Quantitative Methods For Business 13th Edition Solution Manual
Complete Solution Manual Quantitative Methods For Business 13th Edition Questions & Answers with rationales (Chapter 1-17)
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Quantitative Methods For Business 13th Edition Solution Manual Contents Preface Chapter 1: Introduction Chapter 2: Introduction to Probability Chapter 3: Probability Distributions Chapter 4: Decision Analysis Chapter 5: Utility and Game Theory Chapter 6: Forecasting Chapter 7: Introduction to Linear Programming Chapter 8: Linear Programming: Sensitivity Analysis and Interpretation of Solution Chapter 9: Linear Programming Applications in Marketing, Finance, and Operations Management Chapter 10: Distribution and Network Models Chapter 11: Integer Linear Programming Chapter 12: Advanced Optimization Applications Chapter 13: Project Scheduling: PERT/CPM Chapter 14: Inventory Models Chapter 15: Waiting Line Models Chapter 16: Simulation Chapter 17: Markov Processes Appendix A: Building Spreadsheet Models Chapter 1 Introduction Learning Objectives 1. Develop a general understanding of the management science/operations research approach to decision making. 2. Realize that quantitative applications begin with a problem situation. 3. Obtain a brief introduction to quantitative techniques and their frequency of use in practice. 4. Understand that managerial problem situations have both quantitative and qualitative considerations that are important in the decision making process. 5. Learn about models in terms of what they are and why they are useful (the emphasis is on mathematical models). 6. Identify the step -by-step procedure that is used in most quantitative approaches to decision making. 7. Learn about basic models of cost, revenue, and profit and be able to compute the breakeven point. 8. Obtain an introduction to the use of computer software packages such as Microsoft Excel in applying quantitative methods to decision making. 9. Understand the following terms: model infeasible solution objective function management science constraint operations research deterministic model fixed cost stochastic model variable cost feasible solution breakeven point 1 - Chapter 1 Solutions: 1. Management science and operations research, terms used almost interchangeably, are broad disciplines that employ scientific methodology in managerial decision making or problem solving. Drawing upon a variety of disciplines (behavioral, mathematical, etc.), management science and operations research combine quantitative and qualitative conside rations in order to establish policies and decisions that are in the best interest of the organization. 2. Define the problem Identify the alternatives Determine the criteria Evaluate the alternatives Choose an alternative For further discussion see section 1.3 3. See section 1.2. 4. A quantitative approach should be considered because the problem is large, complex, important, new and repetitive. 5. Models usually have time, cost, and risk advantages over experimenting with actual situations. 6. Model (a) may be quicker to formulate, easier to solve, and/or more easily understood. 7. Let d = distance m = miles per gallon c = cost per gallon, Therefore Total Cost = We must be willing to treat m and c as known and not subject to variation. 8. a. Maximize 10x + 5y s.t. 5x + 2y 40 x 0, y 0 b. Controllable inputs: x and y Uncontrollable inputs: profit (10,5), labor hours (5,2) and labor -hour availability (40) Projected Profit and check on production time constraint Output Introduction c. Profit: $10/unit for x $ 5/ unit for y Labor Hours: 5/unit for x 2/ unit for y 40 labor -hour capacity Uncontrollable Inputs d. x = 0, y = 20 Profit = $100 (Solution by trial-and-error) e. Deterministic - all uncontrollable inputs are fixed and known. 9. If a = 3, x = 13 1/3 and profit = 133 If a = 4, x = 10 and profit = 100 If a = 5, x = 8 and profit = 80 If a = 6, x = 6 2/3 and profit = 67 Since a is unknown, the actual values of x and profit are not known with certainty. 10. a. Total Units Received = x + y b. Total Cost = 0.20 x +0.25 y c. x + y = 5000 d. x 4000 Kansas City Constraint y 3000 Minneapolis Constraint e. Min 0.20 x + 0.25 y s.t. x + y = 5000 x 4000 y 3000 x, y 0 Mathematical Model 0 0 y 40 + 5 y 10x + 5y Max s.t. 10 x x Controllable Input Production Quantities x and y
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