Prescriptive analytics: CHAPTER Optimization 6 and simulation Learning Objectives for Chapter 6 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics Understand the basic concepts of analytical decision modeling Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support Describe how spreadsheets can be used for analytical modeling and solutions Explain the basic concepts of optimization and when to use them Describe how to structure a linear programming model Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking Understand the concepts and applications of different types of simulation Understand potential applications of d iscrete event simulation Copyright C2018 Pearson Education, Inc
1 Copyright © 2018Pearson Education, Inc. Prescriptive Analytics: Optimization and Simulation Learning Objectives for Chapter 6 ▪ Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics ▪ Understand the basic concepts of analytical decision modeling ▪ Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support ▪ Describe how spreadsheets can be used for analytical modeling and solutions ▪ Explain the basic concepts of optimization and when to use them ▪ Describe how to structure a linear programming model ▪ Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking ▪ Understand the concepts and applications of different types of simulation ▪ Understand potential applications of discrete event simulation CHAPTER 6
CHAPTER OUTLINE 6. 1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts 6.2 Model-Based Decision Making 6.3 Structure of Mathematical Models for Decision Support 6. 4 Certainty, Uncertainty, and risk 6.5 Decision Modeling with Spreadsheets 6.6 Mathematical Programming Optimization 6.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 6.8 Decision analysis with Decision Tables and Decision Trees 6.9 Introduction to simulation 6.10 Visual Interactive simulation ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 6.1 Review Questions What decision was being made in this vignette? The school district of Philadelphia was searching for private bus vendors to outsource some of their bus routes to locations in this scenario? predictive)might one need to make the best The vendors were evaluated based on five variables: cost, capabilities, reliance financial stability and business acumen 3. What other costs or constraints might you have to consider in award ing contracts for such routes? Add itionally you might look at minimizing the total number of vendors, the quality of service provided, the potential longevity of the contract, and the vendor s capacity 4. Which other situations might be appropriate for applications of such models Copyright C2018 Pearson Education, Inc
2 Copyright © 2018Pearson Education, Inc. CHAPTER OUTLINE 6.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts 6.2 Model-Based Decision Making 6.3 Structure of Mathematical Models for Decision Support 6.4 Certainty, Uncertainty, and Risk 6.5 Decision Modeling with Spreadsheets 6.6 Mathematical Programming Optimization 6.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 6.8 Decision Analysis with Decision Tables and Decision Trees 6.9 Introduction to Simulation 6.10 Visual Interactive Simulation ANSWERS TO END OF SECTION REVIEW QUESTIONS Section 6.1 Review Questions 1. What decision was being made in this vignette? The school district of Philadelphia was searching for private bus vendors to outsource some of their bus routes to. 2. What data (descriptive and or predictive) might one need to make the best allocations in this scenario? The vendors were evaluated based on five variables: cost, capabilities, reliance, financial stability, and business acumen. 3. What other costs or constraints might you have to consider in awarding contracts for such routes? Additionally you might look at minimizing the total number of vendors, the quality of service provided, the potential longevity of the contract, and the vendor’s capacity. 4. Which other situations might be appropriate for applications of such models?
There are a large number of other situations that might be applicable to this methodology. Some examples might include for construction work, or making advertising decisions Section 6.2 Review Questions List three lessons learned from modeling Models can be used for a wide array of applications. Some examples include making efficient purchasing decisions, making cost-effective travel plans, and efficiently managing a workforce 2. List and describe the major issues in modeling Two major issues in modeling focus on model management and knowledge-based modeling Model management focuses on the use and reuse of existing models in construction of solvable/usable models and predictive analysis techniques or the a fashion that maintains their integrity. Knowledge-based modeling allows 3. What are the major types of models used in DSS? DSS uses mostly quantitative models, whereas expert systems use qualitative, knowledge-based models in their applications 4. Why are models not used in industry as frequently as they should or could be? Models may not be used as often in industry as possible because users see them as being too difficult to create, the software too difficult to use, or fear of making mistakes in the creation of the model itself What are the current trends in modeling? These trends include o the development of model libraries and solution technique libraries o developing and using cloud-based tools and software to access and even run software to perform modeling, optimization, simulation o making analytics models completely transparent to the decision maker o build ing a model of a model to help in its analys Copyright C2018 Pearson Education, Inc
3 Copyright © 2018Pearson Education, Inc. There are a large number of other situations that might be applicable to this methodology. Some examples might include for construction work, or making advertising decisions. Section 6.2 Review Questions 1. List three lessons learned from modeling. Models can be used for a wide array of applications. Some examples include making efficient purchasing decisions, making cost-effective travel plans, and efficiently managing a workforce. 2. List and describe the major issues in modeling. Two major issues in modeling focus on model management and knowledge-based modeling. Model management focuses on the use and reuse of existing models in a fashion that maintains their integrity. Knowledge-based modeling allows for the construction of solvable/usable models and predictive analysis techniques. 3. What are the major types of models used in DSS? DSS uses mostly quantitative models, whereas expert systems use qualitative, knowledge-based models in their applications. 4. Why are models not used in industry as frequently as they should or could be? Models may not be used as often in industry as possible because users see them as being too difficult to create, the software too difficult to use, or fear of making mistakes in the creation of the model itself. 5. What are the current trends in modeling? These trends include: o the development of model libraries and solution technique libraries o developing and using cloud-based tools and software to access and even run software to perform modeling, optimization, simulation o making analytics models completely transparent to the decision maker o building a model of a model to help in its analysis
Section 6.3 Review Questions What is a decision variable? Decision variables describe alternative courses of action The decision maker controls the decision variables 2. List and briefly discuss the major components of a quantitative model Result(outcome) variables reflect the level of effectiveness of a system; that is, they ind icate how well the system performs or attains its goal(s) Decision variables describe alternative courses of action The decision maker controls the decision variables Uncontrollable Variables in any decision-making situation, there are factors that affect the result variables but are not under the control of the decision maker Intermed iate result variables reflect intermed iate outcomes in mathematical model Explain the role of intermed iate result variabl Intermediate result variables reflect intermed iate outcomes in mathemat models Copyright C2018 Pearson Education, Inc
4 Copyright © 2018Pearson Education, Inc. Section 6.3 Review Questions 1. What is a decision variable? Decision variables describe alternative courses of action. The decision maker controls the decision variables. 2. List and briefly discuss the major components of a quantitative model. • Result (outcome) variables reflect the level of effectiveness of a system; that is, they indicate how well the system performs or attains its goal(s). • Decision variables describe alternative courses of action. The decision maker controls the decision variables. • Uncontrollable Variables in any decision-making situation, there are factors that affect the result variables but are not under the control of the decision maker. • Intermediate result variables reflect intermediate outcomes in mathematical models. 3. Explain the role of intermediate result variables. Intermediate result variables reflect intermediate outcomes in mathematical models
Section 6.4 Review Questions Define what it means to perform decision making under assumed certainty, risk and uncertaint In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of action will be a decision made under risk is one in which the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence In decision making under uncertainty, the decision maker considers situations in which several outcomes are possible for each course of action How can decision-making problems under assumed certainty be handled? Decision-making problems under assumed certainty are hand led as if there is only one possible outcome How can decision-making problems under assumed uncertainty be handled? Decision-making problems under assumed uncertainty are handled as if multiple outcomes are possible How can decision-making problems under assumed risk be handled? Decision-making problems under assumed risk are handled as if multiple outcomes are possible, but the probability of each outcome is known Copyright C2018 Pearson Education, Inc
5 Copyright © 2018Pearson Education, Inc. Section 6.4 Review Questions 1. Define what it means to perform decision making under assumed certainty, risk, and uncertainty. In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of action will be. A decision made under risk is one in which the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence. In decision making under uncertainty, the decision maker considers situations in which several outcomes are possible for each course of action. 2. How can decision-making problems under assumed certainty be handled? Decision-making problems under assumed certainty are handled as if there is only one possible outcome. 3. How can decision-making problems under assumed uncertainty be handled? Decision-making problems under assumed uncertainty are handled as if multiple outcomes are possible. 4. How can decision-making problems under assumed risk be handled? Decision-making problems under assumed risk are handled as if multiple outcomes are possible, but the probability of each outcome is known