Sensitivity Analysis) 1.pdf
 
Chapter 2 Linear Programming for business (1).pptx
Chapter 2 Linear Programming for business (1).pptxChapter 2 Linear Programming for business (1).pptx
Chapter 2 Linear Programming for business (1).pptx
 
LPP.pptx
LPP.pptxLPP.pptx
LPP.pptx
 

Recently uploaded

Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxnull - The Open Security Community
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Neo4j
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentationphoebematthew05
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfjimielynbastida
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024BookNet Canada
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsAndrey Dotsenko
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April  Automation LPDGAPIForce Zurich 5 April  Automation LPDG
APIForce Zurich 5 April Automation LPDGMarianaLemus7
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 

Recently uploaded (20)

Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentation
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdf
 
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Panjabi Bagh 🔝 9953056974 🔝 Delhi escort Service
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April  Automation LPDGAPIForce Zurich 5 April  Automation LPDG
APIForce Zurich 5 April Automation LPDG
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping Elbows
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 

7b3ba module ii lpp_part i- formulation (a)

  • 1. Course Instructor: Dr. Swati Singh Course: BBA III Amity Business School
  • 2. Linear Programming Problem  LPP is a mathematical modeling technique, used to determine a level of operational activity in order to achieve an objective, subject to restrictions.  It is a mathematical modeling technique, useful for economic allocation of ‘scarce’ or ‘limited’ resources like labor, material, machine, time, space, energy etc. to several competing activities like product, services, jobs etc. on the basis of a given criterion of optimality.
  • 3. LPP Consists of:  Decision Variables: Decision to produce no. of units of different items.  Objective Function: Linear mathematical relationship used to describe objective of an operation in terms of decision variables.  Constraints: Restrictions placed on decision situation by operating environment.  Feasible Solution: Any solution of general LPP which also satisfies non negative restrictions.  Optimum Solution : The feasible solution which optimizes the objective function.
  • 4. General Structure of LPP  Maximize (or Minimize) Z = c1x1 + c2x2 + ---- + cnxn  Subject to, a11x1 + a12x2 + -------- + a1nxn (≤, =, ≥ ) b1 a11x1 + a12x2 + -------- + a1nxn (≤, =, ≥ ) b2 an1x1 + an2x2 + -------- + annxn (≤, =, ≥ ) bn where, x1 ≥ 0, x2 ≥ 0 ---- xn ≥ 0
  • 5.
  • 6. Question 1. A dealer wishes to purchase a no. of fans and Air Conditioners. He has only Rs. 5760 to invest & space for at most 20 items. A fan costs him Rs. 360 & AC Rs. 240. His expectation is that he can sell a fan at a profit of Rs. 22 & AC at profit of Rs. 18. Assuming he can sell all items he can buy, how should he invest money in order to maximize his profits?
  • 7. Solution 1. purchases x1 Fans & x2 ACs.  Let us suppose, dealer Since no. of fans & ACs can’t be negative So, x1 ≥ 0, x2 ≥ 0  Since cost of fan = Rs. 360 & AC = Rs. 240 & Total money to be invested = Rs. 5760 Thus, 360 x1 + 240 x2 ≤ 5760  Also, space is for at most 20 items So, x1 + x2 ≤ 20  Again, if dealer can sell all his items Profit is Z = 22 x1 + 18 x2, which is to be maximized Thus, the required LPP is: Maximize Z = 22 x1 + 18 x2 Subject to Constraints, 360 x1 + 240 x2 ≤ 5760 x1 + x2 ≤ 20 & x1 ≥ 0, x2 ≥ 0
  • 8. Question 2. A company produces two articles R & S. Processing is done through assembly & finishing departments. The potential capacity of the assembly department is 60 hrs. a week & that of finishing department is 48 hrs. a week. Production of one unit of R requires 4 hrs. in assembly & 2hrs. in finishing. Each of the unit S requires 2 hrs. in assembly & 4hrs. in finishing. If profit is Rs. 8 for each unit of R & Rs. 6 for each unit of S. Find out the no. of units of R & S to be produced each week to give maximum profit.
  • 9. Solution 2. Products Time Required for Producing One Total hrs. Unit available x1 x2 Assembly Dept. 4 2 60 Finishing Dept. 2 4 48 Profit Rs. 8 Rs. 6 Objective Function: Max. Z = 8x1 + 6x2 Subject to Constraints, 4 x1 + 2 x2 ≤ 60 (Time available in assembly dept.) 2 x1 + 4 x2 ≤ 48 (Time available in finishing dept.) where, x1 ≥ 0, x2 ≥ 0