Transcript Overview of the Operations Research Modeling Approach

Overview of the Operations
Research Modeling Approach
Chapter 2: Hillier and Lieberman
Chapter 2: Decision Tools for Agribusiness
Dr. Hurley’s AGB 328 Course
Terms to Know

Data Mining, Decision Variables, Objective
Function, Constraints, Parameters, Linear
Programming Model, Overall Measure of
Performance, Algorithm, Optimal, Solution,
Satisficing, Heuristic Procedures,
Suboptimal Solution, Metaheuristics,
Postoptimality Analysis, What-if Analysis,
Sensitivity Analysis, Sensitive Parameter,
Model Validation, Retrospective Test,
Decision Support System
Major Phases in Operation
Research Studies
Define the Problem
 Gather Relevant Data
 Develop a Mathematical Model
 Create or Utilize a Procedure to
Generate Solutions

Major Phases in Operation
Research Studies Cont.
Test and Refine the Model and
Procedures as Needed
 Apply the Model as Needed by
Management
 Assist in Implementing Chosen Solution

Problem Definition
Much effort needs to go into
understanding the problem at hand.
 You need to take the vague and
convoluted and make it confined and
precise.
 There is a need to understand the
appropriate objectives that need to be
met.
 This phase can take considerable time.

Data Gathering
Data gathering can take a considerable
amount of time.
 The data might come from primary or
secondary sources.

◦ What is the difference between the two?

The data may be known with near
certainty or could be best guesses (“soft”
data).
Data Gathering Cont.
Time may be spent conditioning the data.
 There may be very little data or
potentially too much.

Mathematical Modeling
A mathematical model is an abstraction of
a real world problem which is based on a
set of assumptions for the purposes of
tractability.
 It should be noted that when building
models, you should start small.

Mathematical Modeling Cont.

The main components are:
◦ The Objective Function
◦ The Decision Variables
◦ The Constraints
Create or Utilize a Procedure to
Generate Solutions

Many algorithms exist for developing
solutions for particular mathematical
models.
◦ What is an algorithm?

Usually these algorithms need computers
to find the solution in a reasonable time
period.
Testing and Refining the Model

Your model should be tested to see if the
solutions make sense.
◦ The model may need many levels of
refinement to be usable and worthwhile.
◦ It is useful to test a model out with known
solutions.
Testing and Refining the Model

Most if not all models start out with
having issues (bugs).
◦ Bugs should be identified and fixed.
◦ Finding bugs/issues in your model can be
challenging.
 You need to develop a set of skills for identifying
issues with your model.