Lecture 7 Introduction to Linear Programming
LECTURE 7.1: In this lecture we shall:
Introduce the concepts and terminology of linear programming.
Get you to the point where you can write formulations for small problems and interpret
formulations for any
Lecture 12 Facility Location
In this lecture we shall learn about the:
! Understand factors effecting facility location.
" Understand different facility location problems.
# Learn how to solve a few of these problems.
Figure 1: Webers Classification of In
Lecture 11 Vehicle Routing
LECTURE 11.1: In this lecture we shall learn about the:
Issues in vehicle routing.
TSP heuristic for vehicle routing
Sweep heuristic for vehicle routing
1) ISSUES IN VEHICLE ROUTING
A. Problem contexts for Vehicle Routing
The fo
Lecture 6 Three Solution Methods
In this lecture we shall get an introduction to:
Heuristics
Optimization
Simulation
We shall learn the pros and cons of each and
Do an example of each.
DEFINITIONS
HEURISTIC is a process for solving a problem that gene
Lecture 3 Time Series Approaches: Introduction to Time
Series Methods and Measuring Forecasting Error
LECTURE 3.1 In this lecture we shall learn about:
Decomposing a time series
Simple time series approaches
1) DECOMPOSING A TIME SERIES
A. DESCRIBING A
Lecture 10 Routing
LECTURE 10.1: In this lecture we shall learn about the:
Minimum spanning tree problem.
Postman problem.
Traveling salesman problem.
Shortest path problem.
Background: A Graph
Figure 1: A Graph
In mathematics and computer science a GRAPH
Lecture 4 NonStationary Methods
LECTURE 4.1: In this lecture we shall learn about:
Exponential Smoothing for Trended Data
Exponential Smoothing for Seasonal Data
Exponential Smoothing for Trended & Seasonal Data
Regression Techniques
1) HOLTS MODEL F
Lecture 13: Intro to Network Modeling
To motivate the use of modeling
To recognize modeling opportunities
To understand the complexity in terms of
variables and data required
To understand the importance of sensitivity
analysis
To be aware of modelin
1) Profit Maximization Problem
i) Decision Variables
Let Sij= the number of units shipped from Plant I to Customer j
i= A, B, C
j= 1,5
ii) Objective Function
MAX 9SA1 + 3SA2 + 4SA3 + 7SA4 + 5SA5 +11SB1 + 4SB2 + 5SB3 + 8SB4 + 6SB5 +12SC1 + 8SC2 + 7SC3 + 9S
Formulate the following problems as Mathematical Programs (assuming linear). Show
both the full mathematical formulation and the Tableau form, formulate the program in
Excel and use Solver to solve the problem.
Plants
1. BioFuel systems has manufacturing
Lecture Notes: Lecture 9 Advanced Topics in Inventory
Management
LECTURE 9.3: MANAGING UNCERTAINTY In this lecture we shall learn:
Learn about managing uncertainty
Using aggregation strategies such as: Centralization, Component Commonality, and
Postpone
Lecture Notes: Lecture 9 Advanced Topics in Inventory
Management
LECTURE 9.2: DRP In this lecture we shall learn:
! Learn about managing multiechelon inventory
" How to construct and implement a distribution resource plan (DRP)
1) A MULTIECHELON INVENTO
Lecture Notes: Lecture 9 Advanced Topics in Inventory
Management
LECTURE 9.1: INVENTORY MODELS In this lecture we shall:
Review some basic inventory principals
Learn to set policies for managing single item inventory in the presence of variable
demand a
Lecture 2 Introduction to Forecasting
Lets start by exploring why we need to forecast and then examine a taxonomy or c lassification
of forecasting procedures.
1) FORECASTING DEFINED
the prediction, projection or
e stimation of the occurrences of uncertai
Lecture 5: Forecasting in
Practice
Learning from Steve Robeano s Article on Ross
So you have a forecast. Now what?
1
Learning from Ross Labs
2
What Can We Do to Diminish Our
Dependence on Forecasts?
3
What Do You Do with a
Forecast?
Forecasting is half
Appendix
KMedian Problems
Lecture 10
1. Enumeration method for solving the 2 Median Problem
3000 2000 4000 1000 100
A
B
C
D
E
AB
AC
AD
AE
BC
BD
BE
CD
CE
DE


2
4
1

1

2
2

1
2

2

1
3
4

1


2
1
1

2

1
1

2
4

3
2


3
2
1

2

2
1
2

Total Cost Annual Holding Cost Annual Ordering Cost
Q
D
H
S
2
Q
where
Q Order quantity in units
H Holding (carrying) cost per unit
D Demand, usually in unit per year
S Ordering cost
Order cost Total carrying costs to end of period T
T
Daily order cost =