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Harvard

Subject:

Literature & Language

Type:

Editing

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English (U.K.)

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Topic:

# Business Decision Making (Editing Sample)

Instructions:

reediting work, i have attached the coursework and the feedback. you will only have to concentrate on the quantitative parts. thanks

source..Content:

Introduction

In this assessment I am going to show the full formulation of the linear programming problem. The formulation will include a definition of the variables, the objective function and the constraints. I will also draw a graph showing the constraints and the feasible region. In addition, I will show a printout of the Excel setup of the linear programming problem. I will also make comments on the results and advise the factory on how many trucks and bicycles they need to produce and sell each day in order to maximize its profit.

PART 1: QUANTITATIVE

* Linear Programming:

To formulate the linear program, let:

b = No. of bicycles required to maximize profit

t =No. of trucks required to maximize profit

The variables in the equation are the number of bicycles and the number of trucks. The time to be taken in the production is a fixed item.

The constraints for the programming can be defined as follows:

Number of hours available on lathe = 2b + t <= 80

Number of hours available on assembler = 2b + 3t <= 96

The objective is to maximize the profit.

The objective function can be defined as:

16b + 14t = Maximum

The maximum profit is achievable when the number of bicycles produced is

36 and the number of trucks is 8.

Table 1.1: Answer Report

Target Cell (Max)

Cell

Name

Original Value

Final Value

$D$16

Profit Contribution SumProduct

0

688

Adjustable Cells

Cell

Name

Original Value

Final Value

$B$3

Number of bicycles (b)

0

36

$B$4

Number of trucks (t)

0

8

Constraints

Cell

Name

Cell Value

Formula

Status

Slack

$D$10

Lathe SumProduct

80

$D$10<=$F$10

Binding

0

$D$11

Assembler SumProduct

96

$D$11<=$F$11

Binding

0

Table 1.2: Sensitivity Report

Adjustable Cells

Â

Â

Final

Reduced

Objective

Allowable

Allowable

Cell

Name

Value

Cost

Coefficient

Increase

Decrease

$B$3

Number of bicycles (b)

36

0

16

12

6.666666667

$B$4

Number of trucks (t)

8

0

14

10

6

Constraints

Â

Â

Final

Shadow

Constraint

Allowable

Allowable

Cell

Name

Value

Price

R.H. Side

Increase

Decrease

$D$10

Lathe SumProduct

80

5

80

16

48

$D$11

Assembler SumProduct

96

3

96

144

16

Solving the linear program through Excel Solver, it can be observed that the optimum values for the number of bicycles and the number of trucks in order to maximize profit are 36 and 8 respectively.

The corresponding answer reports and sensitivity reports can be found in Tables 1.1 and 1.2 respectivel...

In this assessment I am going to show the full formulation of the linear programming problem. The formulation will include a definition of the variables, the objective function and the constraints. I will also draw a graph showing the constraints and the feasible region. In addition, I will show a printout of the Excel setup of the linear programming problem. I will also make comments on the results and advise the factory on how many trucks and bicycles they need to produce and sell each day in order to maximize its profit.

PART 1: QUANTITATIVE

* Linear Programming:

To formulate the linear program, let:

b = No. of bicycles required to maximize profit

t =No. of trucks required to maximize profit

The variables in the equation are the number of bicycles and the number of trucks. The time to be taken in the production is a fixed item.

The constraints for the programming can be defined as follows:

Number of hours available on lathe = 2b + t <= 80

Number of hours available on assembler = 2b + 3t <= 96

The objective is to maximize the profit.

The objective function can be defined as:

16b + 14t = Maximum

The maximum profit is achievable when the number of bicycles produced is

36 and the number of trucks is 8.

Table 1.1: Answer Report

Target Cell (Max)

Cell

Name

Original Value

Final Value

$D$16

Profit Contribution SumProduct

0

688

Adjustable Cells

Cell

Name

Original Value

Final Value

$B$3

Number of bicycles (b)

0

36

$B$4

Number of trucks (t)

0

8

Constraints

Cell

Name

Cell Value

Formula

Status

Slack

$D$10

Lathe SumProduct

80

$D$10<=$F$10

Binding

0

$D$11

Assembler SumProduct

96

$D$11<=$F$11

Binding

0

Table 1.2: Sensitivity Report

Adjustable Cells

Â

Â

Final

Reduced

Objective

Allowable

Allowable

Cell

Name

Value

Cost

Coefficient

Increase

Decrease

$B$3

Number of bicycles (b)

36

0

16

12

6.666666667

$B$4

Number of trucks (t)

8

0

14

10

6

Constraints

Â

Â

Final

Shadow

Constraint

Allowable

Allowable

Cell

Name

Value

Price

R.H. Side

Increase

Decrease

$D$10

Lathe SumProduct

80

5

80

16

48

$D$11

Assembler SumProduct

96

3

96

144

16

Solving the linear program through Excel Solver, it can be observed that the optimum values for the number of bicycles and the number of trucks in order to maximize profit are 36 and 8 respectively.

The corresponding answer reports and sensitivity reports can be found in Tables 1.1 and 1.2 respectivel...

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