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Pages:
3 pages/≈825 words
Sources:
Level:
Harvard
Subject:
Literature & Language
Type:
Editing
Language:
English (U.K.)
Document:
MS Word
Date:
Total cost:
$ 10.8
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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