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School: City University Of Hong Kong
Course: Forecasting Methods For Business
tt, tJQ A~ ~n )S. At4r[ ~kcJlfllcfw_>dd ~ I ;~ 1lue / ttl tcfw_;v)O~tJ M (';~ IvvILPl hJ : t 576/ IAtfb 7g/ h, cfw_' &1'rC (o;fi I7fl A QOtr 7'b d&WYI \ffA'd fJ(ow~ cuvcfw_ ( &i: "7tl L 'r R ~cfw_ Ol:t . tCL I , s I. eA. ~ f~ I e. l 90 b Ac
School: City University Of Hong Kong
Course: Forecasting Methods For Business
CITY UNIVERSITY OF HONG KONG _ Course code & title : MS4102 Business Forecasting Methods Session : Semester B, 2009/2010 Time allowed : Two hours This paper has 17 pages (including this page) Materials, aids and instruments permitted to be used during exa
School: City University Of Hong Kong
Course: Forecasting Methods For Business
Chapter Topics Multiple regression Autocorrelation Regression Method Slide 2 1 Regression Methods Simple Linear Regression To forecast an outcome (response variable, dependent variable) of a study based on a certain number of factors (explanatory varia
School: City University Of Hong Kong
Course: Forecasting Methods For Business
="b~t~_ 11L~dJee _tA~ _  ~ ~(l2 , L _ .e  k  11 )"G,=)LT)~\_~ ;ijV~t .+ ~ri: 2_"'  G +*'~f4   r~~ +1b Lbj ! .Je' >l\~     . .  10 .>c(e<,?~ l~ by, b(eS.>'cfw_lf.t;. L 1Lt; J~ ~ ~ "1"t\2:. ~k!E~ ~~h:kc_QofLt'~..
School: City University Of Hong Kong
Course: Forecasting Methods For Business
? ,~,~,~ . > '$~b '1"'6 ' .2:,;,'. ,:,.t,<,\> If 1f' . M S6215: F orecastinq M ethods f or B usiness M idsession t est 1 ) W hat i s t he v alue o f h i n e xpression ( l.l)? 2 ) How m any u nknown p arameters a re t here i n ( 1.1) a nd w hat a re t
School: City University Of Hong Kong
Course: Quantitative Methods
Classical Network Models Social Network MS5211: Quantitative Methods Lecture 8: Network Models Lecture 8: Network Models Quantitative Methods Classical Network Models Social Network 1 Classical Network Models 2 Social Network Lecture 8: Network Models Qua
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Dynamic Programming Decision Making Under Uncertainty and Risks MS5211: Quantitative Methods Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Quanti
School: City University Of Hong Kong
Course: Quantitative Methods
Transportation Models The Assignment Problem and Transshipment Models Special Situations and Assignment Algorithm MS5211: Quantitative Methods Lecture 4: Transportation and Assignment Models Lecture 4: Transportation and Assignment Models Quantitative Met
School: City University Of Hong Kong
Course: Quantitative Methods
More about Inventory Control Revenue Management MS5211: Quantitative Methods Lecture 7: Revenue Management Lecture 7: Revenue Management Quantitative Methods More about Inventory Control Revenue Management 1 More about Inventory Control 2 Revenue Manageme
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Formulation and Assumptions Solution Methods Four Special Cases in LP and Sensitivity Analysis MS5211: Quantitative Methods Lecture 2: Introduction to Linear Programming Lecture 2: Introduction to Linear Programming Quantitative Methods Basic Formul
School: City University Of Hong Kong
Course: Quantitative Methods
Integer Programming Goal Programming Nonlinear Programming MS5211: Quantitative Methods Lecture 5: Integer Programming and Nonlinear Programming Lecture 5: Integer Programming and Nonlinear Programming Quantitative Methods Integer Programming Goal Progra
School: City University Of Hong Kong
Course: Forecasting Methods For Business
tt, tJQ A~ ~n )S. At4r[ ~kcJlfllcfw_>dd ~ I ;~ 1lue / ttl tcfw_;v)O~tJ M (';~ IvvILPl hJ : t 576/ IAtfb 7g/ h, cfw_' &1'rC (o;fi I7fl A QOtr 7'b d&WYI \ffA'd fJ(ow~ cuvcfw_ ( &i: "7tl L 'r R ~cfw_ Ol:t . tCL I , s I. eA. ~ f~ I e. l 90 b Ac
School: City University Of Hong Kong
Course: Forecasting Methods For Business
CITY UNIVERSITY OF HONG KONG _ Course code & title : MS4102 Business Forecasting Methods Session : Semester B, 2009/2010 Time allowed : Two hours This paper has 17 pages (including this page) Materials, aids and instruments permitted to be used during exa
School: City University Of Hong Kong
Course: Forecasting Methods For Business
="b~t~_ 11L~dJee _tA~ _  ~ ~(l2 , L _ .e  k  11 )"G,=)LT)~\_~ ;ijV~t .+ ~ri: 2_"'  G +*'~f4   r~~ +1b Lbj ! .Je' >l\~     . .  10 .>c(e<,?~ l~ by, b(eS.>'cfw_lf.t;. L 1Lt; J~ ~ ~ "1"t\2:. ~k!E~ ~~h:kc_QofLt'~..
School: City University Of Hong Kong
Course: Forecasting Methods For Business
? ,~,~,~ . > '$~b '1"'6 ' .2:,;,'. ,:,.t,<,\> If 1f' . M S6215: F orecastinq M ethods f or B usiness M idsession t est 1 ) W hat i s t he v alue o f h i n e xpression ( l.l)? 2 ) How m any u nknown p arameters a re t here i n ( 1.1) a nd w hat a re t
School: City University Of Hong Kong
Course: Forecasting Methods For Business
G 1h ( 0 sf:, ( ~= 20 / 2 & I A ) wl~/\j (!/l~g CiliM3 (0 :=: A.I" A,.? 2 0 LwlKMW\,\ plMa~e.s 2) )A 2 0 U,Vl~W't\ p ~Ws. 20 observa;cfw_flM.,) / Rl . ~&., CortesrflvvJ.~ /' f7 of sq  tk LoJ+!i~eJ " e.)(f(aA~tt b<J ~ bCts'l~ If. of Gjdu p~ obstzrv~
School: City University Of Hong Kong
School: City University Of Hong Kong
FB2201 Management Sciences II (Sem B, 2010 2011) Assignment Due on Week 12 Tutorial Farmer Jones must determine whether to plant corn or wheat. If he plants corn and the weather is warm, he earns $800000; if he plants corn and the weather is cold, he earn
School: City University Of Hong Kong
Course: Pms
Topic 1: Linear Programming Reference Solution Chapter 4 Q10 a) Let Xi be the number of cars of type i; where i=1 (Sedan), 2 (Hatchback), 3 (Minivan) Minimize Z = 12500 X1 + 8500X2 + 13700X3 Subject to: 10500 X1 + 9500 X2 + 12300 X3 = 2800000 (Note: In o
School: City University Of Hong Kong
Course: Pms
Supplementary Exercises on Linear Programming Sensitivity Analysis Question 1 The Win Big Gambling Club promotes gambling junkets from a large Midwestern city to casinos in the Bahamas. The club has budgeted up to $8,000 per week for local advertising. Th
School: City University Of Hong Kong
Course: Pms
Topic 1: Linear Programming Reference Solution Chapter 3 Q25 Let x1 = no. of telephone interviewers, Minimize Z = 50x1 + 70x2 subject to 80x1 + 40x2 3,000 80x1 1,000 40x2 800 x 1, x 2 0 (minimum number of interviews) (minimum number of interviews by tele
School: City University Of Hong Kong
Course: Pms
Transportation, Transshipment, and Assignment (TTA) Chapter Outline To examine special types of LP problems: Transportation Problem Transshipment Problem Assignment Problem Chapter 6 1 2 Transportation Model: Characteristics (1 of 2) Consider the transp
School: City University Of Hong Kong
Course: Forecasting Methods For Business
tt, tJQ A~ ~n )S. At4r[ ~kcJlfllcfw_>dd ~ I ;~ 1lue / ttl tcfw_;v)O~tJ M (';~ IvvILPl hJ : t 576/ IAtfb 7g/ h, cfw_' &1'rC (o;fi I7fl A QOtr 7'b d&WYI \ffA'd fJ(ow~ cuvcfw_ ( &i: "7tl L 'r R ~cfw_ Ol:t . tCL I , s I. eA. ~ f~ I e. l 90 b Ac
School: City University Of Hong Kong
Course: Forecasting Methods For Business
CITY UNIVERSITY OF HONG KONG _ Course code & title : MS4102 Business Forecasting Methods Session : Semester B, 2009/2010 Time allowed : Two hours This paper has 17 pages (including this page) Materials, aids and instruments permitted to be used during exa
School: City University Of Hong Kong
Course: Forecasting Methods For Business
Chapter Topics Multiple regression Autocorrelation Regression Method Slide 2 1 Regression Methods Simple Linear Regression To forecast an outcome (response variable, dependent variable) of a study based on a certain number of factors (explanatory varia
School: City University Of Hong Kong
Course: Forecasting Methods For Business
="b~t~_ 11L~dJee _tA~ _  ~ ~(l2 , L _ .e  k  11 )"G,=)LT)~\_~ ;ijV~t .+ ~ri: 2_"'  G +*'~f4   r~~ +1b Lbj ! .Je' >l\~     . .  10 .>c(e<,?~ l~ by, b(eS.>'cfw_lf.t;. L 1Lt; J~ ~ ~ "1"t\2:. ~k!E~ ~~h:kc_QofLt'~..
School: City University Of Hong Kong
Course: Forecasting Methods For Business
? ,~,~,~ . > '$~b '1"'6 ' .2:,;,'. ,:,.t,<,\> If 1f' . M S6215: F orecastinq M ethods f or B usiness M idsession t est 1 ) W hat i s t he v alue o f h i n e xpression ( l.l)? 2 ) How m any u nknown p arameters a re t here i n ( 1.1) a nd w hat a re t
School: City University Of Hong Kong
Course: Forecasting Methods For Business
G 1h ( 0 sf:, ( ~= 20 / 2 & I A ) wl~/\j (!/l~g CiliM3 (0 :=: A.I" A,.? 2 0 LwlKMW\,\ plMa~e.s 2) )A 2 0 U,Vl~W't\ p ~Ws. 20 observa;cfw_flM.,) / Rl . ~&., CortesrflvvJ.~ /' f7 of sq  tk LoJ+!i~eJ " e.)(f(aA~tt b<J ~ bCts'l~ If. of Gjdu p~ obstzrv~
School: City University Of Hong Kong
Course: Forecasting Methods For Business
. . _   _._ . . _._ . . _ _. . _.  ._.    . _ _    .__ ._ . _ _. _ . _ . S1 Dh5';rj. M('!j\l'."l~ .tbf/.C. (0 b_ J )!i.o,r"'O'ilL c4 Acfw_(.r~Jo.t't '/ ) . r"\ 'I 6 .kctr cfw_I\ 0\1\'7 C hgJ. ~ ~ren\se ~\!.,1;\/:. : ct1.t?, Lf 't/4S1 00;:
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession Test 2008 Question 1 The following table gives the incidents of infant mortality for a developing African country in 2005  2007 (thousand of deaths). A general Fourier model has been fitted to the data
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 19 October 2010 Time allowed: 70 minutes Question 1 (45 marks) The following SAS program performs Fourier Series analysis based on 48 observations on the change in S&P GSCI Crude Oil Price (labeled
School: City University Of Hong Kong
Course: Forecasting Methods For Business
Chapter Topics Exponential Smoothing Methods Introduction to exponential smoothing Simple Exponential Smoothing Holts Trend Corrected Exponential Smoothing HoltWinters Methods Multiplicative HoltWinters method Additive HoltWinters method Slide 2 1 Mo
School: City University Of Hong Kong
Course: Forecasting Methods For Business
Types of Data Time series data: a sequence of observations measured over time (usually at equally spaced intervals, e.g., weekly, monthly and annually). Examples of time series data include: Gross Domestic Product each quarter; annual rainfall; daily stoc
School: City University Of Hong Kong
Course: Forecasting Methods For Business
Fourier Series Analysis Suitable for modelling seasonality and/or cyclicalness Fourier Series (Spectral) Analysis Identifying peaks and troughs 1 A sine wave is a repeating pattern that goes through one cycle every 2 (i.e. 2 3.141593 = 6.283186) units of
School: City University Of Hong Kong
Course: Forecasting Methods For Business
BoxJenkins (ARIMA) Models The BoxJenkins methodology refers to a set of procedures for identifying and estimating time series models within the class of autoregressive integrated moving average (ARIMA) models. ARIMA models are regression models that use
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 16 February 2012, 8:00pm 9:30pm The time series St in Output 1 contains 28 quarterly observations of sales volume (in millions of U.S. dollars) between 2005 Quarter 1 and 2011 Quarter 4. A plot of
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 2013/2014 Semester B Time allowed: 80 minutes Question 1 Consider the following quarterly observations from 2008Q1 to 2010Q4: 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2010Q1 2010Q2 2
School: City University Of Hong Kong
Course: Quantitative Methods
Classical Network Models Social Network MS5211: Quantitative Methods Lecture 8: Network Models Lecture 8: Network Models Quantitative Methods Classical Network Models Social Network 1 Classical Network Models 2 Social Network Lecture 8: Network Models Qua
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Dynamic Programming Decision Making Under Uncertainty and Risks MS5211: Quantitative Methods Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Quanti
School: City University Of Hong Kong
Course: Quantitative Methods
Transportation Models The Assignment Problem and Transshipment Models Special Situations and Assignment Algorithm MS5211: Quantitative Methods Lecture 4: Transportation and Assignment Models Lecture 4: Transportation and Assignment Models Quantitative Met
School: City University Of Hong Kong
Course: Quantitative Methods
More about Inventory Control Revenue Management MS5211: Quantitative Methods Lecture 7: Revenue Management Lecture 7: Revenue Management Quantitative Methods More about Inventory Control Revenue Management 1 More about Inventory Control 2 Revenue Manageme
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Formulation and Assumptions Solution Methods Four Special Cases in LP and Sensitivity Analysis MS5211: Quantitative Methods Lecture 2: Introduction to Linear Programming Lecture 2: Introduction to Linear Programming Quantitative Methods Basic Formul
School: City University Of Hong Kong
Course: Quantitative Methods
Integer Programming Goal Programming Nonlinear Programming MS5211: Quantitative Methods Lecture 5: Integer Programming and Nonlinear Programming Lecture 5: Integer Programming and Nonlinear Programming Quantitative Methods Integer Programming Goal Progra
School: City University Of Hong Kong
Course: Quantitative Methods
What is Quantitative Analysis How to perform Quantitative Analysis Simple Example of Revenue Management MS5211: Quantitative Methods Lecture 1: Introduction Lecture 1: Introduction Quantitative Methods What is Quantitative Analysis How to perform Quantita
School: City University Of Hong Kong
Course: Quantitative Methods
Basics of EOQ Extensions and Modications of EOQ MS5211: Quantitative Methods Lecture 6: Inventory Control Models Lecture 6: Inventory Control Models Quantitative Methods Basics of EOQ Extensions and Modications of EOQ 1 Basics of EOQ 2 Extensions and Modi
School: City University Of Hong Kong
Course: Quantitative Methods
More Applications of Linear Programming Introduction of the Standard Form and Basic Theory of Linear Programming Solving Linear Programming MS5211: Quantitative Methods Lecture 3: Applications and Solution Methods of Linear Programming Lecture 3: Applicat
School: City University Of Hong Kong
FB2201 Management Sciences II Topic 4: Project Management (Solutions) Q13.12 Nicolas Mag is the personnel director of Maian and Manjusri, a company that specializes in consulting and research. One of the training programs that Nicolas is considering for t
School: City University Of Hong Kong
FB2201 Management Sciences II Topic 3: Decision Analysis (Part 2) (Solutions) Q3.33 Jinza Buro is going to help his brother who wants to open a food store. Jinza initially believes that there is a 5050 chance that his brothers food store would be a succe
School: City University Of Hong Kong
FB2201 Management Sciences II Topic 3: Decision Analysis (Part 1) (Solutions) Q3.21 Ziang Zameen has always been proud of his personal investment strategies and has done very well over the past several years. He invests primarily in the stock market. Over
School: City University Of Hong Kong
Course: Quantitative Methods
Classical Network Models Social Network MS5211: Quantitative Methods Lecture 8: Network Models Lecture 8: Network Models Quantitative Methods Classical Network Models Social Network 1 Classical Network Models 2 Social Network Lecture 8: Network Models Qua
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Dynamic Programming Decision Making Under Uncertainty and Risks MS5211: Quantitative Methods Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Lecture 9: Basic Dynamic Programming and Decision Making Under Uncertainty Quanti
School: City University Of Hong Kong
Course: Quantitative Methods
Transportation Models The Assignment Problem and Transshipment Models Special Situations and Assignment Algorithm MS5211: Quantitative Methods Lecture 4: Transportation and Assignment Models Lecture 4: Transportation and Assignment Models Quantitative Met
School: City University Of Hong Kong
Course: Quantitative Methods
More about Inventory Control Revenue Management MS5211: Quantitative Methods Lecture 7: Revenue Management Lecture 7: Revenue Management Quantitative Methods More about Inventory Control Revenue Management 1 More about Inventory Control 2 Revenue Manageme
School: City University Of Hong Kong
Course: Quantitative Methods
Basic Formulation and Assumptions Solution Methods Four Special Cases in LP and Sensitivity Analysis MS5211: Quantitative Methods Lecture 2: Introduction to Linear Programming Lecture 2: Introduction to Linear Programming Quantitative Methods Basic Formul
School: City University Of Hong Kong
Course: Quantitative Methods
Integer Programming Goal Programming Nonlinear Programming MS5211: Quantitative Methods Lecture 5: Integer Programming and Nonlinear Programming Lecture 5: Integer Programming and Nonlinear Programming Quantitative Methods Integer Programming Goal Progra
School: City University Of Hong Kong
Course: Quantitative Methods
What is Quantitative Analysis How to perform Quantitative Analysis Simple Example of Revenue Management MS5211: Quantitative Methods Lecture 1: Introduction Lecture 1: Introduction Quantitative Methods What is Quantitative Analysis How to perform Quantita
School: City University Of Hong Kong
Course: Quantitative Methods
Basics of EOQ Extensions and Modications of EOQ MS5211: Quantitative Methods Lecture 6: Inventory Control Models Lecture 6: Inventory Control Models Quantitative Methods Basics of EOQ Extensions and Modications of EOQ 1 Basics of EOQ 2 Extensions and Modi
School: City University Of Hong Kong
Course: Quantitative Methods
More Applications of Linear Programming Introduction of the Standard Form and Basic Theory of Linear Programming Solving Linear Programming MS5211: Quantitative Methods Lecture 3: Applications and Solution Methods of Linear Programming Lecture 3: Applicat
School: City University Of Hong Kong
Course: Pms
Example Problem No. 2 (1 of 3) Solve the following model graphically: Maximize Z = 4x1 + 5x2 subject to: x1 + 2x2 10 6x1 + 6x2 36 x1 4 x1, x2 0 Step 1: Plot the constraints as equations Example Problem No. 2 (2 of 3) Maximize Z = 4x1 + 5x2 subject to: x1
School: City University Of Hong Kong
Course: Forecasting Methods For Business
tt, tJQ A~ ~n )S. At4r[ ~kcJlfllcfw_>dd ~ I ;~ 1lue / ttl tcfw_;v)O~tJ M (';~ IvvILPl hJ : t 576/ IAtfb 7g/ h, cfw_' &1'rC (o;fi I7fl A QOtr 7'b d&WYI \ffA'd fJ(ow~ cuvcfw_ ( &i: "7tl L 'r R ~cfw_ Ol:t . tCL I , s I. eA. ~ f~ I e. l 90 b Ac
School: City University Of Hong Kong
Course: Forecasting Methods For Business
CITY UNIVERSITY OF HONG KONG _ Course code & title : MS4102 Business Forecasting Methods Session : Semester B, 2009/2010 Time allowed : Two hours This paper has 17 pages (including this page) Materials, aids and instruments permitted to be used during exa
School: City University Of Hong Kong
Course: Forecasting Methods For Business
="b~t~_ 11L~dJee _tA~ _  ~ ~(l2 , L _ .e  k  11 )"G,=)LT)~\_~ ;ijV~t .+ ~ri: 2_"'  G +*'~f4   r~~ +1b Lbj ! .Je' >l\~     . .  10 .>c(e<,?~ l~ by, b(eS.>'cfw_lf.t;. L 1Lt; J~ ~ ~ "1"t\2:. ~k!E~ ~~h:kc_QofLt'~..
School: City University Of Hong Kong
Course: Forecasting Methods For Business
? ,~,~,~ . > '$~b '1"'6 ' .2:,;,'. ,:,.t,<,\> If 1f' . M S6215: F orecastinq M ethods f or B usiness M idsession t est 1 ) W hat i s t he v alue o f h i n e xpression ( l.l)? 2 ) How m any u nknown p arameters a re t here i n ( 1.1) a nd w hat a re t
School: City University Of Hong Kong
Course: Forecasting Methods For Business
G 1h ( 0 sf:, ( ~= 20 / 2 & I A ) wl~/\j (!/l~g CiliM3 (0 :=: A.I" A,.? 2 0 LwlKMW\,\ plMa~e.s 2) )A 2 0 U,Vl~W't\ p ~Ws. 20 observa;cfw_flM.,) / Rl . ~&., CortesrflvvJ.~ /' f7 of sq  tk LoJ+!i~eJ " e.)(f(aA~tt b<J ~ bCts'l~ If. of Gjdu p~ obstzrv~
School: City University Of Hong Kong
Course: Forecasting Methods For Business
. . _   _._ . . _._ . . _ _. . _.  ._.    . _ _    .__ ._ . _ _. _ . _ . S1 Dh5';rj. M('!j\l'."l~ .tbf/.C. (0 b_ J )!i.o,r"'O'ilL c4 Acfw_(.r~Jo.t't '/ ) . r"\ 'I 6 .kctr cfw_I\ 0\1\'7 C hgJ. ~ ~ren\se ~\!.,1;\/:. : ct1.t?, Lf 't/4S1 00;:
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession Test 2008 Question 1 The following table gives the incidents of infant mortality for a developing African country in 2005  2007 (thousand of deaths). A general Fourier model has been fitted to the data
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 19 October 2010 Time allowed: 70 minutes Question 1 (45 marks) The following SAS program performs Fourier Series analysis based on 48 observations on the change in S&P GSCI Crude Oil Price (labeled
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 16 February 2012, 8:00pm 9:30pm The time series St in Output 1 contains 28 quarterly observations of sales volume (in millions of U.S. dollars) between 2005 Quarter 1 and 2011 Quarter 4. A plot of
School: City University Of Hong Kong
Course: Forecasting Methods For Business
MS6215: Forecasting Methods for Business Midsession test 2013/2014 Semester B Time allowed: 80 minutes Question 1 Consider the following quarterly observations from 2008Q1 to 2010Q4: 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2010Q1 2010Q2 2
School: City University Of Hong Kong
School: City University Of Hong Kong
Solution Question 1 a Let Xij be the number of barrels of component i used in motor oil grade j where i = 1, 2 and j = A, B Total Revenue = Total Cost = Total Profit = Maximize subject to available component 1 available component 2 at least 50% of compone
School: City University Of Hong Kong
FB2201 Management Science II Mock Midterm Time allowed: One hour and Thirty minutes Question 1a (15 marks) A petroleum company produces two grades of motor oil grade A and grade B, from two components. The company wants to determine the optimal mix of th
School: City University Of Hong Kong
School: City University Of Hong Kong
School: City University Of Hong Kong
School: City University Of Hong Kong
FB2201 Management Sciences II (Sem B, 2010 2011) Assignment Due on Week 12 Tutorial Farmer Jones must determine whether to plant corn or wheat. If he plants corn and the weather is warm, he earns $800000; if he plants corn and the weather is cold, he earn
School: City University Of Hong Kong
Course: Pms
Topic 1: Linear Programming Reference Solution Chapter 4 Q10 a) Let Xi be the number of cars of type i; where i=1 (Sedan), 2 (Hatchback), 3 (Minivan) Minimize Z = 12500 X1 + 8500X2 + 13700X3 Subject to: 10500 X1 + 9500 X2 + 12300 X3 = 2800000 (Note: In o
School: City University Of Hong Kong
Course: Pms
Supplementary Exercises on Linear Programming Sensitivity Analysis Question 1 The Win Big Gambling Club promotes gambling junkets from a large Midwestern city to casinos in the Bahamas. The club has budgeted up to $8,000 per week for local advertising. Th
School: City University Of Hong Kong
Course: Pms
Topic 1: Linear Programming Reference Solution Chapter 3 Q25 Let x1 = no. of telephone interviewers, Minimize Z = 50x1 + 70x2 subject to 80x1 + 40x2 3,000 80x1 1,000 40x2 800 x 1, x 2 0 (minimum number of interviews) (minimum number of interviews by tele
School: City University Of Hong Kong
Course: Pms
Transportation, Transshipment, and Assignment (TTA) Chapter Outline To examine special types of LP problems: Transportation Problem Transshipment Problem Assignment Problem Chapter 6 1 2 Transportation Model: Characteristics (1 of 2) Consider the transp
School: City University Of Hong Kong
Course: Pms
TTA Problems Transportation Chapter 6 Q6 Solution Let X ij be the amount shipped from i to j where i=A, B, C and j= 1, 2, 3 Min Z = $(6 X A1 + 9 X A2 +7 X A3 +12 X B1 +3 X B 2 +5 X B 3 +4 X C1 +8 X C 2 +11 X C 3 ) Subject to : Q7 Solution Let X ij be the
School: City University Of Hong Kong
Course: Pms
Integer Programming Chapter 5 Q13. Solution Let X1, X2 = numbers of salespeople assign region 1 and region 2 respectively Y1 = 0 if no sales office in region 1, and 1 if sales office opened in region 1 Max z = 85000X1 + 60000X2  18000 Y1 s.t. : X1 + X2 1
School: City University Of Hong Kong
Course: Pms
Supplementary Exercises on Project Management Question 1 Consider a project with the following activity list. Activity A B C D E F G H I J K L M N a) b) c) d) Immediate Predecessors  A B B B C D,E F G, H I I J K L Estimated Duration (months) 1 2 4 3 2 3
School: City University Of Hong Kong
Course: Pms
MS3401 Chapter 8 Project Management Q3 Solution 1 4 4 3 5 9 9 5 8 4+ 8 + 2 =14 4+ 8 +5 + 6 =23* 4+ 3 + 6 =13 7+ 9 + 6 =22 7+ 5 =12 3 8 6 5 8 6 Finish 7 2 Q10 Solution 8 g 11 12 3 15 S=4 2 2 b 6 S=0 0 0 a 2 2 2 S=0 2 6 2 4 c 6 4 8 S=2 d 4 2 8 S=4 8 8 8 9 e
School: City University Of Hong Kong
Course: Pms
Supplementary Exercises on Inventory Management Question 1 A factory purchases a component used in the manufacture of automobile generators directly from the supplier. The generator production operation, which in operated at a constant rate, will require
School: City University Of Hong Kong
Course: Pms
MS3401 (Summer Term) Chapter 16: Inventory Management Q4 Solution D = 35000 Co = $500 Cc = $0.35 Q5 Solution D = 1215000 Co = $1200 Cc = $0.08 a) Q = 2Co D Cc Q = 2Co D Cc = 2( 1200 )( 1215000 ) 0.08 = 190918.8 lbs 2( 500 )( 35000 ) 0.35 = 10000 yards = b
School: City University Of Hong Kong
Course: Pms
Supplementary Exercises on Decision Analysis Question 1 The ABC Company is considering whether or not to enter the highly competitive personal computer market with a new design called the PX20. They have developed prior probabilities and expected ten yea
School: City University Of Hong Kong
Course: Pms
MS3401 (Summer Term) Chapter 12: Decision Analysis Q18 Solution a) EV (widget) = 120,000 (0.2) + 70,000 (0.7) 30,000 (0.1) = $70,000 EV (hummer) = 60,000 (0.2) + 40,000 (0.7) + 20,000 (0.1) = $42,000 EV (nimnot) = 35,000 (0.2) + 30,000 (0.7) + 30,000 (0.1
School: City University Of Hong Kong
Course: Pms
Topic 1: Linear Programming Reference Solution Chapter 2 Q1 a) Let 1 be the number of cakes made ; Q3 a) Let 1 be the number of ounces of oats should include in each box of cereal; 2 be the number of ounces of rice should include in each box of cereal M