INTRODUCTION TO MANAGEMENT SCIENCE
Today’s Topic:
1. Define Management Science
2. Modeling and Models
3. Problem Solving and Decision Making Process
4. Quantitative Models
5. Management Science In Practice
6. History of Linear Programming
1. Define Management Science (Operations Research)
 a scientific (quantitative) approach to decision making that involves
operations
 the art of giving bad answers to problems which otherwise worse answers are
given
 quantitative common sense
 an aid for executives to make decisions
 ways to effectively use information
2. Modeling and Models
 Physical models
 Graphic models
 Quantitative models
* deterministic
* probabilistic
* descriptive
* prescriptive
-- Advantages of using models
 cost
 time
 risk
 what-if question
3. Problem Solving and Decision Making Process
 define the problem
 choose the criteria (single or multiple)
 formulate model
 collect data
 verify model
 choose an alternative (quantitatively or qualitatively)
 implement the selection
 evaluate the result
4. Quantitative Models
-- When to use?
 problem is complex
 problem is important and needs a thorough analysis
 problem is new
 problem is repetitive and time consuming
 what if questions
-- Components of quantitative models
 controllable inputs (decision variables)
 uncontrollable inputs (parameters): deterministic or stochastic
 objective function
 constraints
 outputs
-- An Example:
The Erlanger Manufacturing Company makes two products. The contribution to profit is
$25 for each unit of product 1 and $30 for each unit of product 2. The labor-hour
requirements for the products in each of two production departments are summarized
below:
Product
1
2
Department A
1.5
3.0
Department B
2.0
1.0
For the current production period, Erlanger has the following number of labor-hours
available in each department: 450 hours in Department A and 350 hours in Department B.
Assume that Erlanger’s objective is to maximize the total contribution to profit.
5. Management Science In Practice
-- Optimization techniques
 linear programming
 integer programming
 network models
 project management
 goal programming
-- Other statistical techniques
 simulation
 forecasting
 queuing models
 inventory models
-- Methods used most frequently in corporate world
 regression, forecasting
 simulation
 project management
 linear programming
 queuing
-- Techniques most familiar to practitioners
 linear programming
 simulation
 network models
 queuing
-- Sample applications of optimization techniques
Organization
Nature of Applications
Monsanto
Optimize production operations in chemical plants to
Corp.
meet production targets with minimum cost.
Weyerharuser Optimize how trees are cut into wood products to
Co.
maximize their yield.
Eletrobras/C Optimally allocate hydro and thermal resources in the
EPAL, Brazil national electrical generating system.
United
Schedule shift work at reservation offices and airports
Airlines
to meet customer needs with minimum cost.
San Francisco Optimally schedule and deploy police patrol officers
Police
with a computerized system.
Department
Texaco, Inc. Optimally blend available ingredients into gasoline
products to meet quality and sales requirements.
Yellow
Optimize the design of a national trucking network
Freight
and the routing of shipments
System, Inc.
American
Design a system of fare structures, overbooking, and
Airlines
coordinating flights to increasing revenues.
New Haven Design an effective needle exchange program to
Health Dept. combat the spread of HIV/AIDS.
6. History of Linear Programming






started in WWII
simplex method by Dantzig
computer revolution
business applications
ellipsoid algorithm
interior point method
Annual
Savings
$2 mil
$15 mil
$43 mil
$6 mil
$11 mil
$30 mil
$17.3
mil
$500
mil
33%
less
HIV