2.  Marketing Application  Media Selection  Financial Application  Portfolio Selection  Financial Planning  Product Management Application  Product Scheduling  Data Envelopment Analysis  Revenue Management

3. LP Modeling Application For a particular application we begin with the problem scenario and data, then: 1) Define the decision variables 2) Formulate the LP model using the decision variables Write the objective function equation Write each of the constraint equations Implement the Model using QM or MS

4.  Helps marketing manager to allocate the advertising budget to various advertising media  News Paper  TV  Internet  Magazine  Radio

5.  A Construction Company wants to advertise his new project and hired an advertising company.  The advertising budget for first month campaign is $30,000  Other Restrictions:  At least 10 television commercial must be used  At least 50,000 potential customer must be reached  No more than $18000 may be spent on TV advertisement  Need to recommend an advertising selection media plan

6. PLAN DECISION CRETERIA EXPOSURE QUALITY It is a measure of the relative value of advertisement in each of media. It is measured in term of an exposure quality unit. Potential customers Reached

7. We can use the graph of an LP to see what happens when: 1. An OFC changes, or 2. A RHS changes Recall the Flair Furniture problem ADVERTISING MEDIA # OF CUSTOMER REACHED COST PER ADVERTISMEN T MAX TIME AVAIALBLE PER MONTH EXPOSURE QUALITY UNITS DAY TIME TV(1 MIN) 100015001565 EVENING TV (30 SEC) 200030001090 DAILY NEWS PAPER 15004002540 SUNDAY NEWS PAPER 25001000460 RADIO 8 AM TO 5 PM NEWS 30 SEC 3001003020

8.  DTV : # of Day time TV is used  ETV: # of times evening TV is used  DN: # of times daily news paper used  SN: # of time Sunday news paper is used  R: # of time Radio is used  Advertising plan with DTV =65 DTV Quality unit  Advertising plan with ETV =90 DTV Quality unit  Advertising plan with DN =40 DTV Quality unit  Objective Function ????

9.  Max 65DTV + 90ETV + 40DN + 60SN + 20R (Exposure quality )  Constraints  Availability of Media  Budget Constraint  Television Restriction

10.  Availability of Media  DTV <=15  ETV <=10  DN<=25  SN<=4  R<=30  Budget constraints  1500DTV +3000ETV +400DN +1000SN +100R <=30,000  Television Restriction  DTV +ETV >=10  1500DTV +3000ETV<=18000  1000DTV+2000ETV+1500DN +2500SN +300R >=50,000

11.  OBJ FUNCTION Value: 2370 (Exposure Quality unit)  Decision variable  Potential customers  ???? MEDIAFREQUENCY DTV10 ETV0 DN25 SN2 RADIO30

12.  dtvetvdnsn rRHS dual  Maximize6590406020  Constraint 110000<=150  Constraint 201000<=100  Constraint 300100<=2516  Constraint 400010<=40  Constraint 500001<=3014  Constraint 6150030004001000100<=300000.06  Constraint 711000>=10-25  Constraint 815003000000<=180000  Constraint 91000200015002500300>=500000  Solution->100251.99999930 $2,370.

13.  Dual Price for constraint 3 is 16 ????  (DN >=25) exposure quality unit ????  Dual price for constraint 5 is 14  (R <=30) exposure quality unit ????  Dual price for constraint 6 is 0.060  1500DTV +3000ETV +400DN +1000SN +100R <=30,000 exposure quality unit ????  Dual price for constraint 7 is -25  DTV +ETV >=10 ???

14.  Reducing the TV commercial by 1 will increase the quality unit by 25 this means  The reducing the requirement having at least 10 TV commercial should be reduced

15.  Portfolio Selection  1.A company wants to invest $100,000 either in oil, steel or govt industry with following guidelines:  2.Neither industry (oil or steel ) should receive more than $50,000  3.Govt bonds should be at least 25% of the steel industry investment  4.The investment in pacific oil cannot be more than 60% of total oil industry.  What portfolio recommendations investments and amount should be made for available $100,000

16.  Decision Variables  A = $ invested in Atlantic Oil  P= $ invested in Pacific Oil  M= $ invested in Midwest Steel  H = $ invested in Huber Steel  G = $ invested in govt bonds  Objective function ???? InvestmentProjected Rate of Return % Atlantic oil7.3% Pacific oil10.3% Midwest steel6.4% Huber Steel7.5% Govt Bonds4.5%

17.  Max 0.073A + 0.103P + 0.064M + 0.075H + 0.045G  1.A+P+M+H+G=100000  2.A+P <=50,000, M+H <= 50,000  3. G>=0.25(M + H) or G -0.25M -0.25 H>=0  4. P<=0.60(A+P) or -0.60A +0.40P<=0

18.  Objective Function=8000 VariableValueReduced Cost A200000.00 P300000.00 M 0.011 H400000.00 G100000.00 ConstraintSlack/surplusDual price 100.069 200.022 3100000.00 40-0.024 500.030 InvestmentAmountExpected Annual Return A$20,000$1460 P$30,000$3090 H40,000$3000 G$10,000$450 Total$100000$8000 Overall Return ????

19.  Dual price for constraint 3 is zero increase in steel industry maximum will not improve the optimal solution hence it is not binding constraint.,  Others are binding constraint as dual prices are zero  For constrain 1 0.069 value of optimal solution will increase by 0.069 if one more dollar is invested.  A negative value for constrain 4 is -0.024 which mean optimal solution get worse by 0.024 if one unit on RHS of constrain is increased. What does this mean

20.  If one more dollar is invested in govt bonds the total return will decrease by $0.024 Why???  Marginal Return by constraint 1 is 6.9%  Average Return is 8%  Rate of return on govt bond is 4.5%/

21.  Associated reduced cost for M=0.011 tells  Obj function coefficient of for midwest steel should be increase by 0.011 before considering it to be advisable alternative.  With such increase 0.064 +0.011 =0.075 making this as desirable as Huber steel investment.

22.  It is an application of the linear programming model used to measure the relative efficiency of the operating units with same goal and objectives.  Fast Food Chain  Target inefficient outlets that should be targeted for further study  Relative efficiency of the Hospital, banks,courts and so on

23.  General Hospital; University Hospital  County Hospital; State Hospital  Input Measure  # of full time equivalent (FTE) nonphysician personnel  Amount spent on supplies  # of bed-days available  Output Measures  Patient-days of service under Medicare  Patient-days of service notunder Medicare  # of nurses trained  # of interns trained

24. Input MeasureGeneralUniversityCountyState FTE285.50162.30275.70210.40 Supply Expense123.80128.70348.50154.10 Bed-days available106.7264.21104.10104.04 ANNUAL SERVICES PROVIDED BY FOUR HOSPITALS Output MeasureGeneralUniversityCountyState Medicare patient days 48.1434.6236.7233.16 Non-Medicare patient days 43.1027.1145.9856.46 Nurses Trained253148175160 Interns trained41272384

25.  Construct a hypothetical composite Hospital  Output & inputs of composite hospital is determined by computing the average weight of corresponding output & input of four hospitals.  Constraint Requirement  All output of the Composite hospital should be greater than or equal to outputs of County Hospital  If composite output produce same or more output with relatively less input as compared to county hospital than composite hospital is more efficient and county hospital will be considered as inefficient.

26.  Wg= weight applied to inputs and output for general hospital  Wu = weight applied to input & output for University Hospital  Wc=weight applied to input & output for County Hospital  Ws = weight applied to input and outputs for state hospital

27.  Constraint 1  Wg+ wu + wc + ws=1  Output of Composite Hospital  Medicare: 48.14wg + 34.62wu + 36.72wc+ 33.16ws  Non- Medicare:43.10wg+27.11wu+45.98wc+54.46ws  Nurses:253wg+148wu+175wc+160ws  Interns:41wg+27wu+23wc+84ws

28.  Constraint 2:  Output for Composite Hospital >=Output for County Hospital  Medicare: 48.14wg + 34.62wu + 36.72wc+ 33.16ws >=36.72  Non- Medicare:43.10wg+27.11wu+45.98wc+54.46ws> =45.98  Nurses:253wg+148wu+175wc+160ws >=175  Interns:41wg+27wu+23wc+84ws >=23

29.  Constraint 3  Input for composite Hospital <=Resource available to Composite Hospital  FTE:285.20wg+162.30wu+275.70wc+210.40ws  Sup:123.80wg+128.70wu+348.50wc+154.10ws  Bed-dys:106.72wg+64.21wu+104.10wc+104.04ws  We need a value for RHS:  %tage of input values for county Hospital.

30.  E= Fraction of County Hospital ‘s input available to composite hospital  Resources to Composite Hospital= E*Resources to County Hospital  If E=1 then ???  If E> 1 then Composite Hospital would acquire more resources than county  If E <1 ….

31.  FTE:285.20wg+162.30wu+275.70wc+210ws<=275.70E  SUP:123.80wg+128.70wu+348.50wc+154.10ws<=348. 50E  Beddays:106.72wg+64.21wu+104.10wc+104.04ws<=10 4.10E  If E=1 composite hospital=county hospital there is no evidence county hospital is inefficient  If E <1 composite hospital require less input to obtain output achieved by county hospital hence county hospital is more inefficient,.

32.  Min E  Wg+wu+wc+ws=1  48.14wg + 34.62wu + 36.72wc+ 33.16ws >=36.72  43.10wg+27.11wu+45.98wc+54.46ws>=45.98  253wg+148wu+175wc+160ws >=175  41wg+27wu+23wc+84ws >=23  285.20wg+162.30wu+275.70wc+210.40ws-275.70E <=0  123.80wg+128.70wu+348.50wc+154.10ws-348.50E <=0  106.72wg+64.21wu+104.10wc+104.04ws-104.10E <=0

33. VariableValueReduced cost E0.9050 WG0.2120 WU0.2600 WC0.000.95 WS0.5270 ConstraintSlack/Sur plus Dual Price 100.239 20-0.014 30 41.6150.0 537.0270.0 635.8240.0 7174.4220.0 80.000.010 Composite Hospital as much of as each output as County Hospital (constrain 2-5) but provides 1.6 more trained nurses and 37 more interim. Contraint 6 and 7 are for input which means that Composite hospital used less than 90.5 of resources of FTE and supplies

34.  E=0.905  Efficiency score of County Hospital is 0.905  Composite hospital need 90.5% of resources to produce the same output of County Hospital hence it is efficient than county hospital. and county hospital is relatively inefficient  Wg=0.212;Wu=0.26;Ws=0.527.