Decision support and BI systems chapter 04

Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 4: Modeling and Analysis Learning Objectives      4-2 Understand the basic concepts of management support system (MSS) modeling Describe how MSS models interact with data and the users Understand the well-known model classes and decision making with a few alternatives Describe how spreadsheets can be used for MSS modeling and solution Explain the basic concepts of optimization, simulation and heuristics; when to use which Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Learning Objectives       4-3 Describe how to structure a linear programming model Understand how search methods are used to solve MSS models Explain the differences among algorithms, blind search, and heuristics Describe how to handle multiple goals Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking Describe the key issues of model management Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Opening Vignette: “Model-Based Auctions Serve More Lunches in Chile”  Background: problem situation  Proposed solution  Results  Answer and discuss the case questions 4-4 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Modeling and Analysis Topics           4-5 Modeling for MSS (a critical component) Static and dynamic models Treating certainty, uncertainty, and risk Influence diagrams (in the posted PDF file) MSS modeling in spreadsheets Decision analysis of a few alternatives (with decision tables and decision trees) Optimization via mathematical programming Heuristic programming Simulation Model base management Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall MSS Modeling   A key element in most MSS Leads to reduced cost and increased revenue  DuPont Simulates Rail Transportation System and Avoids Costly Capital Expenses  Procter & Gamble uses several DSS models collectively to support strategic decisions   4-6 Locating distribution centers, assignment of DCs to warehouses/customers, forecasting demand, scheduling production per product type, etc Fiat, Pillowtex (…operational efficiency)… Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Major Modeling Issues   Problem identification and environmental analysis (information collection) Variable identification   Forecasting/predicting   4-7 More information leads to better prediction Multiple models: A MSS can include several models, each of which represents a different part of the decision-making problem   Influence diagrams, cognitive maps Categories of models >>> Model management Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Categories of Models Category 4-8 Objective Techniques Optimization of problems with few alternatives Find the best solution from Decision tables, a small number of decision trees alternatives Optimization via algorithm Find the best solution from a large number of alternatives using a stepby-step process Linear and other mathematical programming models Optimization via an analytic formula Find the best solution in one step using a formula Some inventory models Simulation Find a good enough solution by experimenting with a dynamic model of the system Several types of simulation Heuristics Find a good enough solution using “commonsense” rules Heuristic programming and expert systems Predictive and Predict future Forecasting, Markov Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall other models occurrences, what-if chains, financial, … Static and Dynamic Models  Static Analysis     Dynamic Analysis      4-9 Single snapshot of the situation Single interval Steady state Dynamic models Evaluate scenarios that change over time Time dependent Represents trends and patterns over time More realistic: Extends static models Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Decision Making: Treating Certainty, Uncertainty and Risk  Certainty Models     Uncertainty     Several outcomes for each decision Probability of each outcome is unknown Knowledge would lead to less uncertainty Risk analysis (probabilistic decision making)   4-10 Assume complete knowledge All potential outcomes are known May yield optimal solution Probability of each of several outcomes occurring Level of uncertainty => Risk (expected value) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Heuristic Programming     Cuts the search space Gets satisfactory solutions more quickly and less expensively Finds good enough feasible solutions to very complex problems Heuristics can be    4-36 Quantitative Qualitative (in ES) Traveling Salesman Problem >>> Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Heuristic Programming - SEARCH 4-37 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Traveling Salesman Problem  What is it?   4-38 A traveling salesman must visit customers in several cities, visiting each city only once, across the country Goal: Find the shortest possible route Total number of unique routes (TNUR): TNUR = (1/2) (Number of Cities – 1)! Number of Cities TNUR 12 60 20,160 20 1.22 1018 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall When to Use Heuristics When to Use Heuristics      Inexact or limited input data Complex reality Reliable, exact algorithm not available Computation time excessive For making quick decisions Limitations of Heuristics  4-39 Cannot guarantee an optimal solution Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Modern Heuristic Methods  Tabu search   Genetic algorithms   Survival of the fittest Simulated annealing  4-40 Intelligent search algorithm Analogy to Thermodynamics Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Simulation 4-41  Technique for conducting experiments with a computer on a comprehensive model of the behavior of a system  Frequently used in DSS tools Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Major Characteristics of Simulation     ! 4-42 Imitates reality and capture its richness Technique for conducting experiments Descriptive, not normative tool Often to “solve” very complex problems Simulation is normally used only when a problem is too complex to be treated using numerical optimization techniques Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Advantages of Simulation         4-43 The theory is fairly straightforward Great deal of time compression Experiment with different alternatives The model reflects manager’s perspective Can handle wide variety of problem types Can include the real complexities of problems Produces important performance measures Often it is the only DSS modeling tool for non-structured problems Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Limitations of Simulation      4-44 Cannot guarantee an optimal solution Slow and costly construction process Cannot transfer solutions and inferences to solve other problems (problem specific) So easy to explain/sell to managers, may lead overlooking analytical solutions Software may require special skills Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Simulation Methodology   Model real system and conduct repetitive experiments Steps: 4-45 Define problem Conduct experiments Construct simulation model Evaluate results Test and validate model Implement solution Design experiments Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Simulation Types  Stochastic vs Deterministic Simulation   Time-dependent vs Time-independent Simulation     Time independent stochastic simulation via Monte Carlo technique (X = A + B) Discrete event vs Continuous simulation Steady State vs Transient Simulation Simulation Implementation   4-46 In stochastic simulations: We use distributions (Discrete or Continuous probability distributions) Visual simulation Object-oriented simulation Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Visual Interactive Modeling (VIM) / Visual Interactive Simulation  Visual interactive modeling (VIM) (VIS) Also called        4-47 Visual interactive problem solving Visual interactive modeling Visual interactive simulation Uses computer graphics to present the impact of different management decisions Often integrated with GIS Users perform sensitivity analysis Static or a dynamic (animation) systems Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Model Base Management     MBMS: capabilities similar to that of DBMS But, there are no comprehensive model base management packages Each organization uses models somewhat differently There are many model classes    4-48 Within each class there are different solution approaches Relations MBMS Object-oriented MBMS Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall End of the Chapter  4-49 Questions / Comments… Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Printed in the United States of America Copyright © 2011 Pearson Education, Inc   Publishing as Prentice Hall 4-50 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall ... 4-2 Understand the basic concepts of management support system (MSS) modeling Describe how MSS models interact with data and the users Understand the well-known model classes and decision making... Influence Diagrams: Software  Analytica, Lumina Decision Systems   DecisionPro, Vanguard Software Co   Integrates influence diagrams and Excel, also supports Monte Carlo simulations PrecisionTree,... influence diagrams, decision trees and simulation Definitive Scenario, Definitive Software   Supports hierarchical (tree structured) diagrams DATA Decision Analysis, TreeAge Software   Supports hierarchical