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Hillier W6.pptx
1.
© 2015 McGraw-Hill
Education. All rights reserved. © 2015 McGraw-Hill Education. All rights reserved. Frederick S. Hillier ∎ Gerald J. Lieberman Chapter 2 Overview of the Operations Research Modeling Approach
2.
© 2015 McGraw-Hill
Education. All rights reserved. 2.1 Defining the Problem and Gathering Data • Elements of problem definition – Identify the appropriate objectives – Identify constraints – Identify interrelationships with other areas of the organization – Identify alternative courses of action – Define the time constraints 2
3.
© 2015 McGraw-Hill
Education. All rights reserved. Defining the Problem and Gathering Data • OR team typically works in an advisory capacity – Management makes the final decisions • Identify the decision maker – Probe his/her thinking regarding objectives • Objectives need to be specific – Also aligned with organizational objectives 3
4.
© 2015 McGraw-Hill
Education. All rights reserved. Defining the Problem and Gathering Data • Example of an objective in a for-profit organization – Maximum profit in the long run • More typical objective – Satisfactory profit combined with other defined objective 4
5.
© 2015 McGraw-Hill
Education. All rights reserved. Defining the Problem and Gathering Data • Parties affected by a business firm operating in a single country – Stockholders (owners) – Employees – Customers – Suppliers – Government (nation) • International firms obligated to follow socially responsible practices 5
6.
© 2015 McGraw-Hill
Education. All rights reserved. Defining the Problem and Gathering Data • Gathering relevant data necessary for: – Complete problem understanding – Input into mathematical models • Problem: too little data available – Solution: build management information system to collect data • Problem: too much data available – Solution: data mining methods 6
7.
© 2015 McGraw-Hill
Education. All rights reserved. • Models – Idealized representations – Examples: model airplanes, portraits, globes • Mathematical models – Expressed in terms of mathematical symbols – Example: Newton’s Law: F = ma • Mathematical model of a business problem – Expressed as system of equations 7 2.2 Formulating a Mathematical Model
8.
© 2015 McGraw-Hill
Education. All rights reserved. Formulating a Mathematical Model • Decision variables – Represent the decisions to be made – Examples: x1, x2, ….xn • Objective function – Performance measure expressed as a function of the decision variables – Example: profit, P 𝑃 = 3𝑥1 + 2𝑥2 + ⋯ 5𝑥𝑛 8
9.
© 2015 McGraw-Hill
Education. All rights reserved. Formulating a Mathematical Model • Constraints – Mathematical expressions for the restrictions – Often expressed as inequalities – Example: 𝑥1 + 3𝑥1𝑥2 + 2𝑥2 ≤ 10 • Constants in the equations called parameters of the model – Example: the number 10 in the above equation 9
10.
© 2015 McGraw-Hill
Education. All rights reserved. Formulating a Mathematical Model • Determining parameter values – Often difficult – Done by gathering data • Typical expression of the problem – Choose values of decision variables so as to maximize the objective function • Subject to the specified constraints • Real problems often do not have a single “right” model 10
11.
© 2015 McGraw-Hill
Education. All rights reserved. Formulating a Mathematical Model • What are the advantages of a mathematical model over a verbal description of the problem? – More concise – Reveals important cause and effect relationships – Clearly indicates what data is relevant – Forms a bridge to use computers for analysis 11
12.
© 2015 McGraw-Hill
Education. All rights reserved. Formulating a Mathematical Model • What are the disadvantages of mathematical models? – Often must simplify assumptions to make problem solvable • Judging a model’s validity – Desire high correlation between model’s prediction and real-world outcome – Testing (validation phase) – Multiple objectives may be combined into an overall measure of performance 12
13.
© 2015 McGraw-Hill
Education. All rights reserved. 2.3 Deriving Solutions from the Model • Sometimes a relatively simple step • Algorithms applied in a computer using a commercially-available software package • Search for the optimal solution – Common theme in OR problems – Recognize that the solution is optimal only with respect to model being used – More common goal: seek a satisfactory solution, rather than the optimal 13
14.
© 2015 McGraw-Hill
Education. All rights reserved. Deriving Solutions from the Model • Postoptimality analysis – Analysis done after finding an optimal solution – Very important part of most OR studies – Also called “what-if” analysis • What would happen if different assumptions were made? • Sensitivity analysis – Determines which variables affect the solution the most 14
15.
© 2015 McGraw-Hill
Education. All rights reserved. 2.4 Testing the Model • Model validation – Process of testing model output and improving the model until satisfied with output • Computer program analogy – Find and correct major bugs – Determine flaws in the model • Example of flaws: – Factors that were not incorporated – Parameters that were estimated incorrectly 15
16.
© 2015 McGraw-Hill
Education. All rights reserved. Testing the Model • Process varies with the model • Check for dimensional consistency of units – In all mathematical expressions • Vary values of parameters and/or decision variables – See if output behaves in a plausible way 16
17.
© 2015 McGraw-Hill
Education. All rights reserved. Testing the Model • Retrospective test – Uses historical data to reconstruct the past – Determines how well the model and solution would have performed • If it had been used • Disadvantages of the retrospective test – Uses same data as used to formulate the model – The past may not be indicative of the future 17
18.
© 2015 McGraw-Hill
Education. All rights reserved. 2.5 Preparing to Apply the Model • Install a well-documented system for applying the model – Includes the model, solution procedure, and implementation procedures – Usually computer-based • Databases and management information systems – Provide up-to-date model input 18
19.
© 2015 McGraw-Hill
Education. All rights reserved. Preparing to Apply the Model • Decision-support system – Interactive, computer-based system – Helps managers use data and models to support their decision-making 19
20.
© 2015 McGraw-Hill
Education. All rights reserved. 2.6 Implementation • Benefits of the study are reaped during implementation phase • Important for OR team to participate in launch – To make sure model is correctly translated • Success of implementation depends on support from: – Top management – Operations management 20
21.
© 2015 McGraw-Hill
Education. All rights reserved. Implementation • Steps in the implementation phase – OR gives management explanation of new system • How does it relate to operating realities? – Develop procedures to put system into operation • Responsibility of OR team and management – Initiate new course of action – OR team evaluates initial experience – Gather feedback 21
22.
© 2015 McGraw-Hill
Education. All rights reserved. Implementation • Steps in the implementation phase (cont’d.) – Document methodology • Work should be reproducible – Periodically revisit assumptions 22
23.
© 2015 McGraw-Hill
Education. All rights reserved. 2.7 Conclusions • Subsequent chapters focus on constructing and solving mathematical models • Phases described in the chapter are equally important • There are always exceptions to the “rules” – OR requires innovation and ingenuity 23
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