Question 1

Using Data Envelopment Analysis (DEA) for an inefficient unit, a more efficient composite unit can be found by

Accepted Answer

A)  Solving its DEA problem and retrieving the weights from the answer report. 
B)  Solving its DEA problem and examining those units whose final value is non-zero. 
C)  Solving its DEA problem and using the resulting shadow prices as composite weights. 
D)  Solving its DEA problem and using the positive resulting shadow prices as composite weights. 
A)  Solving its DEA problem and retrieving the weights from the answer report. 
B)  Solving its DEA problem and examining those units whose final value is non-zero. 
C)  Solving its DEA problem and using the resulting shadow prices as composite weights. 
D)  Solving its DEA problem and using the positive resulting shadow prices as composite weights. C

Question 2

The "Analyze Without Solving" tool in Risk Solver Platform (RSP) is useful for

Accepted Answer

A)  verifying the equations in a spreadsheet model. 
B)  toggling between absolute and relative cell referencing. 
C)  executing the Excel spreadsheet layout Wizard. 
D)  naming cells and cell ranges for easier modifiability. 
A)  verifying the equations in a spreadsheet model. 
B)  toggling between absolute and relative cell referencing. 
C)  executing the Excel spreadsheet layout Wizard. 
D)  naming cells and cell ranges for easier modifiability. A

Question 3

A company needs to purchase several new machines to meet its future production needs. It can purchase three different types of machines A, B, and C. Each machine A costs $80,000 and requires 2,000 square feet of floor space. Each machine B costs $50,000 and requires 3,000 square feet of floor space. Each machine C costs $40,000 and requires 5,000 square feet of floor space. The machines can produce 200, 250 and 350 units per day respectively. The plant can only afford $500,000 for all the machines and has at most 20,000 square feet of room for the machines. The company wants to buy as many machines as possible to maximize daily production.
What are the key formulas for this Excel spreadsheet implementation of the following formulation?
 Let $\quad \mathbf { x } _ { \mathrm { i } } =$ mumber of machines of type i purchased

$\begin{array}{ll}
\text { MAX: } & 200 \mathrm{X}_{1}+250 \mathrm{X}_{2}+300 \mathrm{X}_{3} \
\text { Subject to: } & 2 \mathrm{X}_{1}+3 \mathrm{X}_{2}+5 \mathrm{X}_{3} \leq 20 \
& 80 \mathrm{X}_{1}+50 \mathrm{X}_{2}+40 \mathrm{X}_{3} \leq 500 \
& \mathrm{X}_{1}, \mathrm{X}_{2} \mathrm{X}_{3} \geq 0
\end{array}$

Accepted Answer

$\begin{array}{lll}
\text { Cell } & \text { Formula } & \text { Copied to } \
\hline \text { G4 } & =\text { SUMPRODUCT(B2:D2,B4:D4) } & \
\text { E7 } & =\text { SUMPRODUCT } \$ \mathrm{~B} \$ 2: \$ \mathrm{D} \$ 2, \mathrm{~B} 7: \mathrm{D} 7) & \text { E8 }
\end{array}$

Question 4

Exhibit 3.2
The following questions are based on this problem and accompanying Excel windows.
The Byte computer company produces two models of computers, Plain and Fancy. It wants to plan how many computers to produce next month to maximize profits. Producing these computers requires wiring, assembly and inspection time. Each computer produces a certain level of profits but faces only a limited demand. There are also a limited number of wiring, assembly and inspection hours available in each month. The data for this problem is summarized in the following table.
 $\begin{array} { l c c c c c } 
&&\text { Maximum } &&\text { Assembly }&\text { Inspection } \
\text { Computer } &\text { Profit per }&\text { demand for }&\text { Wiring Hours }  &\text { Hours } &\text { Hours }  \
\text { Model }&\text { Model (\$)}&\text { product }&\text { Required }&\text { Required }&\text { Required }\
\hline \text { Plain } & 30 & 80 & .4 & .5 & .2 \
\text { Fancy } & 40 & 90 & .5 & .4 & .3 \
\hline & & \text { Hours Available } & 50 & 50 & 22
\end{array}$

Let $\quad X _ { 1 } =$ Number of Plain computers to produce
$\quad\quad X _ { 2 } =$ Number of Fancy computers to produce

MAX: $\quad \quad\quad30 X _ { 1 } + 40 X _ { 2 }$
Subject to: $\quad .4 \mathrm { X } _ { 1 } + .5 \mathrm { X } _ { 2 } \leq 50$ (wiring hours)
$\quad\quad\quad\quad\quad.5 \mathrm { X } _ { 1 } + .4 \mathrm { X } _ { 2 } \leq 50$ (assembly hours)
$\quad\quad\quad\quad\quad.2 \mathrm { X } _ { 1 } + .2 \mathrm { X } _ { 2 } \leq 22$ (inspection hours)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 1 } \leq 80$ (Plin computers demand)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 2 } \leq 90$ (Fancy computers demand)
$\quad\quad\quad\quad \quad X _ { 1 } , X _ { 2 } \geq 0$ $$\begin{array} { | c | l | c | c | c | c | } 
\hline &  { \text { A } } & \text { B } & \text { C } & \text { D } & \text { E } \
\hline 1 & & \text { Byte Computer Company } & & \
\hline 2 & & & & & \
\hline 3 & & \text { Plain } & \text { Fancy } & & \
\hline 4 & \text { Number to make: } & & & & \text { Total Profit: } \
\hline 5 & \text { Unit profit: } & 30 & 40 & & \
\hline 6 & & & & & \
\hline 7 & \text { Constraints: } & & & \text { Used } & \text { Available } \
\hline 8 & \text { Wiring } & 0.4 & 0.5 & & 50 \
\hline 9 & \text { Assembly } & 0.5 & 0.4 & & 50 \
\hline 10 & \text { Inspection } & 0.2 & 0.3 & & 22 \
\hline 11 & \text { Plain Demand } & 1 & & & 80 \
\hline 12 & \text { Fancy Demand } & & 1 & & 90 \
\hline
\end{array}$$
-Refer to Exhibit 3.2. Which cells should be the constraint cells in this problem?

Accepted Answer

A)  B4:C4 
B)  E5 
C)  D8:D12 
D)  E8:E12 
A)  B4:C4 
B)  E5 
C)  D8:D12 
D)  E8:E12

Question 5

Exhibit 3.2
The following questions are based on this problem and accompanying Excel windows.
The Byte computer company produces two models of computers, Plain and Fancy. It wants to plan how many computers to produce next month to maximize profits. Producing these computers requires wiring, assembly and inspection time. Each computer produces a certain level of profits but faces only a limited demand. There are also a limited number of wiring, assembly and inspection hours available in each month. The data for this problem is summarized in the following table.
 $\begin{array} { l c c c c c } 
&&\text { Maximum } &&\text { Assembly }&\text { Inspection } \
\text { Computer } &\text { Profit per }&\text { demand for }&\text { Wiring Hours }  &\text { Hours } &\text { Hours }  \
\text { Model }&\text { Model (\$)}&\text { product }&\text { Required }&\text { Required }&\text { Required }\
\hline \text { Plain } & 30 & 80 & .4 & .5 & .2 \
\text { Fancy } & 40 & 90 & .5 & .4 & .3 \
\hline & & \text { Hours Available } & 50 & 50 & 22
\end{array}$

Let $\quad X _ { 1 } =$ Number of Plain computers to produce
$\quad\quad X _ { 2 } =$ Number of Fancy computers to produce

MAX: $\quad \quad\quad30 X _ { 1 } + 40 X _ { 2 }$
Subject to: $\quad .4 \mathrm { X } _ { 1 } + .5 \mathrm { X } _ { 2 } \leq 50$ (wiring hours)
$\quad\quad\quad\quad\quad.5 \mathrm { X } _ { 1 } + .4 \mathrm { X } _ { 2 } \leq 50$ (assembly hours)
$\quad\quad\quad\quad\quad.2 \mathrm { X } _ { 1 } + .2 \mathrm { X } _ { 2 } \leq 22$ (inspection hours)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 1 } \leq 80$ (Plin computers demand)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 2 } \leq 90$ (Fancy computers demand)
$\quad\quad\quad\quad \quad X _ { 1 } , X _ { 2 } \geq 0$ $$\begin{array} { | c | l | c | c | c | c | } 
\hline &  { \text { A } } & \text { B } & \text { C } & \text { D } & \text { E } \
\hline 1 & & \text { Byte Computer Company } & & \
\hline 2 & & & & & \
\hline 3 & & \text { Plain } & \text { Fancy } & & \
\hline 4 & \text { Number to make: } & & & & \text { Total Profit: } \
\hline 5 & \text { Unit profit: } & 30 & 40 & & \
\hline 6 & & & & & \
\hline 7 & \text { Constraints: } & & & \text { Used } & \text { Available } \
\hline 8 & \text { Wiring } & 0.4 & 0.5 & & 50 \
\hline 9 & \text { Assembly } & 0.5 & 0.4 & & 50 \
\hline 10 & \text { Inspection } & 0.2 & 0.3 & & 22 \
\hline 11 & \text { Plain Demand } & 1 & & & 80 \
\hline 12 & \text { Fancy Demand } & & 1 & & 90 \
\hline
\end{array}$$
-Refer to Exhibit 3.2. What formula should be entered in cell E5 in the accompanying Excel spreadsheet to compute total profit?

Accepted Answer

A)  =B4*C4+B5*C5 
B)  =SUMPRODUCT(B4:C4,B5:C5) 
C)  =SUM(B5:C5) 
D)  =SUM(E8:E10) 
A)  =B4*C4+B5*C5 
B)  =SUMPRODUCT(B4:C4,B5:C5) 
C)  =SUM(B5:C5) 
D)  =SUM(E8:E10)

Question 6

A heuristic solution is

Accepted Answer

A)  used by Risk Solver Platform (RSP) when the Guess button is used. 
B)  guaranteed to produce an optimal solution. 
C)  used by Risk Solver Platform (RSP) if Standard GRG Nonlinear method is selected. 
D)  a rule-of-thumb for making decisions. 
A)  used by Risk Solver Platform (RSP) when the Guess button is used. 
B)  guaranteed to produce an optimal solution. 
C)  used by Risk Solver Platform (RSP) if Standard GRG Nonlinear method is selected. 
D)  a rule-of-thumb for making decisions.

Question 7

Exhibit 3.3
The following questions are based on this problem and accompanying Excel windows.
Jack's distillery blends scotches for local bars and saloons. One of his customers has requested a special blend of scotch targeted as a bar scotch. The customer wants the blend to involve two scotch products, call them A and B. Product A is a higher quality scotch while product B is a cheaper brand. The customer wants to make the claim the blend is closer to high quality than the alternative. The customer wants 50 1500 ml bottles of the blend. Each bottle must contain at least 48% of Product A and at least 500 ml of B. The customer also specified that the blend have an alcohol content of at least 85%. Product A contains 95% alcohol while product B contains 78%. The blend is sold for $12.50 per bottle. Product A costs $7 per liter and product B costs $3 per liter. The company wants to determine the blend that will meet the customer's requirements and maximize profit.
 Let $\quad X _ { 1 } =$ Number of liters of product $A _ { 1 }$ in total blend delivered
$\quad\quad\quad\mathrm { X } _ { 2 } =$ Number of liters of product $\mathrm { B }$ in total blend delivered

MIN: $\quad 7 \mathrm { X } _ { 1 } + 3 \mathrm { X } _ { 2 }$
Subject to: $\quad \mathrm { X } _ { 1 } + \mathrm { X } _ { 2 } = 1.5 * 50$ (Total liters of mix)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 1 } \geq 0.48 * 1.5 * 50 \left( \mathrm { X } _ { 1 } \right.$ minimum)
$\quad\quad\quad\quad\quad\mathrm { X } _ { 2 } \geq 0.5 ^ { * } 50 \left( \mathrm { X } _ { 2 } \right.$ minimum)
$\quad\quad\quad\quad.0 .95 \mathrm { X } _ { 1 } + 0.78 \mathrm { X } _ { 2 } \geq 0.85 * 1.5 * 50$ (85\% alcohol minimum)
$\quad\quad\quad\quad\mathrm { X } _ { 1 } , \mathrm { X } _ { 2 } \geq 0$  
-Refer to Exhibit 3.3. Which cells should be changing cells in this problem?

Accepted Answer

A)  B4:C4 
B)  E5 
C)  D8:D10 
D)  E8:E10 
A)  B4:C4 
B)  E5 
C)  D8:D10 
D)  E8:E10

Question 8

What action is required to make Risk Solver Platform (RSP) solve a specified problem?

Accepted Answer

A)  Type go in cell A1. 
B)  Click the "Optimize" button on the RSP Ribbon, or the green arrow "Solve" in the Task Pane. 
C)  Click the Close button in the RSP Parameters dialog box. 
D)  Click the Guess button in the RSP Parameters dialog box. 
A)  Type go in cell A1. 
B)  Click the "Optimize" button on the RSP Ribbon, or the green arrow "Solve" in the Task Pane. 
C)  Click the Close button in the RSP Parameters dialog box. 
D)  Click the Guess button in the RSP Parameters dialog box.

Question 9

Exhibit 3.5
The following questions are based on this problem and accompanying Excel windows.
A company is planning production for the next 4 quarters. They want to minimize the cost of production. The production cost is stable but demand and production capacity vary from quarter to quarter. The maximum amount of inventory which can be held is 12,000 units and management wants to keep at least 3,000 units on hand. Quarterly inventory holding cost is 3% of the cost of production. The company estimates the number of units carried in inventory each month by averaging the beginning and ending inventory for each month. There are currently 5,000 units in inventory. The company wants to produce at no less than one half of its maximum capacity in any quarter.
 $\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\quad $$\text { Quarter }$
$\begin{array}{l}
\begin{array} { l r r r r } 
& 1 & 2 & 3 & { 4 } \
\hline \text { Unit Production Cost } & \$ 300 & \$ 300 & \$ 300 & \$ 300 \
\text { Units Demanded } & 2,000 & 9,000 & 12,000 & 11,000 \
\text { Maximum Production } & 8,000 & 7,000 & 8,000 & 9,000
\end{array}
\end{array}$ \(\begin{array} { l l } 
\text { Let } & P _ { i } = \text { number of units produced in quarter } i , i = 1 , \ldots , 4 \
& B _ { i } = \text { beginning inventory for quarter i } \
\
\text { MiN: } & 300 P _ { 1 } + 300 P _ { 2 } + 300 P _ { 3 } + 300 P _ { 4 } + \
& 9 \left( B _ { 1 } + B _ { 2 } \right) / 2 + 9 \left( B _ { 2 } + B _ { 3 } \right) / 2 + 9 \left( B _ { 3 } + B _ { 4 } \right) / 2 + 9 \left( B _ { 4 } + B _ { 5 } \right) / 2 \
\text { Subject to: } & 4000 \leq P _ { 1 } \leq 8000 \
& 3500 \leq P _ { 2 } \leq 7000 \
& 4000 \leq P _ { 3 } \leq 8000 \
& 4500 \leq P _ { 4 } \leq 9000 \
&3000 \leq B _ { 1 } + P _ { 1 } - 2000 \leq 12000 \
& 3000 \leq B _ { 2 } + P _ { 2 } - 9000 \leq 12000 \
&3000 \leq B _ { 3 } + P _ { 3 } - 12000 \leq 12000 \
& 3000 \leq B _ { 4 } + P _ { 4 } - 11000 \leq 12000 \
& B _ { 2 } = B _ { 1 } + P _ { 1 } - 2000 \
& B _ { 3 } = B _ { 2 } + P _ { 2 } - 9000 \
&B _ { 4 } = B _ { 3 } + P _ { 3 } - 12000 \
& B _ { 5 } = B _ { 4 } + P _ { 4 } - 11000 \
&P _ { i } , B _ { i } \geq 0
\end{array}\) \(\begin{array}{|c|l|c|c|c|c|c|}
\hline &{\text { A }} & \mathrm{B} & \mathrm{C} & \mathrm{D} & \mathrm{E} & \mathrm{F} \
\hline 1 & & & & \text { Quarter } & & \
\hline 2 & & & 1 & 2 & 3 & 4 \
\hline 3 & \text { Beginning Inventory } & & 5,000 & 11,000 & 9,000 & 5,000 \
\hline 4 & \text { Units Produced } & & 8,000 & 7,000 & 8,000 & 9,000 \
\hline 5 & \text { Units Demanded } & & 2,000 & 9,000 & 12,000 & 11,000 \
\hline 6 & \text { Ending Inventory } & & 11,000 & 9,000 & 5,000 & 3,000 \
\hline 7 & & & & & & \
\hline 8 & \text { Minimum Procduction } & & 4,000 & 3,500 & 4,000 & 4,500 \
\hline 9 & \text { Maximum Froduction } & & 8,000 & 7,000 & 8,000 & 9,000 \
\hline 10 & & & & & & \
\hline 11 & \text { Minimum Inventory } & & 3,000 & 3,000 & 3,000 & 3,000 \
\hline 12 & \text { Maximum Inventory } & & 12,000 & 12,000 & 12,000 & 12,000 \
\hline 13 & & & & & & \
\hline 14 & \text { Unit Production Cost } & & \$ 300 & \$ 300 & \$ 300 & \$ 300 \
\hline 15 & \text { Unit Carrying Cost } & 3.0 \% & \$ 9,00 & \$ 9.00 & \$ 9,00 & \$ 9.00 \
\hline 16 & & & & & & \
\hline 17 & \text { Quarterly Production Cost } & & \$ 2,400,000 & \$ 2,100,000 & \$ 2,400,000 & \$ 2,700,000 \
\hline 18 & \text { Quarterly Carrying Cost } & & \$ 72,000 & \$ 90,000 & \$ 63,000 & \$ 36,000 \
\hline 19 & & & & & & \
\hline 20 & & & & & \text { Tota1 Cost } & \$ 9,861,000 \
\hline
\end{array}\)

-Refer to Exhibit 3.5. What formula could be entered in cell F20 in the accompanying Excel spreadsheet to compute the Total Cost for all four quarters?

Accepted Answer

A)  SUMPRODUCT($C$4:$F$4,C17:F17) 
B)  SUM(C17:F17) + SUM(C18:F18) 
C)  PRODUCT(SUM(C14:F15,C17:F18) 
D)  SUMPRODUCT(C4:F4,C14:F14) + SUMPRODUCT(C6:F6,C15:F15) 
A)  SUMPRODUCT($C$4:$F$4,C17:F17) 
B)  SUM(C17:F17) + SUM(C18:F18) 
C)  PRODUCT(SUM(C14:F15,C17:F18) 
D)  SUMPRODUCT(C4:F4,C14:F14) + SUMPRODUCT(C6:F6,C15:F15)

Question 10

Exhibit 3.1
The following questions are based on this problem and accompanying Excel windows.
Jones Furniture Company produces beds and desks for college students. The production process requires carpentry and varnishing. Each bed requires 6 hours of carpentry and 4 hour of varnishing. Each desk requires 4 hours of carpentry and 8 hours of varnishing. There are 36 hours of carpentry time and 40 hours of varnishing time available. Beds generate $30 of profit and desks generate $40 of profit. Demand for desks is limited, so at most 8 will be produced.
 Let
$$\begin{array} { l } 
\mathrm { X } _ { 1 } = \text { Number of Beds to produce } \
\mathrm { X } _ { 2 } = \text { Number of Desks to produce }
\end{array}$$ The LP model for the problem is
 $$\begin{array} { l l } 
\text { MAX: } & 30 X _ { 1 } + 40 X _ { 2 } \
\text { Subject to: } & 6 X _ { 1 } + 4 X _ { 2 } \leq 36 \text { (carpentry) } \
& 4 X _ { 1 } + 8 X _ { 2 } \leq 40 \text { (varnishing) } \
& X _ { 2 } \leq 8 \text { (demand for desks) } \
& X _ { 1 } , X _ { 2 } \geq 0
\end{array}$$  
-Refer to Exhibit 3.1. What formula should be entered in cell D8 in the accompanying Excel spreadsheet to compute the amount of carpentry used?

Accepted Answer

A)  =B4*B5+C4*C5 
B)  =SUMPRODUCT(B8:C8,$B$4:$C$4) 
C)  =SUM(B5:C5) 
D)  =SUM(E8:E10) 
A)  =B4*B5+C4*C5 
B)  =SUMPRODUCT(B8:C8,$B$4:$C$4) 
C)  =SUM(B5:C5) 
D)  =SUM(E8:E10)

Question 11

A company needs to purchase several new machines to meet its future production needs. It can purchase three different types of machines A, B, and C. Each machine A costs $80,000 and requires 2,000 square feet of floor space. Each machine B costs $50,000 and requires 3,000 square feet of floor space. Each machine C costs $40,000 and requires 5,000 square feet of floor space. The machines can produce 200, 250 and 350 units per day respectively. The plant can only afford $500,000 for all the machines and has at most 20,000 square feet of room for the machines. The company wants to buy as many machines as possible to maximize daily production.
Formulate the LP for this problem.

Accepted Answer

The answer of A company needs to purchase several new...

Question 12

Which command is equivalent to =SUMPRODUCT(A1:A3,B1:B3)?

Accepted Answer

A)  =SUM(PRODUCT((A1:A3,B1:B3)) 
B)  =PRODUCT(SUM((A1:A3,B1:B3)) 
C)  =PRODUCT(A1+B1,A2+B2,A3+B3)) 
D)  =A1*B1+A2*B2+A3*B3 
A)  =SUM(PRODUCT((A1:A3,B1:B3)) 
B)  =PRODUCT(SUM((A1:A3,B1:B3)) 
C)  =PRODUCT(A1+B1,A2+B2,A3+B3)) 
D)  =A1*B1+A2*B2+A3*B3

Question 13

You have been given the following linear programming model and Excel spreadsheet to solve this problem. What formulas should be entered into cells E5 and D8:D10 to implement this model?
 $$\begin{array} { l l } 
\text { MAX: } & 4 X _ { 1 } + 3 X _ { 2 } \
\text { Subject to: } & 6 X _ { 1 } + 7 X _ { 2 } \leq 84 \
& X _ { 1 } \leq 10 \
& X _ { 2 } \leq 8 \
& X _ { 1 } , X _ { 2 } \geq 0
\end{array}$$ $$\begin{array} { | c | c | c | c | c | c | } 
\hline & \text { A } & \text { B } & \text { C } & \text { D } & \text { E } \
\hline 1 & & & & & \
\hline 2 & & & & & \
\hline 3 & & \mathrm { X } _ { 1 } & \mathrm { X } _ { 2 } & & \
\hline 4 & \text { Number to make: } & & & & \text { OBJ. FN. VALUE } \
\hline 5 & \text { Unit profit: } & 4 & 3 & & \
\hline 6 & & & & & \
\hline 7 & \text { Constraints: } & & & \text { Used } & \text { Available } \
\hline 8 & 1 & 6 & 7 & & 84 \
\hline 9 & 2 & 1 & 0 & & 10 \
\hline 10 & 3 & 0 & 1 & & 8 \
\hline
\end{array}$$

Accepted Answer

The answer of You have been given the following linear...

Question 14

Scaling problems

Accepted Answer

A)  can cause Risk Solver Platform (RSP) to consider a linear problem as nonlinear. 
B)  can cause problems in accuracy of solutions returned. 
C)  are caused by small numbers and large numbers used in the same problem. 
D)  all of these. 
A)  can cause Risk Solver Platform (RSP) to consider a linear problem as nonlinear. 
B)  can cause problems in accuracy of solutions returned. 
C)  are caused by small numbers and large numbers used in the same problem. 
D)  all of these.

Question 15

A hospital needs to determine how many nurses to hire to cover a 24 hour period. The nurses must work 8 consecutive hours but can start work at the start of 6 different shifts. They are paid different wages depending on when they start their shifts. The number of nurses required per 4-hour time period and their wages are shown in the following table.
 $\begin{array}{lcc}
\text { Time period } & \text { Required \# of Nurses } & \text { Wage }(\$ / \mathrm{hr}) \
\hline 12 \mathrm{am}-4 \mathrm{am} & 20 & 15 \
4 \mathrm{am}-8 & 30 & 16 \
\text { am } & & \
8 \mathrm{am}-12 \mathrm{pm} & 40 & 13 \
12 \mathrm{pm}-4 \mathrm{pm} & 50 & 13 \
4 \mathrm{pm}-4 & 40 & 14 \
\mathrm{pm}\
8 \mathrm{pm}-12 \mathrm{am}&30 & 15  
\end{array}$

What values would you enter in the Risk Solver Platform (RSP) task pane for the following cells for this Excel spreadsheet implementation of the formulation for this problem?
Objective Cell:
Variables Cells:
Constraints Cells:
 Let $\quad X _ { i } =$ mumber of nurses warking in thme period $i ; i = 1,6$
$$\begin{array} { l l } 
\text { MiN: } & 1 \mathbf { X } _ { 1 } + 1 \mathbf { X } _ { \mathbf { 2 } } + 1 \mathbf { X } _ { \mathbf { 3 } } + 1 \mathbf { X } _ { 4 } + 1 \mathbf { X } _ { \mathbf { 5 } } + 1 \mathbf { X } _ { \mathbf { 6 } } \
\text { Subject ta: } & 1 \mathbf { X } _ { 1 } + 1 \mathbf { X } _ { \mathbf { z } } \geq 30 \
& 1 \mathbf { X } _ { \mathbf { 2 } } + 1 \mathbf { X } _ { \mathbf { 3 } } \geq 40 \
& 1 \mathbf { X } _ { \mathbf { 3 } } + 1 \mathbf { X } _ { 4 } \geq 50 \
& 1 \mathbf { X } _ { 4 } + 1 \mathbf { X } _ { \mathbf { 5 } } \geq 40 \
& 1 \mathbf { X } _ { 5 } + 1 \mathbf { X } _ { \mathbf { 1 } } \geq 30 \
& 1 \mathbf { X } _ { 1 } + 1 \mathbf { X } _ { \mathbf { 1 } } \geq 20 \
& \mathbf { X } _ { \mathrm { i } } \geq 0
\end{array}$$

Accepted Answer

The answer of A hospital needs to determine how many...

Question 16

Data Envelopment Analysis (DEA) is an LP-based methodology in which weighted sums of inputs and outputs are calculated and

Accepted Answer

A)  the constraints capture the maximum effectiveness of each unit. 
B)  the objective is to maximize every units output. 
C)  the constraints ensure the sum of the weighted outputs is one. 
D)  the objective for each unit is to maximize the weighted sum of its outputs. 
A)  the constraints capture the maximum effectiveness of each unit. 
B)  the objective is to maximize every units output. 
C)  the constraints ensure the sum of the weighted outputs is one. 
D)  the objective for each unit is to maximize the weighted sum of its outputs.

Question 17

Numeric constants should be

Accepted Answer

A)  embedded in formulas. 
B)  placed in individual cells 
C)  placed in separate workbooks. 
D)  entered manually every time a model is solved. 
A)  embedded in formulas. 
B)  placed in individual cells 
C)  placed in separate workbooks. 
D)  entered manually every time a model is solved.

Question 18

You have been given the following linear programming model and Excel spreadsheet to solve this problem. What numbers should be entered into cells B5:C5 and B8:C10 to implement this model?
 MIN: $\quad 8 X _ { 1 } + 3 X _ { 2 }$
Subject to: $\quad X _ { 2 } \geq 8$
$\begin{array} { l } 
8 \mathrm { X } _ { 1 } + 5 \mathrm { X } _ { 2 } \geq 80 \
3 \mathrm { X } _ { 1 } + 5 \mathrm { X } _ { 2 } \geq 60 \
\mathrm { X } _ { 1 } , \mathrm { X } _ { 2 } \geq 0
\end{array}$ $$\begin{array} { | c | c | c | c | c | c | } 
\hline & \text { A } & \text { B } & \text { C } & \text { D } & \text { E } \
\hline 1 & & & & & \
\hline 2 & & & & & \
\hline 3 & & \mathrm { X } _ { 1 } & \mathrm { X } _ { 2 } & & \
\hline 4 & \text { Number to make: } & & & & \text { OBJ. FN. VALUE } \
\hline 5 & \text { Unit profit: } & & & & \
\hline 6 & & & & & \
\hline 7 & \text { Constraints: } & & & \text { Used } & \text { Available } \
\hline 8 & 1 & & & & 8 \
\hline 9 & 2 & & & & 80 \
\hline 10 & 3 & & & & 60 \
\hline
\end{array}$$

Accepted Answer

The answer of You have been given the following linear...

Question 19

Problems which have only integer solutions are called

Accepted Answer

A)  discrete programming problems. 
B)  integer programming problems 
C)  discrete programming problems 
D)  infeasible programming problems 
A)  discrete programming problems. 
B)  integer programming problems 
C)  discrete programming problems 
D)  infeasible programming problems

Question 20

The built-in Solver in Excel is found under which tab on the ribbon?

Accepted Answer

A)  Tools 
B)  Insert 
C)  Data 
D)  Window 
A)  Tools 
B)  Insert 
C)  Data 
D)  Window

Using Data Envelopment Analysis (DEA) for an inefficient unit, a more efficient composite unit can be found by

The "Analyze Without Solving" tool in Risk Solver Platform (RSP) is useful for

A heuristic solution is

What action is required to make Risk Solver Platform (RSP) solve a specified problem?

Which command is equivalent to =SUMPRODUCT(A1:A3,B1:B3)?

Scaling problems

Data Envelopment Analysis (DEA) is an LP-based methodology in which weighted sums of inputs and outputs are calculated and

Numeric constants should be

Problems which have only integer solutions are called

The built-in Solver in Excel is found under which tab on the ribbon?

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming and Evolutionary Optimization

Regression Analysis

Discriminant Analysis

Time Series Forecasting

Introduction to Simulation Using Risk Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters

Exam 3: Modeling and Solving Lp Problems in a Spreadsheet

Using Data Envelopment Analysis (DEA) for an inefficient unit, a more efficient composite unit can be found by

The "Analyze Without Solving" tool in Risk Solver Platform (RSP) is useful for

A heuristic solution is

What action is required to make Risk Solver Platform (RSP) solve a specified problem?

Which command is equivalent to =SUMPRODUCT(A1:A3,B1:B3)?

Scaling problems

Data Envelopment Analysis (DEA) is an LP-based methodology in which weighted sums of inputs and outputs are calculated and

Numeric constants should be

Problems which have only integer solutions are called

The built-in Solver in Excel is found under which tab on the ribbon?

Introduction to Modeling and Decision Analysis

Introduction to Optimization and Linear Programming

Sensitivity Analysis and the Simplex Method

Network Modeling

Integer Linear Programming

Goal Programming and Multiple Objective Optimization

Nonlinear Programming and Evolutionary Optimization

Regression Analysis

Discriminant Analysis

Time Series Forecasting

Introduction to Simulation Using Risk Solver Platform

Queuing Theory

Decision Analysis

Project Management Online

Filters