Overview of the Operations Research Modeling Approach

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Overview of the Operations Research Modeling Approach. Chapter 2: Hillier and Lieberman Dr. Hurley’s AGB 328 Course. Terms to Know.

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### Overview of the Operations Research Modeling Approach

Chapter 2: Hillier and Lieberman

Dr. Hurley’s AGB 328 Course

Terms to Know
• Data Mining, Decision Variables, Objective Function, Constraints, Parameters, Sensitivity Analysis, 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
• Test and Refine the Model and Procedures as Needed
• Apply the Model as Needed by Management
• Assist in Implementing Chosen Solution
Problem Definition
• This phase can take considerable time.
• 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.
Data Gathering
• Data gathering can take a considerable amount of time.
• The data might come from primary or secondary sources.
• The data may be known with near certainty or could be best guesses (“soft” data).
• 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.
• The main components are:
• The Objective Function
• The Decision Variables
• The Constraints
• It should be noted that when building models, you should start small.
Create or Utilize a Procedure to Generate Solutions
• Many algorithms exist for developing solutions for particular mathematical models.
• Usually these algorithms need computers to find the solution in a reasonable time period.
Testing and Refining the Model
• Most if not all models start out with having issues (bugs).
• 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.
• Bugs should be identified and fixed.