NEW MEXICO INSTITUTE OF
MINING AND TECHNOLOGY
Department of Management
Management Science for Engineering
Management (EMGT 501)
Fall, 2005
Instructor : Toshi Sueyoshi (Ph.D.)HP address : www.nmt.edu/~toshiE-mail Address : [email protected] : Speare 143-A
1. Course Description:
The purpose of this course is to introduce
Management Science (MS) techniques for
manufacturing, services, and public sector.
MS includes a variety of techniques used in modeling
business applications for both better understanding
the system in question and making best decisions.
MS techniques have been applied in many
situations, ranging from inventory management
in manufacturing firms to capital budgeting in
large and small organizations.
Public and Private Sector Applications
The main objective of this graduate course is to
provide engineers with a variety of decisional tools
available for modeling and solving problems in a
real business and/or nonprofit context.
In this class, each individual will explore how to
make various business models and how to solve
them effectively.
2. Texts -- The texts for this course:
(1) Anderson, Sweeney and Williams
An Introduction to Management Science,
South-Western
(2) Chang Yih-Long, WinQSB , John Wiley&Sons
3. Grading:
In a course, like this class, homework problems are essential. We will have homework assignments. Homework has significant weight. The grade allocation is separated as follows:
Homework 20%
Mid-Term Exam 40%
Final Exam 40%
The usual scale (90-100=A, 80-89.99=B, 70-79.99=C, 60-69.99=D) will be used.
Please remember no makeup exam.
4. Course Outline:
Week Topic(s) Text(s)
1 Introduction and Overview Ch. 1&2
2 Linear Programming Ch. 3&4
3 Solving Linear Programming Ch. 5
4 Duality Theory Ch. 6
5 No Class
6 Project Scheduling: PERT-CPM Ch. 10
7 Inventory Models Ch. 11
8 Review for Mid-Term EXAM
Week Topic(s) Text(s)
9 Waiting Line Models Ch. 13
10 Waiting Line Models Ch. 13
11 Decision Analysis Ch. 14
12 Multi-criteria Decision Ch. 15
13 No Class
14 Forecasting Ch. 16
15 Markov Process Ch. 17
16 Review for FINAL EXAM
Linear Programming (LP):
A mathematical method that consists of an objective
function and many constraints.
LP involves the planning of activities to obtain an
optimal result, using a mathematical model, in
which all the functions are expressed by a linear
relation.
0,0
1823
1220
401
53
21
21
21
21
21
xx
xx
xx
xx
xxMaximize
subject to
A standard Linear Programming Problem
Applications: Man Power Design, Portfolio Analysis
Simplex method:
A remarkably efficient solution procedure for
solving various LP problems.
Extensions and variations of the simplex method
are used to perform postoptimality analysis
(including sensitivity analysis).
1x 2x 3x 4x 5xZ
3x4x5x
(0)(1)(2)(3)
21 53 xxZ 1x 3x
2x 4x21 23 xx 5x 18
12
4
0
(0)
(1)
(2)
(3)
(a) Algebraic Form
(b) Tabular Form
Coefficient of: RightSide
Basic VariableZ
Eq.
1 -3 -5 0 0 0 00 1 0 1 0 0 00 2 0 0 1 0 120 3 2 0 0 1 18
Duality Theory:
An important discovery in the early development
of LP is Duality Theory.
Each LP problem, referred to as ” a primal
problem” is associated with another LP problem
called “a dual problem”.
One of the key uses of duality theory lies in the
interpretation and implementation of sensitivity
analysis.
PERT (Program Evaluation and Review
Technique)-CPM (Critical Path Method):
PERT and CPM have been used extensively to
assist project managers in planning, scheduling,
and controlling their projects.
Applications: Project Management, Project
Scheduling
A 2
B
C
E
M N
START
FINISH
H
G
D
J
I
F
LK
4
10
4 76
7
9
8
54
62
5
0
0
Critical Path
2 + 4 + 10 + 4 + 5 + 8 + 5 + 6 = 44 weeks
Decision Analysis:
An important technique for decision making in
uncertainty.
It divides decision making between the cases
of without experimentation and with
experimentation.
Applications: Decision Making, Planning
Oil0.5 0.3Favorable
0.75Dry
0.85Dry
a
e
d
c
b
f
g
h
Drill
Sell
Drill
Sell
Sell
DrillOil0.14
Oil0.25
0.5DryDo
seism
ic
surv
ey
Unfavorable
0.7
No seismic survey
decision forkchance fork
Markov Chain Model:
A special kind of a stochastic process.
It has a special property that probabilities,
involving how a process will evolve in
future, depend only on the present state of
the process, and so are independent of events
in the past.
Applications: Inventory Control, Forecasting
Queueing Theory:
This theory studies queueing systems by
formulating mathematical models of their
operation and then using these models to derive
measures of performance.
This analysis provides vital information for
effectively designing queueing systems that
achieve an appropriate balance between the
cost of providing a service and the cost
associated with waiting for the service.
SS ServiceS facilityS
CCCC
Served customers
Served customers
C C C C C C
Queueing system
CustomersQueue
Applications: Waiting Line Design, Banking, Network Design
Inventory Theory:
This theory is used by both wholesalers and retailers
to maintain inventories of goods to be available for
purchase by customers.
The just-in-time inventory system is such an example
that emphasizes planning and scheduling so that the
needed materials arrive “just-in-time” for their use.
Applications: Inventory Analysis, Warehouse Design
Economic Order Quantity (EOQ) model
Q
Q
atQ
Time t
Inventory level
Batchsize
a
Q
a
Q20
Forecasting:
When historical sales data are available, statistical
forecasting methods have been developed for using
these data to forecast future demand.
Several judgmental forecasting methods use expert
judgment.
Applications: Future Prediction, Inventory Analysis
1/99 4/99 7/99 10/99 1/00 4/00 7/00
The evolution of the monthly sales of a product illustrates a time series
Mon
thly
sal
es (
unit
s so
ld)
10,000
8,000
6,000
4,000
2,000
0
Introduction to MS/OR
MS: Management Science
OR: Operations Research
Key components: (a) Modeling/Formulation
(b) Algorithm
(c) Application
Management Science (MS)
(1) A discipline that attempts to aid managerial
decision making by applying a scientific approach
to managerial problems that involve quantitative
factors.
(2) MS is based upon mathematics, computer
science and other social sciences like economics
and business.
General Steps of MS
Step 1: Define problem and gather data
Step 2: Formulate a mathematical model to
represent the problem
Step 3: Develop a computer based procedure
for deriving a solution(s) to the
problem
Step 4: Test the model and refine it as needed
Step 5: Apply the model to analyze the
problem and make recommendation
for management
Step 6: Help implementation
Linear Programming (LP)
[1] LP Formulation
(a) Decision Variables :
All the decision variables are non-negative.
(b) Objective Function : Min or Max
(c) Constraints
nxxx ,,, 21
21 32 xxMinimize
0,0
414
343..
21
21
21
xx
xx
xxts
s.t. : subject to
[2] Example
A company has three plants, Plant 1, Plant 2, Plant 3. Because of declining earnings, top management has decided to revamp the company’s product line.
Product 1: It requires some of production capacity
in Plants 1 and 3.
Product 2: It needs Plants 2 and 3.
The marketing division has concluded that the
company could sell as much as could be
produced by these plants.
However, because both products would be
competing for the same production capacity in
Plant 3, it is not clear which mix of the two
products would be most profitable.
The data needed to be gathered:
1. Number of hours of production time available per week in each plant for these new products. (The available capacity for the new products is quite limited.)
2. Production time used in each plant for each batch to yield each new product.
3. There is a profit per batch from a new product.
Production Timeper Batch, Hours
Production TimeAvailable
per Week, HoursPlant
Product
Profit per batch
1
2
3
4
12
18
1 2
1 0
0 2
3 2
$3,000 $5,000
: # of batches of product 1 produced per week : # of batches of product 2 produced per week : the total profit per week
Maximizesubject to
1x2x
Z
0x,0x
18x2x3
12x2x0
4x0x1
x5x3
21
21
21
21
21
1x0 2 4 6 8
2x
2
4
6
8
10
Graphic Solution
0,0 21 xx
Feasibleregion
1x0 2 4 6 8
2x
2
4
6
8
10
0,0 21 xx
41 x
Feasibleregion
1x0 2 4 6 8
2x
2
4
6
8
10
0,0 21 xx
122 2 x41 x
Feasibleregion
1x0 2 4 6 8
2x
2
4
6
8
10
0,0 21 xx
122 2 x41 x
1823 21 xx
Feasibleregion
1x0 2 4 6 8 10
2x
2
4
6
8
21 5310 xxZ
21 5320 xxZ
21 53 xx Maximize:
21 5336 xxZ
)6,2(
The optimal solution
The largest value
Slope-intercept form: 21 53 xxZ
Zxx
5
1
5
312
nn2211 xcxcxc
22222121
11212111
bxaxaxa
bxaxaxa
nn
nn
0,,0,0 21
2211
n
mnmnmm
xxx
bxaxaxa
Maximize
s.t.
[4] Standard Form of LP Model
[5] Other Forms
The other LP forms are the following:
1. Minimizing the objective function:
2. Greater-than-or-equal-to constraints:
.2211 nn xcxcxcZ
inin22i11i bxaxaxa
Minimize
3. Some functional constraints in equation form:
4. Deleting the nonnegativity constraints for
some decision variables:
ininii bxaxaxa 2211
jx : unrestricted in sign jjj xnxpx
[6] Key Terminology
(a) A feasible solution is a solution
for which all constraints are satisfied
(b) An infeasible solution is a solution
for which at least one constraint is violated
(c) A feasible region is a collection
of all feasible solutions
(d) An optimal solution is a feasible solution
that has the most favorable value of
the objective function
(e) Multiple optimal solutions have an infinite
number of solutions with the same
optimal objective value
,23 21 xxZ
1x
0,0
1823
21
21
xx
xx
122 2 x4
and
Maximize
Subject to
Example
Multiple optimal solutions:
21 2318 xxZ
1x0 2 4 6 8 10
2x
2
4
6
8
Feasibleregion
Every point on this red line
segment is optimal,
each with Z=18.
Multiple optimal solutions
(f) An unbounded solution occurs when
the constraints do not prevent improving
the value of the objective function.
2x
1x
Case Study - Personal Scheduling
UNION AIRWAYS needs to hire additional customer service agents.
Management recognizes the need for cost control while also consistently providing a satisfactory level of service to customers.
Based on the new schedule of flights, an analysis has been made of the minimum number of customer service agents that need to be on duty at different times of the day to provide a satisfactory level of service.
** ** ** * * * * * * * * * * * *
ShiftTime Period Covered Minimum #
of Agents needed
Time Period
6:00 am to 8:00 am8:00 am to10:00 am10:00 am to noon Noon to 2:00 pm2:00 pm to 4:00 pm4:00 pm to 6:00 pm6:00 pm to 8:00 pm8:00 pm to 10:00 pm10:00 pm to midnightMidnight to 6:00 am
1 2 3 4 548796587647382435215
170 160 175 180 195Daily cost per agent
The problem is to determine how many agents should be assigned to the respective shifts each day to minimize the total personnel cost for agents, while meeting (or surpassing) the service requirements.
Activities correspond to shifts, where the level of each activity is the number of agents assigned to that shift.
This problem involves finding the best mix of shift sizes.
1x2x3x
4x5x
: # of agents for shift 1 (6AM - 2PM)
: # of agents for shift 2 (8AM - 4PM)
: # of agents for shift 3 (Noon - 8PM)
: # of agents for shift 4 (4PM - Midnight)
: # of agents for shift 5 (10PM - 6AM)
The objective is to minimize the total cost of the agents assigned to the five shifts.
Min
s.t.54321 195180175160170 xxxxx
0ix )5~1( iall 15
52
43
82
73
64
87
65
79
48
5
54
4
43
43
32
321
21
21
1
x
xx
x
xx
xx
xx
xxx
xx
xx
x
15
52
43
82
64
87
79
48
5
54
4
43
32
321
21
1
x
xx
x
xx
xx
xxx
xx
x
)15,43,39,31,48(),,,,( 54321 xxxxx
Total Personal Cost = $30,610