預(yù)測(cè)決策理論與方法教學(xué)大綱（秋）英文版

1、Syllabus ofForecasting and Decision-making Theory andMethodsCourse Code：Total Credit Hours： 48Experiment Hours： 0Practice Hours： 0Course Name： Forecasting and decision-making theory and methodsCredits： 3Lecture Hours： 48Programming Hours： 0Total Number of Experimental (Programming) Projects 0Where,

2、Compulsory ( 0 ), Optional ( 0 ).School ： School of BusinessTarget Major： Industrial EngineeringI、Course Nature & AimsCourse Nature: Forecasting and decision-making theory and methods is a main course of management science and an important elective course for related majors. It is an important basic

3、 course that allows students to systematically master the concepts, knowledge, theories and basic techniques of predictive decision-making. Use the forecasting and decision-making methods and technical analysis to solve practical problems and to cultivate students1 thinking and ability in scientific

4、 prediction and decision-making.Course purpose: Through the study of this course, students can understand the concept, function and meaning of forecasting, systematically master commonly used forecasting methods and technologies, and be able to apply the forecasting methods learned in conjunction wi

5、th actual problems to perform forecasting analysis to provide support for scientific decision making Students understand the concepts, procedures and basic steps of decision-making, systematically master common decision-making methods, and can apply the learned decision-making methods to make decisi

6、on analysis. In view of the characteristics of the forecasting and decision-making course, pay attention to the cultivation of students scientific decision-making thinking in the teaching process, emphasizing the combination of theoretical learning and applied practice, focusing on the combination o

7、f qualitative analysis and quantitative analysis, for students* subsequent learning, practice and future work Lay a good foundation for development.II Course Objectives1. Moral Education and Character Cultivation.Learn the theory, methods and techniques of forecasting and decision-making through the

8、 course to have a comprehensive understanding of the knowledge related to forecasting and decision-making. By explaining the11、 12)extrapolation forecasting methods, Markov forecasting method applications, gray Sequence operator, grey prediction model, grey prediction technologyObjective 2(indices 1

9、、2、3、4、5、6、7、8、9、1()、11、12)Breakeven analysis, multi-scheme decision analysis, risk-based decision analysis methods, analytic hierarchy process, data envelopment analysis method, uncertain decision criteria, basic concepts of gray decision, gray target decision model, gray cluster decision model5101

10、02550TotaUI、Course ResourcesTextbooks：Liu Sifeng, Jian Lirong, Mi Chuanmin. Management Forecast and Decision Method (Third Edition) M.Beijing: Science Press, 2018.Bibliography：. Liu Sifeng. Forecasting Methods and Technology M. Beijing: Higher Education Press, 2009.Xu Guoxiang. Statisti

11、cal Forecasting and Decision Making (Fourth Edition) M. Shanghai: ShanghaiUniversity of Finance and Economics Press, 2012.IX、NotesPrerequisites:Follow-up Courses: NoContents and Requirements of Students Self-study: NoBilingual Teaching or Not: NoRequirements and Proportion of Bilingual Teaching: NoD

12、iscipline and Considerations of Practice Session: no practice sessionNotes: NoAuthor：Approved by：development history of forecasting and decision-making theory and the process of establishing relevant theories and technologies, understand how predecessors think in the development process of forecasti

13、ng and decision-making, how to overcome the obstacles encountered, and help students establish scientific thinking methods and courage to face challenges. From the perspective of applying forecasting and decision-making theory to promote innovation-driven development in China, taking the research wo

14、rk of outstanding contributors as the carrier, integrating the socialist core values education into the curriculum teaching content and all aspects of the entire teaching process, highlighting value guidance, knowledge transfer and ability training. To help students correctly understand the laws of

15、history, accurately grasp the basic national conditions, grasp the scientific world outlook and methodology, and promote the establishment of a correct world outlook and values.Course ObjectivesThrough the study of this course, students1 qualities, skills, knowledge and abilities obtained are as fol

16、lows：Objective 1. Master the relevant concepts of forecasting concepts, basic principles, common methods, and evaluation of forecasting effects, and be able to apply forecasting methods learned to forecast and analyze actual problems. (Corresponding to Chapter 1-7, supporting for graduation requirem

17、ents index 1 2、3、4、5、9、10 11、 12)Objective 2. Systematically master the concept of decision-making, basic theory and typical methods, and can apply the learned decision-making methods to analyze and make decisions on actual problems.(Corresponding to Chapter 8-13, supporting for graduation requireme

18、nts index 1 2、3、4 5、6、7、8、9、 10、Ik 12). Supporting for Graduation RequirementsThe graduation requirements supported by course objectives are mainly reflected in the graduation requirements indices 1 -12 , as follows:Supporting for Graduation RequirementsIH Basic Course ContentCourseObjectivesGraduat

19、ionRequirementsIndices and Contents Supporting for Graduation RequirementsTeachingTopicsLevel ofSupportIndicesContentsObjective1Master solid mathematical basic theory and good computer skillsIndex 2,3,52)Ability to apply mathematics, science and engineering knowledge3) Ability to understand and use

20、the latest industrial engineering techniques and tools5) Ability to analyze and interpret dataChapter2、 3、 4、5、 6、 7、9、10、11、 12、13MObjective2Master basic knowledge and skills in economic management, humanities andIndex7,8,9,10,11,12Professional and ethical responsibilitiesAbility to communicate eff

21、ectivelyLifelong learning cognition and abilityUnderstanding of contemporary issuesAbility to understand the impact ofChapter1、 2、 3、4、 5、 6、7、 8、 9、1()、11、Msocial sciencesengineering solutions on the global, economic, environmental and social aspects with broad knowledge12) A high humanistic qualit

22、y12、13Objective3Master comprehensive professional ability to analyze, plan, design, manage and operate complex socio-economic activity systemsIndex2,3,4,5,6,112)Ability to apply mathematics, science and engineering knowledgeAbility to understand and use the latest industrial engineering techniques a

23、nd toolsSystem planning and design capabilitiesAbility to analyze and interpret dataTeamwork and leadership skills11) Ability to understand the impact of engineering solutions on the global, economic, environmental and social aspects with broad knowledgeChapter2、 3、 4、5、 6、 7、9、10、11、 12、13HOverview

24、 of Forecasting (supporting course objectives * 1 *)IntroductionThe role of predictionBasic principles of forecastingClassification of predictionsForecasting proceduresPrediction accuracy and valueTeaching Requirements: Through the study in Chapter 1, students are required to clarify the concept of

25、forecasting; understand the role and significance of forecasting; master the basic principles of forecasting and the classification of forecasting; be familiar with forecasting procedures and applications; and correctly understand the value of forecasting.Key Points： Prediction concept and function;

26、 prediction classification, prediction program and application; prediction accuracy and value.Difficult Points：Qualitative Forecasting Method (supporting course objectives * 1*)IntroductionMarket survey forecast methodExpert predictionSubjective probability methodOmen prediction methodTeaching Requi

27、rements: Through the study in Chapter 2, students are required to master the market survey forecasting method, expert forecasting method, and subjective probability forecasting method; they can correctly use the learned methods to make predictions.Key Points： market research and forecasting method;

28、brainstorming method; Delphi method; subjective probability forecasting methodDifficult Points：Time Series Smooth Prediction Method (supporting course objectives * 1 *)Overview of time seriesMoving average methodExponential smoothingDifference exponential smoothingAdaptive filtering methodTeaching R

29、equirements: Through the study in Chapter 3, students are required to understand the concept and combination of time series, master the time series smooth prediction method, moving average prediction method, differential exponential smooth prediction method, and adaptive filtering method; be able to

30、 use various skills proficiently The time series smooth prediction method predicts actual problems.Key Points： the concept of time series and its combination; moving average prediction method; exponential smoothing prediction method; adaptive filtering methodDifficult Points：Regression Analysis and

31、Forecasting Method (supporting course objectives * 1 *)IntroductionUnary linear regression prediction methodMultiple linear regression prediction methodDummy variable regression predictionNonlinear regression prediction methodTeaching Requirements:Through the study in Chapter 4, students are require

32、d to understand the concepts and assumptions of univariate linear regression models and multiple linear regression models, master the estimation and testing methods of linear regression model parameters, and be able to use linear regression models to solve practical problems ; Understand the regress

33、ion model with dummy variables, and be able to select explanatory variables; Understand the different forms and classifications of nonlinear regression models.Key Points： linear regression prediction methods, regression models with dummy variables, nonlinear regression prediction modelsDifficult Poi

34、nts：Trend Extrapolation Forecasting Method (supporting course objectives * 1*)Exponential curve methodModified exponential curve methodGrowth curve methodEnvelope curve methodTeaching Requirements: Through the study in Chapter 5, students are required to master the exponential curve method and the g

35、rowth curve method; understand the envelope curve prediction method.Key Points： exponential curve method and modified exponential curve method; growth curve method; envelope curve methodDifficult Points：Markov Forecasting Method (supporting course objectives * 1 *)Introduction to Markov ChainForecas

36、t of commodity sales statusMarket Share ForecastExpected profit forecastTeaching Requirements: Through the study in Chapter 6, students are required to master the concept of Markov chain and the estimation method of state transition probability. They can use the Markov chain and its state transition

37、 probability to predict the sales status, market share and expected profit of the commodity .Key Points： Markov chain; commodity sales status prediction; market share forecast; expected profit forecastDifficult Points：Grey System Forecasting (supporting course objectives * 1 *)IntroductionSequence o

38、perator and gray information miningGrey system prediction modelGrey system prediction technologyTeaching Requirements: Through the study in Chapter 7, students are required to understand the nature of the buffer operator km and buffer operator, to master the structure and function of the weakened bu

39、ffer operator, the enhanced buffer operator, the accumulation operator, and the accumulation operator; (1,1) The basic form of the model and its scope of use; master the interval prediction and gray catastrophe prediction methods, and understand the waveform prediction methods.Key Points： sequence o

40、perators and gray information mining; gray prediction model; gray prediction technologyDifficult Points：Overview of Decision-making (supporting course objectives * 2*)Connotation and basic elements of decision analysisClassification and basic principles of decision analysisBasic steps of decision an

41、alysisOverview of decision analysis methodsTeaching Requirements: Through the study in Chapter 8, students are required to be familiar with the concept, development status and basic elements of decision analysis; master the classification, basic principles and basic steps of decision analysis.Key Po

42、ints： decision analysis concepts and basic elements; decision analysis classification, procedures and basic principles; decision analysis stepsDifficult Points：Definite Decision Analysis (supporting course objectives * 2*)Overview of Definitive Decision AnalysisProfit and loss decision analysisMulti

43、-scheme investment decisionTeaching Requirements:Through the study in Chapter 9, students are required to be familiar with the process and steps of definite decision-making; master the basic theoretical methods of profit and loss decision-making analysis; master the static and dynamic evaluation met

44、hods of independent investment program decisions; The main evaluation method.Key Points： deterministic decision analysis method, profit and loss decision analysis method, multi-project investment decision methodDifficult Points：Risk-Based Decision Analysis (supporting course objectives * 2*)Expectat

45、ion criteria for risk-based decision-making and its applicationDecision tree analysis methodBayesian Decision MethodUtility decision methodTeaching Requirements: Through the study in Chapter 10, students are required to be familiar with the connotation and basic ideas of risk-based decision-making;

46、master the expected value criterion decision-making method; familiar with the basic principles and procedures of decision tree analysis method; master the basic theoretical method of Bayesian decision-making; familiar The basic method of the utility criterion.Key Points： Expectation criteria for ris

47、k-based decision-making; risk-based decision-making methodsDifficult Points：Uncertain Decision-making (supporting course objectives * 2*)Basic concepts of uncertain decision-makingOptimistic decision criteriaCriteria for pessimistic decision-makingThe compromise decision criterionEqual probability d

48、ecision criteriaRegret decision criteriaTeaching Requirements:Through the study in Chapter 11, students are required to be familiar with the basic concepts of uncertain decision-making; master the commonly used uncertain decision-making criteria and understand the scope and problems of uncertain dec

49、ision-making criteria.Key Points： Uncertain decision concept; Uncertain decision ruleDifficult Points：Multi-Objective Decision Analysis (supporting course objectives * 2*)Overview of multi-objective decision analysisAHPData Envelopment Analysis MethodTeaching Requirements: Through the study in Chapt

50、er 12, students are required to understand the classification and characteristics of multi-objective decision-making problems, and be able to construct a target criterion system for multi-objective decision-making problems; understand and master the basic principles, methods and applications of AHP;

51、 understand and Master the basic principles, methods and applications of data envelopment analysis methods.Key Points： target criterion system for multi-objective decision-making; AHP method; data envelopment analysis methodDifficult Points：Grey Decision Model (supporting course objectives * 2*)Basi

52、c Concepts of Grey DecisionGrey target decisionGray clustering decision model based on mixed likelihood functionMulti-target weighted gray target decision modelTwo-stage gray decision-making modelTeaching Requirements: Through the study in Chapter 13, students are required to understand the gray tar

53、get decision-making model; be familiar with the gray clustering decision model based on the central and short-point mixed likelihood function; master the multi-target weighted intelligent gray target decision model; understand the two stages Grey decision model.Key Points： gray target decision model

54、; gray cluster decision model based on mixed likelihood function; gray cluster decision model based on mixed likelihood function; two-stage gray decision modelDifficult Points：IV、Table of Credit Hour DistributionTeaching ContentIdeological andPoliticalIntegratedLectureHoursExperi ment HoursPracticeH

55、oursProgra mming HoursSelf-studyHoursExerciseClassDiscussionHoursChapter 1 overview offorecastingScientificworldview4Chapter 2 Qualitativeprediction methodScientificmethodology2Chapter 3 Time series smooth predictionScientificmethodology4Chapter 4Regression analysis prediction methodScientificmethodology2Chapter 5Trend extrapolation prediction methodScientificmethodology4Chapter 6Markov predictionmethodScientificmethodology2Ch叩ter 7Grey system forecastScientificmethodology4Chapter 8Overview of decision-makingScientific way ofth