ISE 195 Introduction to Industrial Engineering

1
ISE 195Introduction to Industrial Engineering
2
Lecture 3Mathematical Optimization(Topics in
ISE 470 Deterministic Operations Research Models)
3
What is OR?

• Operations Research Study of Mathematical
  Optimization
• OR is short for Operations Research
• Home professional society the Institute for
  Operations Research and the Management Sciences
  (INFORMS)
• Researchers in OR focus on how to improve the
  theory (mathematical) and algorithm
  (computational) aspects of formulating and
  solving mathematical optimization problems
• Basic OR tools are helpful when a decision
  problem has many variables and constraints that
  can be described with a linear function
• Web Site, http//www.informs.org

4
INFORMS
5
ISE and OR

• Industrial Systems Engineering Branch of
  Engineering Concerned with Integrating and
  Improving Systems
• ISEs can use OR tools to do this, usually with
  the help of a computer
• ISEs focus on problems in Logistics, Scheduling,
  Healthcare, etc. that have an optimization focus
  and that have a scale large enough to utilize
  OR tools
• ISEs use OR to formulate design problems and
  generate solutions

6
Why the Comparison?

• Pure Operations Research has a heavy mathematical
  and computational orientation
• There are many mathematical details to
  formulating problems successfully
• There are many computational (computer
  programming, algorithmic) details to successfully
  finding optimal solutions to a stated problem
• ISE applications of OR do not have as high a
  theoretical mathematical or algorithmic content
• ISEs try to use the correct technique to improve
  the integrated system under investigation,
  including OR when appropriate

7
Model Formulation and Solution

• Mathematical optimization model formulation and
  solution
• Represent the system or phenomena in some set of
  algebraic structures
• Uses the decision-makers view, usually
  different from the real-world view
• Simulation models have a closer mapping to real
  world details
• Encode the resulting model in a computer via some
  modeling language
• GAMS, X-Press, Excel
• Find a solution to the model (hopefully
  optimal)
• Solution algorithms vary for linear, nonlinear
  and integer decision variables
• Solutions generated suggest new designs for a
  system
• A prescriptive decision technique
• Trying to find a best solution with which to
  prescribe how to make the best use of limited
  resources

8
Mathematical Modeling

• Describe system with set of algebraic equations
• Capture key relationships within the system
• Capture key behaviors in system
• Decisions for which insight needed are decision
  variables
• Goal embedded within the objective function
• Limitations/restrictions in constraints
• Physical constraints
• Logical constraints

9
General Form of Math Model
10
General Parametric Form
MAX (or MIN) c1X1 c2X2 cnXn Subject
to a11X1 a12X2 a1nXn lt
b1 ak1X1 ak2X2 aknXn lt bk
am1X1 am2X2 amnXn bm
11
A Simple Example
Blue Ridge Hot Tubs produces two types of hot
tubs Aqua-Spas Hydro-Luxes.
There are 200 pumps, 1566 hours of labor, and
2880 feet of tubing available. How many of
each type should be produced to maximize profits?
12
Model Formulation Process
1. Understand the problem 2. Identify the
decision variables X1number of Aqua-Spas to
produce X2number of Hydro-Luxes to
produce 3. State the objective function as a
linear combination of the decision
variables MAX 350X1 300X2
13
Model Formulation Process
4. State the constraints as linear combinations
of the decision variables 1X1 1X2 lt 200
pumps 9X1 6X2 lt 1566 labor 12X1 16X2
lt 2880 tubing 5. Identify any upper or lower
bounds on the decision variables X1 gt 0
X2 gt 0
14
Model Formulation Process
MAX (Objective function) 350X1 300X2 S.T.
(Constraint set) 1X1 1X2 lt 200 9X1 6X2
lt 1566 12X1 16X2 lt 2880 Non-negativity
X1 gt 0 X2 gt 0
15
Formulation in Excel Solver
16
2nd Simple Example Model

• Objective Determine production mix that
  maximizes the profit under the raw material
  constraint and other production requirements
  (detailed next).
• Maximize 50D 30C 6 MSubject to 7D 3C
  1.5M lt 2000 (raw steel) D gt 100 (contract
  ) C lt 500 (cushions available) D, C, M
  gt 0 (Non-negativity) D and C are integers

17
What We are Looking For

• Want to find the best solution
• Solution must satisfy each of the constraints
• Constraints must be satisfied simultaneously
• Common area satisfying all the constraints is
  called the feasible region
• ANY point in the feasible region is a possible
  solution to the problem
• What we want is that feasible solution that
  provides the largest value of the objective
  function (the optimal solution)

18
Feasible Region of an LP
X2
1000
Profit 4360
700
500
X1
500
19
Linear Programming

• Assumptions of the linear programming model
• The parameter values are known with certainty.
• The objective function and constraints exhibit
  constant returns to scale.
• There are no interactions between the decision
  variables (the additivity assumption).
• The Continuity assumption Variables can take on
  any value within a given feasible range.

20
How Do We Solve an LP?

• Solving an LP Finding the best solution
  possible Finding the optimal solution
• In ISE 470, you will learn the Simplex Method
  of solving an LP
• Uses ideas from matrix algebra (i.e. pay
  attention in MTH 235)
• Can be performed by hand using matrix operations
  (pivots)
• The simplex algorithm is available in many forms
  in software
• Excel Solver Tool
• Many commercial solver packages (CPLEX, XPRESS,
  etc)

21
Integer Programming

• Many real life problems call for at least one
  integer decision variable.
• There are three types of Integer models
• Pure integer (AILP)
• Mixed integer (MILP)
• Binary (BILP) (zero-one variables, on-off)
• Unfortunately, these get quite hard to solve
• Real-world problems can have hundreds or
  thousands of variables and constraints
• Some problems are theoretically hard, even when
  they seem to have small numbers of elements
• Real benefit of these types of models is that we
  can use binary variables to represent a host of
  logical conditions within the mathematical
  formulation

22
Logical Contraints (1)
1. Three of six projects must be selected.
Y1 Y2 Y3 Y4 Y5 Y6 gt 3
2. No more than three of six projects can be
selected.
Y1 Y2 Y3 Y4 Y5 Y6 lt 3
23
Logical Contraints (2)
 3. Either Project 2 or Project 4 or Project 6
must be selected
Y2 Y4 Y6 1
4. Production level for Product 1 cannot exceed
the production level for Product 5.
X1 lt X5
24
Logical Constraint (3)
5. If Product 4 is produced, then at least 120
units of Product 4 must be produced.
X4 lt MY4 X4 gt 120Y4
6. Four projects are numbered in ascending
order. If any project selected, all lower
numbered projects must also be selected.
Y2 lt Y1 Y3 lt Y2 Y4 lt Y3
25
Logical Constraint (4)
7. If Project 3 selected, either Project 4 or
Project 5 must be selected.
Y3 lt Y4 Y5
8. If Product 6 produced, production must be 50
or 100 units.
X6 50Y61 100Y62 Y61 Y62 lt 1
26
Basic Solution Methods

• Linear models Simplex algorithm
• Fast in practice
• Lots of good sensitivity analysis information
• Integer models Branch and bound
• Can take very long time
• No sensitivity information
• Can be sped up with specific knowledge
• Nonlinear models
• Variety of solution methods, usually based on the
  derivative of the objective function
• Use Hill Climbing and other methods

27
Applications of Mathematical Optimization

• Budgeting, capital budgeting
• Resource allocation, how much to make
• How much to make, how much to contract
• Assignment of personnel to jobs
• Any other kind of assignment application
• Funding allocation, investment planning
• Inventory planning
• Facility layout and planning

28
Applications of Mathematical Optimization

• Routing
• Ever use Google Maps?
• Pick-up and delivery of items
• Selection of items for loading onto delivery
  trucks
• Scheduling
• Jobs onto machines
• Patients to doctors
• Doctors to shifts, workers to shifts
• Packing problems, cutting problems
• How to cut patterns from material
• And the list goes on and on

29
Other Related Areas

• Non-linear optimization
• These involve non-linear functions within the
  formulations
• Most applicable solution methods are
  gradient-based local search procedures
• Modern heuristic optimization
• These involve integer programming models that are
  particularly difficult to solve
• Also involve non-linear models with forms for
  which the existing non-linear solution codes have
  a particularly hard time solving
• These are essentially search methods with some
  pretty strange analogies to nature
• Genetic Algorithms
• Ant-Colony Algorithms
• Simulated Annealing Algorithms

30
Large-Scale Applications

• Airline Crew Scheduling
• Scenario A late-day storm has canceled many
  flights for American Airlines in and out of
  Chicago OHare Airport. Many planes scheduled to
  come to Chicago and leave Chicago have not made
  it to their planned destination for the end of
  the day
• Problem How should we reassign crews and planes
  to the routes for the next day, to optimize our
  use of people, aircraft and other resources?
• Companies such as American Airlines, IBM,
  Hewlett-Packard, UPS, FedEx, Toyota have
  large-scale problems that are well suited to
  mathematical optimization

31

• Questions?