decision making
DESCRIPTION
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Advanced Organizer
D ecision Mak ing
P lanning
O rganizing
Leading
C ontro lling
Managem ent Functions
R esearch
D esign
Production
Q uality
Marketing
Project Managem ent
Managing Technology
Tim e Managem ent
E thics
C areer
Personal Technology
Managing Engineering and Technology
1. Recognize Problem / Opportunity
2. Define Goals/Objectives
3. Assemble Relevant Data
4. Identify Feasible Alts
7. Predict Alts’ Outcomes
8. Choose the Best Alt.9. Audit the Results
Overall Mission / Objectives
5. Select the Criterion
6. Construct a Model
Planning/ Decision-Making Process
Planning Tools &Techniques
Techniques for Assessing the environment• Environmental Scanning• Forecasting-• Benchmarking-Techniques for allocating Resources• Budgeting• Scheduling- Gantt charts, PERT Network analysis, CPM• Breakeven Analysis• Linear programming
Mathematical Model
• A model is usually refers to a representation of an actual object to reduced scale such as a wooden model of a building. These models are used to study the behavior of the actual object. For example a scale model of an aero plane can be used in a wind tunnel to simulate the behavior of the actual aero plane in flight. Mathematical model perform the similar function. This mathematical modeling is the aid to decision making
Tools for Decision Making under Certainty
• Linear programming– Graphical solution– Simplex method– Computer software
• Non-linear programming• Engineering Economic Analysis
Linear Programming
• Linear programming is the simplest and most widely used technique for solving decision making problems. The aim of this method is to determine how to meet the desired goals while taking constraints into account.
• The term linear implies proportionately
Decision Variables Objective Function (Maximizing or Minimizing)
Example:A factory produces two products, product X and product Y. If we can realize $10 profit per unit of product X and $14 per unit of Y, what should be the production level for product X and product Y?
Maximize P = 10x + 14y
Linear Programming
Linear Programming
• Constrains– Example:
• 3 machinists• 2 assemblers• Each works 40 hours/week• Product X requires 3 hours of machining and 1 hour of
assembly per unit• Product Y requires 2 hours of machining and 2 hours of
assembly per unit– For machining time: 3x + 2y 3(40)– For assembly time: 1x + 2y 2(40)
Linear programming Graphical solution (Constraints)
3x+2y≤120
(40,0)
(0,60)
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90
Y
X
x+2y≤80
(80,0)
(0,40)
Feasible Region
Corner Solutions
Linear programming Graphical solution (Objective Function)
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90
Y
X
P=10x+14y
P=1050
P=700
P=350
Linear programming Graphical solution (Objective Function)
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90
Y
X
P=10x+14y
P=1050
P=700
P=350
Linear programming Graphical solution
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90
Y
X
Optimal Solution(20, 30)