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Managerial Decision Making DSCN 205

Discover the field of management science using models & optimization techniques to make effective business decisions efficiently. Learn how to build computer models, benefit from modeling, and analyze optimization problems.

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Managerial Decision Making DSCN 205

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  1. Managerial Decision Making DSCN 205 Introduction to Modeling and Decision Analysis

  2. What is Management Science? • A field of study that uses computers, statistics, and mathematics to solve business problems. • Also known as: • Operations Research • Decision Science • Modeling in DSCN 205: • Deterministic Modeling : Deterministic Optimization. • Probabilistic Modeling : Simulation, Queueing. 2

  3. The Modeling Approachto Decision Making • Everyone uses models to make decisions. • Types of models: • Mental (arranging furniture) • Visual (blueprints, road maps) • Physical/Scale (aerodynamics, buildings) • Mathematical (what we’ll be studying) 3

  4. What is a “Computer Model”? • A set of mathematical relationships and logical assumptions implemented in a computer as an abstract representation of a real-world object of phenomenon. • Spreadsheets provide the most convenient way for business people to build computer models. 4

  5. Optimization (Introduction) • Many decision problems are about how to use limited resources such as (Constraints): • Time • Money • Workers • Oil • Land • Space • Mathematical Progamming (MP) is a field of ManagementScience that finds the optimal, or most efficient, way of using limited resources to achieve the objectives of an individual of a business; hence the name Optimization. 5

  6. Benefits of Modeling • Economy - It is often less costly to analyze decision problems using models. • Timeliness - Models often deliver needed information more quickly than their real-world counterparts. • Feasibility - Models can be used to test things that would be impossible. • Insight - Modeling gives us understanding that improves decision making. 6

  7. Characteristics ofOptimization Problems • All optimization problems involve: • Decisions • Objectives • Constraints 9

  8. Example of a Mathematical Model • Production planning – the decisions are: • X1 – the number of units of product 1 to produce • X2 – the number of units of product 2 to produce • The objective is to maximize the total profit Y • Y= f(X1, X2) • There are certain constraints restricting what production decisions are feasible (what it’s possible to do). 10

  9. Example • A manufacturing run produces two types of product (1 and 2). One unit of Product 1 is sold at 4$ and Product 2 is sold at 5$. • Decision variables: • X1 – the number of units of product 1 to produce • X2 – the number of units of product 2 to produce • The objective is to maximize the total profit Y • Y= f(X1, X2) = 4X1 + 5X2

  10. Example • The maximum holding inventory volume is 120m3 . One unit of Product 1 has a volume of 2m3 and one unit of Product 2 has a volume of 3m3. • Constraint: 2X1 + 3X2 <=120

  11. Example • A production run has to produce at least 20 items. • Constraint: X1 + X2 => 20 • Plot the Feasible Region.

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