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Introduction to Manufacturing Systems / Operations Research

Introduction to Manufacturing Systems / Operations Research. What is Operations Research? Management Science?

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Introduction to Manufacturing Systems / Operations Research

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  1. Introduction to Manufacturing Systems / Operations Research What is Operations Research? Management Science? Operations research is concerned with scientifically deciding how to best design and operate man-machine systems, usually under conditions requiring the allocation of scarce resources. (Operations Research Society of America, 1976).

  2. What is Operations Research? Management Science?Management Science is the application of a scientific approach to solving management problems in order to help managers make better decisions. (Intorduction to Management Science, B.W.Taylor III, 1999).OR is the abstraction of real world problems into mathematical models, and the development of techniques to optimally (or attempt to optimally) solve these problems. The application of OR is using the models and methods to aid decision makers (Schultz, 2002).Key Phrase: Management Science / Operations Research aides decision makers.

  3. History of MS/OROrigins in World War II- radar deployment policies- anti-aircraft fire control- fleet convoy sizing - detection of enemy submarineEarly MS/OR Efforts - Large Industries- automotive- oil&gas- airlinesAdditional MS/OR sectors- telecommunications- financial planning- health care- public service

  4. Nature of OR / Approach to Problem Solving - observe situation - define / formulate the problem - construct a model that attempts to sufficiently represent the situation - solve / validate the model - apply the model (aid decision maker)

  5. Why is OR Important? - Company profitability - Overseas competition (trade deficit article) - Erosion of US manufacturing . Can't compete on labor costMS/OR Today: Continental Airlines articles

  6. Modeling Definition - "simplified representation of something real"www.dictionary.com - • A small object, usually built to scale, that represents in detail another, often larger object. • a. A preliminary work or construction that serves as a plan from which a final product is to be made: a clay model ready for casting. b. Such a work or construction used in testing or perfecting a final product: a test model of a solar-powered vehicle. • A schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics

  7. Why Model? - reduce cost - save time - too much complexity - avoid disrupting production system

  8. Types of models - Math programming (Linear, Integer, non-Linear) - Network - Queueing - MDP (Markov Decision Processes) - Regression - Forecasting - Inventory - Dynamic programming - Simulation

  9. Principles of Modeling (Ravindran, Phillips, Solberg) • Do not build a complicated model when a simple one will suffice. • Beware of modeling the problem to fit the technique. • The deduction phase of modeling must be conducted rigorously (conclusions one draws from results). • Models should be validated prior to implementation. • A model should never be taken too literally.

  10. Principles of Modeling (Ravindran, Phillips, Solberg) cont. • A model should never be pressed to do, nor criticized for failing to do, that for which it was never intended. • Beware of overselling a model. • Some of the primary benefits of modeling are associated with the process of developing the model. • A model cannot be any better than the information going into it. • Models cannot replace decision makers.

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