基本信息出版社：清华大学出版社
页码：542 页
出版日期：2009年09月
ISBN：7302206791/9787302206798
条形码：9787302206798
版本：第1版
装帧：平装
开本：16
正文语种：英语
内容简介 《管理决策:理论与实践》内容简介：Management Science is of paramount importance in this effort. It provides decision makers with an extensive range of methodologies, skills, models and software tools that are necessary in order to effectively address a wide range of decision problems. More specifically, Management Science has been very helpful in management and decision making by contributing: a) A rational, systematic and robust methodology that can be followed when addressing a decision problem, b) A rich set of models, techniques and tools that can be used to structure, understand and solve the problem, and c ) A rich interface environment that helps the decision maker understand a solution, evaluate alternative solutions and come up with the preferred one. In all of these steps, the decision maker is supported by specialized easy-to-use software, which often come as additions (add-ins) to popular software like Excel, or are part of corporate Business Intelligence Systems or Decision Support Systems.
编辑推荐 《管理决策:理论与实践》是由清华大学出版社出版的。
目录
PAPTⅠ THE FUNDAMENTALS OF MANAGERIAL DECISION MAKING
CHAFFER 1 INTRODUCTION TO MANAGEMENT SCIENCE
1.1 Introduction to Managerial Decision Making
1.2 Trends Affecting Decision Making Today
1.3 Key Characteristics of Management Science for Decision Making
1.4 The Use of Models for Making Decisions
1.5 The Use of Software in Decision Making
1.6 Applications of Management Science in Business
References
CHAFFER 2 AVOIDING BAD DECISIONS: The Methodology of
Decision Making
2.1 Introduction
2.2 Decision Making Traps
2.3 Decision Making Tips
2.4 The Rational Methodology for Decision Making
2.5 Identification of the Problem
2.6 Analysis of the System
2.7 Formulation of the Objectives
2.8 Initial System Design
2.9 Detailed System Design
2.10 Solution Implementation and Monitoring
References
PAPTⅡ MODELS IN MANAGERIAL DECISION MAKING
CHAPTER 3 LINEAR PROGRAMMING
3.1 Introduction
3.2 Characteristics of LP Problems
3.3 A Maximization Problem
3.4 A Trial-and-Error Approach in Solving LP Problems
3.5 Graphical Solution of a LP Problem
3.6 A Minimization Problem
3.7 General Formulation and Assumptions of LP Models
3.8 Solving LP Problems
Problems
References
CHAPTER 4 USING SOLVER TO SOLVE LINEAR PROGRAMMING
PROBLEMS
4. 1 Introduction
4.2 Introducing the Model in Excel
4.3 Solving the Problem
4.4 Understanding and Analyzing the Solution-SOLVER Reports
4.5 Solving Integer Programming Problems with SOLVER
4.6 Solving Non-Linear Programming Problems with SOLVER
4.7 Conclusions
Problems
References
CHAPTER 5 SENSITIVITY ANALYSIS IN LINEAR
PROGRAMMING
5.1 Introduction
5.2 An Example
5.3 Dual Prices in LP
5.4 Reduced Costs in LP
5.5 Changes in the Objective Function's Coefficients
5.6 Changes in the Right Hand Sides (RHS) of the Constraints
5.7 Evaluation of a New Activity
5.8 Conclusions
Problems
References
CHAPTER 6 INTEGER PROGRAMMING
6.1 Introduction
6.2 Formulating IP Problems with Binary Variables
6.3 An Investment Example
6.4 Formulating IP Problems with Fixed Costs and/or Discounts
6.5 Solving IP Problems
6.6 Heuristic Methods to Solve IP Problems
6.7 Conclusions
Problems
References
CHAPTER 7 MULTI-CRITERIA DECISION MAKING
7. I Introduction
7.2 Empirical Methods
7.3 Goal Programming
7.4 The Analytical Hierarchy Process
7.5 Using Expert Choice to Solve Multicriteria Problems
Problems
References
CHAPTER 8 STATISTICAL METHODS IN DECISION
MAKING
8.1 Introduction to Forecasting
8.2 Key Concepts about Forecasting
8.3 The Moving Averages Forecasting Method
8.4 Exponential Smoothing Forecasting Method
8.5 Other Forecasting Methods
8.6 Linear Regression
8.7 Multiple Regression
8.8 Discriminant Analysis
8.9 Using SPSS for Statistical Analysis
Problems
References
CHAPTER 9 DECISION ANALYSIS
9.1 Introduction
9.2 Key Concepts about Decision Analysis
9.3 Criteria for Making Decisions under Uncertainty
9.4 The Expected Value of Perfect Information
9.5 Introduction to Decision Trees
9.6 Calculating the Risk Profile a Strategy
9.7 Sensitivity Analysis
9.8 Using Precision Tree to Solve Decision Analysis Problems
Problems
References
CHAPTER 10 SIMULATION
10.1 Introduction
10.2 Key Characteristics of Simulation
10.3 Implementation of Simulation under Conditions of Uncertainty
10.4 Simulation of Queuing Systems
10.5 Simulation of an Inventory System
10.6 Analysis of Simulation Results
10.7 Using Simulation for Risk Management
10.8 Using Simulation for Business Process Reengineering
Problems
References
PAPTⅢ IMPLEMENTING MANAGEMENT SCIENCE IN PRACTICE
CHAPTER 11 GETTING TO KNOW YOUR CUSTOMER
11.1 Introduction
11.2 Determining Customer Satisfaction
11.3 Designing New Products
11.4 Sales-Advertising Response Analysis
11.5 Forecasting Sales of New Products
11.6 Identifying Areas of Improvement
11.7 Studying Product Positioning
11.8 Identifying Market Segments
11.9 Identifying Prospect Customers
Problems
References
CHAPTER 12 MARKETING AND SALES MANAGEMENT
12.1 Introduction
12.2 A Product Selection Problem
12.3 Design of Sales Network
12.4 Selection of Communication Media
12.5 Selection of Location
12.6 Design of New Product Marketing Strategy
12.7 Design of Promotion Strategy
12.8 Sales Strategy
12.9 Evaluation of Customer Value for CRM Implementation
……
CHAPTER 13 PRODUCTION AND INVENTORY MANAGEMT
CHAPTER 14 NETWORKS AND TRANSPORT PROBLEMS
CHAPTER 15 LOGISTICS AND SUPPLY CHAIN MANAGEMENT
……
序言 Globalization, the economic uncertainties, and the explosive growth of information andcommunication technologies have transformed the business world radically. A constant pres-sure on companies to reduce costs while improving quality, efficiency, service and innova-tion, is on the daily agenda. In this complex and continuously evolving environment, acompany's ability to react to the challenges becomes of vital importance.
In this new business reality, managers must have the ability to make decisions quicklyand thoroughly, by taking evidence into account, and evaluating consequences and alterna-tive plans in terms of growth, profitability and acceptable risk. In today's environment, be-ing able to make good and fast decisions in all areas of business, including financial and riskmanagement, marketing strategy and customer understanding, production and logistics, pro-curement and supply chain management, human resources management and development,IT-based process redesign, is becoming strategically important.
Management Science is of paramount importance in this effort. It provides decisionmakers with an extensive range of methodologies, skills, models and software tools that arenecessary in order to effectively address a wide range of decision problems. More specifical-ly, Management Science has been very helpful in management and decision making by con-tributing: a） A rational, systematic and robust methodology that can be followed when ad-dressing a decision problem, b） A rich set of models, techniques and tools that can be usedto structure, understand and solve the problem, and c ） A rich interface environment thathelps the decision maker understand a solution, evaluate alternative solutions and come upwith the preferred one. In all of these steps, the decision maker is supported by specializedeasy-to-use software, which often come as additions （add-ins） to popular software like Ex-cel, or are part of corporate Business Intelligence Systems or Decision Support Systems.
文摘 插图：
When the decision variables can only take integer values, the condition of divisibility of Lin- ear Programming does not hold. For example, in the class of problems where the variables can only take values 0 or 1 （0/1 problems）, such as project selection problems, the decision maker has to choose among specific alternatives, and therefore, the decisions are either yes or no for each alternative no "partial" answers are acceptable. To the degree that there exists an objective, which the decision maker has to achieve, and the contributions of eachchoice to this objective can be calculated, these problems can be formulated as Integer Pro-gramming problems and can be solved using Solver.
In order to use Solver in Integer Programming problems, we have to add the constraintsof integrality to the constraints of the model. We implement this through the "Add Con-straint" dialog box, in which we select INT （ for generally integer variables） or BIN （ for bi-nary variables of the type 0 or 1 ）. To input an Integer Programming problem to Excel, wefollow the same steps as in the case of Linear Programming, with the only difference beingthe definition of integrality of variables.Solving Non-Linear Programming Problems with SOLVERSolver can also be used to solve （with a certain approximation） Non-Linear Programmingproblems. This is done through a series of choices that have to do with the way the problemis solved. In order to define these choices, we select at the "Definition of Solver Parame-ters" of the problem （ table 4.8 ）, we left click at "Options" and in the appearing dialog boxwe define （left click again） the following choice:Hypothesis of Linear model