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Overview of the Operations Research Modeling Approach

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

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

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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