System Engineering Basic Publication Time: 2011 Edition Introduction "System Engineering Basics" briefly introduces the basic concepts and general principles of system engineering, and from a practical perspective, systematically introduces the main foundation of system engineering-the most widely used operations research Several branches: linear programming, integer programming, objective planning, graph and network planning technology, dynamic planning, storage theory, decision theory, etc., and some powerful tools commonly used in system analysis and evaluation: system prediction, system simulation, analytic hierarchy process and The fuzzy comprehensive evaluation method uses many examples to introduce the establishment of various methods and mathematical models and their application in practice. Each chapter is accompanied by review questions and exercises, and readers can practice using relevant software. "System Engineering Basics" is applicable to engineering majors, and can also be used as training materials and self-study reference books for managers, engineering technicians and leading cadres at all levels of enterprises and institutions.
Chapter 1 System and System Engineering 1.1 Basic Concepts of the System 1.1.1 Concept of the System 1.1.2 Characteristics of the System 1.1.3 Classification of the System 1.2 Concepts of the System Engineering 1.2. 1 Meaning of System Engineering 1.2.2 Formation and Development of System Engineering 1.3 Methodology of System Engineering 1.3.1 Three-dimensional Structural System 1.3.2 Methodology of Soft Science System Engineering 1.3.3 Technology of System Engineering Contents 1.3.4 Application Examples Chapter 2 Linear Programming 2.1 Linear Programming Problems and Their Mathematical Models 2.1.1 Proposed Problems 2.1.2 General Forms of Linear Programming Mathematical Models 2.1.3 Linear Standard form of mathematical model of planning problem 2.1.4 Any model is standardized 2.2 Graphical method 2.2.1 Graphical method step 2.2.2 Several cases of solution of linear programming problem from graphical method 2 .3 Properties of Linear Programming Problem Solutions 2.3.1 Concepts of Linear Programming Problem Solutions 2.3.2 Several Basic Concepts in the Geometric Sense 2.3.3 Basic Theorems of Linear Programming Problems 2.3.4 Solving Linear Problems Basic Ideas for Planning Problems 2. 4 Simplex Method 2.4.1 Simplex Method Thought 2.4.2 Simple Table 2.5 Solution Two Stage Method (artificial variable method)
2.5.1 Constraint equation is a linear equation 2.5.2 Constraint equation is a mixed equation 2.6 Dual linear programming problem 2.6.1 Dual programming 2.6.2 Matrix representation of the simplex method 2.6. 3 Duality Theorem 2.6.4 Dual Simplex Method 2.7 Transportation Problem 2.7.1 Mathematical Model of Transportation Problem 2.7.2 Worksheet Method 2.7.3 Transportation Problem with Unbalanced Production and Sales 2.8 Assignment problem 2.8.1 Mathematical model of assignment problem 2.8.2 Hungarian solution 2.8.3 Nonstandard form assignment problem 2.9 Integer programming 2.9.1 Branch and bound method 2.9.2 Hidden Enumeration Method for Solving 0-1 Planning 2.9.3 Application Examples 2.10 Application Examples 2.10.1 Production Planning Problem 2.10.2 Construction Planning Problem 2.10.3 Investment Planning Problem 2.10. 4 Coal Seam Collocation and Mining Problem 2.0.5 Production Planning Optimization of Large Coal Enterprises 2.10. Transportation Problem Exercises Chapter 3 Goal Planning 3.1 Mathematical Model of Goal Planning 3.1.1 Example of Goal Planning Problem 3.1 .2 Basic Concepts of Goal Planning 3.2 Graphical Approach to Goal Planning 3.3 Application Exercises of Goal Planning Chapter 4 Diagrams and Network Planning Techniques 4.1 Basics of Diagrams Concept 4.1.1 Graph, Vertex, Edge, Network 4.1.2 Association, Adjacent 4.1.3 Undirected Graph, Directed Graph 4.1.4 Chain, Cycle, Connected Graph, Partial Graph 4. 1.5 Matrix Representation of Graphs 4.2 Trees 4.2.1 Trees and Their Properties 4.2.2 Minimum Tree Problem 4.2.3 Minimum Tree Solving Method 4.3 Shortest Path Problem 4.3.1 Introduction?
4.3.2 Shortest path algorithm (labeling method)
4.4 Maximum flow of the network 4.4.1 Introduction 4.4.2 Basic concepts and theorems 4.4.3 Intercept sets and intercepts 4.4.4 Relationship between streams and intercept capacity 4.4.5 Seeking networks Labeling method for maximum flow 4.5 Minimum cost maximum flow problem 4.5.1 Solving steps 4.5.2 Calculation examples 4.6 Network planning technology 4.6.1 Basic concepts and drawing rules for network diagrams 4.6. 2 Network planning time and key routes 4.6.3 Network planning optimization exercises Chapter 5 Dynamic programming 5.1 Multi-stage decision-making process and examples 5.1.1 Multi-stage decision-making problem 5.1.2 Multi-stage decision-making problem Example 5.2 Reverse order recursive method 5.3 Basic principles and basic concepts of dynamic programming 5.3.1 Basic principles of dynamic programming (Bellman optimization principle)
5.3.2 Basic concepts of dynamic programming 5.4 Application of dynamic programming in multi-stage decision-making 5.4.1 Resource allocation problem 5.4.2 Dynamic programming inventory control model 5.4.3 Applying dynamic programming to solve Linear programming problem 5.4.4 Knapsack problem 5.5 Multi-dimensional variable problem 5.6 Advantages and limitations of dynamic programming methods 5.6.1 Advantages of dynamic programming methods 5.6.2 Limitations of applying dynamic programming methods Chapter 6 Storage Theory 6.1 Basic Concepts 6.2 Inventory ABC Classification Management 6.2.1 ABC Classification Criteria 6.2.2 ABC Classification Management Principles 6.3 Deterministic Storage Model 6.3.1 Economic Order Batch Model 6 .3.2 Economic production batch model 6.3.3 Economic order batch model that allows stock-out 6.3.4 Economic order batch model with discounted price 6.3.5 Sensitivity analysis 6.4 Random storage model 6.4 .1 Single-period single-variety continuous random storage storage model 6.4.2 Multi-period single-variety random storage model exercises Chapter 7 Forecasting methods 7.1 Concept of prediction 7.1.1 Basic concepts 7.1 .2 Classification of Prediction 7.2 Qualitative Analysis Prediction Method 7.2.1 Expert Survey Method 7 2.2 Delphi method 7.2.3 Economic life cycle method 7.3 Time series prediction method 7.3.1 Moving average method 7.3.2 Exponential smoothing method 7.4 Regression analysis prediction method 7.4.1 Univariate Linear Regression Analysis 7.4.2 Multiple Linear Regression Analysis and Nonlinear Regression Analysis 7.4.3 Problems to be Noted in Application Exercises Chapter 8 Decision Theory 8.1 Basic Concepts of Decision 8.1.1 Decision Making Concept 8.1.2 Classification of Decision 8.1.3 Basic Elements of Decision Model 8.1.4 Characteristics of Decision Analysis 8.2 Risk-based Decision 8.2.1 Optimal Expected Gain and Loss Decision 8.2. 2 Decision tree method 8.2.3 Complete intelligence and its value 8.2.4 Bayesian decision 8.2.5 Utility theory 8.3 Uncertain decision-making 8.3.1 Equal possibility criterion 8.3.3. 2 The optimistic criterion 8.3.3 The pessimistic criterion 8.3.4 The compromise criterion 8.3.5 The regret criterion 8. The analytic hierarchy process 8.4.1 The basic idea of the analytic hierarchy process 8.4.2 The analytic hierarchy process The basic principles of 8.5 Markov analysis 8.5.1 Basic concepts of Markov processes 8.5.2 Markov decision analysis 8.6 Fuzzy comprehensive evaluation 8.6.1 Fuzzy 8.6.3 Application Examples Basic concepts of co-8.6.2 Fuzzy Comprehensive Evaluation Exercises References