Systems Engineering with Economics, Probability and Statistics

Dr C. Jotin Khisty is a Professor Emeritus of Civil and Architectural Engineering at the Illinois Institute of Technology (IIT), Chicago, Illinois. He was a professor of Civil Engineering and the Director of the Transportation and Infrastructure program at IIT from 1990 to 2002. Prior to joining IIT, he was on the faculty at Washington State University, Pullman, WA, from 1978 to 1990, where he also served as the Deputy Director of the Washington State Transportation Research Center. He obtained his PhD in Transportation Systems Engineering from The Ohio State University, Columbus. He has had considerable field experience, first in India and Germany on large civil engineering projects, and later as a transportation engineer and planner with Metropolitan Planning Organizations in the USA. He has published more than 100 papers in journals, conference proceedings, and book chapters on systems science, transportation and traffic engineering, infrastructure systems planning, sustainable systems and economic analysis. He is the author of two books on transportation engineering.

• Chapter 1: MAPPING THE TERRAIN OF THE SYSTEMS APPROACH1.1 Introduction1.2 The Nature of Science1.3 Engineering Planning, Design, and Management1.4 The Systems Approach1.5 Steps in Systems Analysis1.6 Classification of Systems1.7 Systems Characteristics1.8 Systems Analysis and Decision Making1.9 Models and Model-BuildingSummaryReferencesExercisesChapter 2: PROBLEM SOLVING AND DESIGNING IN ENGINEERING AND PLANNING2.1 Introduction2.2 Problem Solving and Designing2.3 Hierarchy: Problem-Space, Trees, and Semi-Lattices2.4 Problem Solving Styles2.5 Wicked Problems2.6 Measurement and Scaling2.6.1 Sources of Data2.6.2 Measurement2.6.3 Scales of Measurement2.7 System Model Types and Model-Building2.7.1 Model Types2.7.2 Models Used in Planning and Engineering2.8 Problem-Solving Through Group or Committee ActionSummaryReferencesExercisesChapter 3: BASIC ENGINEERING ECONOMICS AND EVALUATION3.1 Introduction3.2 Notations3.3 Simple Interest3.4 Compound Interest3.5 Uniform Series of Payments3.5.1 Compound Amount Factor (CAF) 3.5.2 Sinking Fund Factor (SFF) 3.5.3 Present Worth Factor (PWF) 3.5.4 Capital Recovery Factor (CRF) 3.6 Uniform Gradient Series3.7 Discrete Compound Interest Factors3.8 Uniform Continuous Cash Flow and Capitalized Cost3.9 Evaluation3.10 Feasibility Issues3.11 Evaluation Issues3.12 The Evaluation Process3.13 Values, Goals, Objectives, Criteria, and Standards3.14 Estimation of Costs, Impacts, and Performance Levels3.14.1 Capital, Operating, and Maintenance Costs3.14.2 User Costs3.14.3 Impacts3.14.4 Performance Levels3.15 Evaluation of Alternatives3.16 Economic and Financial Concepts3.17 Analysis Techniques3.17.1 Economic Evaluation Methods (Efficiency Analysis) 3.17.2 Cost-Effectiveness Analysis3.17.3 Multicriteria Evaluation Method3.17.4 Benefit-Cost Analysis3.17.5 The Willingness-To-Pay Concept3.18 Depreciation and Taxes3.19 Reporting ResultsSummaryReferencesExercisesChapter 4: BASIC MICROECONOMICS FOR ENGINEERS AND PLANNERS4.1 The Scope of Economics and Microeconomics4.2 Some Basic Issues of Economics4.3 Demand for Goods and Services4.4 Demand, Supply, and Equilibrium4.5 Sensitivity of Demand4.6 Factors Affecting Elasticities4.6.1 Income Elasticities4.6.2 Price Elasticities4.6.3 Elasticity and Total Revenue4.6.4 Price Elasticity of Supply4.7 Kraft Demand Model4.8 Direct and Cross Elasticities4.9 Consumer Surplus4.10 Costs4.10.1 Laws Related To Costs4.10.2 Average Cost4.10.3 Marginal Cost4.11 Consumer ChoiceSummaryReferencesExercisesChapter 5: PRINCIPLES OF PROBABILITY: PART I-REVIEW OF PROBABILITY THEORY 5.1 Introduction5.2 Events5.2.1 Complementary Event5.2.2 Combination of Events5.2.3 Mutually Exclusive and Collectively Exhaustive Events5.3 Probability5.3.1 Probability of the Union of Events5.3.2 Conditional Probability and Probability of Intersection of Events5.3.3 Bayes' Theorem5.3.4 deMorgan's Rule5.3.5 Total Probability TheoremSummaryReferencesExercisesChapter 6: PRINCIPLES OF PROBABILITY: PART II-RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS 6.1 Random Variables6.1.1 Discrete Random Variables6.1.2 Continuous Random Variables6.2 Probability Functions6.2.1 Probability Mass Function and Cumulative Distribution Function6.2.2 Probability Density and Distribution Functions6.3 Describing Parameters of a Random Variable6.3.1 Mathematical Expectation6.3.2 Mean or Expected Value6.3.3 The Variance and Standard Deviation6.3.4 Median and Mode6.3.5 Moments of a Random Variable6.4 Several Useful Probability Functions6.4.1 Normal (Gaussian) Probability Density Function6.4.2. Rayleigh Probability Density Function6.4.3 Logarithmic Normal Probability Density Function6.4.4 The T-Distribution6.4.5 Binomial Distribution Function6.4.6 Poisson Distribution Function6.4.7 Exponential Distribution FunctionSummaryReferencesExercisesChapter 7: PRINCIPLES OF PROBABILITY: PART III -JOINT PROBABILITY FUNCTIONS AND CORRELATED VARIABLES 7.1 Introduction7.2 Joint Probability Functions7.2.1 Joint Probability Mass Function7.2.2 Joint Probability Density Function7.3 Covariance and Correlation7.3.1 Covariance7.3.2 Correlation Coefficient7.3.3 Bivariate Normal Distribution7.4 Functions of A Single Random Variable7.4.1 Discrete Random Variable Case7.4.2 Continuous Random Variable Case7.5 Functions of Multiple Random Variables7.5.1 Special Case: Linear Functions of Independent Normal Random Variables7.5.2 Special Case: The Product (or Quotient) of Independent Log-Normal Random Variables7.6. Approximate Methods for Computing Mean, Variance and Probability7.6.1 Simulation Method7.6.2 Approximate First and Second Order Estimates for the Mean and VarianceSummaryReferencesExercisesChapter 8: PRINCIPLES OF STATISTICS: PART I-ESTIMATION OF STATISTICAL PARAMETERS AND TESTING VALIDITY OF DISTRIBUTION FUNCTIONS 8.1 Introduction8.2 Data Compression8.2.1 Processing Data into Occurrence Frequencies8.2.2 Processing Time-Dependent Data8.3 Estimation of Mean and Variance8.4 Confidence Intervals for Statistical Parameters 8.4.1 Confidence Intervals for the Mean8.4.2 One-Sided Confidence Limits8.4.3 Determination of the Sample Size in an Experiment8.4.4 Confidence Intervals for Other Statistical Parameters8.4.5 Determination of Sample Size for Estimating Proportions8.5 Estimation of Statistical Parameters Using the Method of Maximum Likelihood8.6 Testing Data for a Desired Distribution Model8.6.1 Branch-and-Leaves Method8.6.2 Determination of the Shape of Distribution from Frequency-of-Occurrence Diagram8.6.3 Probability-Scaled Plotting8.6.4 Chi-Square Goodness-of-Fit Test for Testing Validity of a Distribution Model8.6.5 Kolmogorov-Smirnov Test SummaryReferencesExercisesChapter 9: PRINCIPLES OF STATISTICS: PART II-HYPOTHESIS TESTING, ANALYSIS OF VARIANCE, REGRESSION, AND CORRELATION ANALYSIS 9.1 Introduction9.2 Hypotheses Testing9.2.1 Hypothesis and Significance Testing of a Population Mean9.2.2 Hypothesis Testing of a Proportion9.3 Comparing Two Population Means9.3.1 Testing of Equality of Two Variances9.3.2 Comparing Mean Values from Independent Populations with Identical Variances (Pooled T) 9.3.3 Comparing Means from Independent Populations with Unequal Variances9.3.4 Comparing Means from Dependent Populations (Paired T) 9.4 Analysis of Variance9.4.1 One-Way Classification, Completely Random Design with Fixed Effect9.4.2 Multiple Range Test9.4.3 Random Effect9.5 Distribution-Free Methods9.5.1 Test of Location for a Given Sample Data9.5.2 Wilcoxon Signed-Rank Test9.5.3 Wilcoxon Signed-Rank Test for Paired Data9.5.4 Wilcoxon Rank-Sum Test for Unmatched Data9.5.5 Tests for Several Populations9.6 Regression and Correlation Analysis9.6.1 Simple Linear Regression Analysis9.6.2 Correlation Coefficient9.6.3 Strength of Linear Correlation9.6.4 Multiple Linear Regression Analysis9.6.5 Nonlinear Regression9.6.6 Spearsman's Rank Correlation CoefficientSummaryReferencesExercisesChapter 10: BASIC HARD SYSTEMS ENGINEERING-PART I10.1 Introduction: Hard Systems Analysis10.2 Methods Based On Calculus10.2.1 Production Function Characteristics10.2.2 Relationship among Total, Marginal, and Average Cost Concepts and Elasticity10.2.3 The Method of Lagrange Multipliers10.3 Critical Path Method10.3.1 Key Concepts10.3.2 CPM Scheduling10.3.3 The Time-Grid Diagram and Bar Charts10.3.4 Resource Scheduling10.3.5 Time-Cost Optimization10.4 Program Evaluation and Review Technique and the Line-of-Balance Technique10.4.1 Key Concepts of PERT (See Figure 10.2) 10.4.2 The LOB Technique10.4.3 Progress Charts and Buffers10.4.4 Resource and LOB Schedule10.5 Network Flow Analysis10.5.1 Key Concepts10.5.2 Minimum Spanning Tree10.5.3 The Maximal Flow Problem10.5.4 Shortest-Path or Minimum-Path Technique10.6 Linear Programming10.6.1 The Graphical Method10.6.2 Simplex Algorithm10.6.3 Marginal Value or Shadow Pricing10.6.4 Primal and Dual Problem Formulation Characteristics and Interpretation10.6.5 Solving Minimization Problems with Simplex10.6.6 Interpretation of the Primal and Dual ModelsExercisesChapter 11: BASIC HARD SYSTEMS ENGINEERING-PART II11.1 Introduction11.2 Forecasting11.2.1 Regularity11.2.2 Use of Time Series11.3 Transportation and Assignment Problem 11.3.1 Introduction11.3.2 Northwest Corner Method11.3.3 The Minimum-Cost Cell Method11.3.4 The Penalty or "Vogel's" Method11.3.5 How Do We Determine An Optimum Solution? 11.3.6 The Unbalanced and Other Transportation Problems11.3.7 The Assignment Problem11.4 Decision Analysis11.4.1 Overview11.4.2 Decision Making Under Conditions of Uncertainty11.4.3 Decision Making Under Uncertainty with Probabilities11.5 Queuing Models 11.5.1 Introduction11.5.2 Characteristics of Queuing Systems11.5.3 Model 1 (D/D/1) Deterministic Queuing Model11.5.4 Model 2 (M/D/1) 11.5.5 Model 3 M/M/111.5.6 The Economics and Operating Characteristics of Queuing Discipline11.5.7 Model 4 M/M/N11.6 Simulation 11.6.1 Introduction11.6.2 Random Numbers11.6.3 Simulations Using Known Probabilities11.7 Markov Analysis11.7.1 Characteristics of Markov Analysis11.7.2 Special Transition MatricesExercisesChapter 12: SYSTEMS THINKING12.1 Introduction 12.2 Systems Thinking 12.2.1 The Nature of Systems12.2.2 System of Systems Thinking12.3 Hard Systems Thinking12.3.1 Preamble12.3.2 Systems Analysis12.3.3 Systems Engineering12.2.4 Operations Research12.4 Soft Systems Thinking12.4.1 Preamble12.4.2 The Path from Optimization to Learning12.4.3 Checkland's SSM12.4.4 Ackoff's Interactive Planning 12.4.5 Senge's Fifth Discipline12.4.6 Strategic Options Development and Analysis (SODA) 12.5 Critical Systems Thinking12.6 Multimodal Systems Thinking12.7 Reflections and SummaryReferencesExercisesChapter 13: SYSTEMS THINKING: CASE STUDIES13.1 Introducing the Case Studies13.2 Case Study 1: Transportation Project Selection Using Robustness Analysis13.2.1 Introduction13.2.2 Background: Robustness and Debility13.2.3 Road Construction using Robustness13.2.4 Algorithm for Robustness13.2.5 Discussion and Summary13.3 Checkland's Soft Systems Methodology (SSM) and Multimethodology Application to the Chicago Region13.3.1 Background: Mixing and Matching13.3.2 The Chicago Region Characteristics and Root Definitions13.3.3 Multimodal Performance Indicators13.3.4 Discussion and Summary13.4 Case Study 3: Using SODA for the Chicago Transit Authority Study13.4.1 Introduction13.4.2 Background: Brown Line Rehabilitation Project13.4.3 Soda Application13.4.4 Findings13.4.5 Summary: Goals and Expectations13.4.6 Discussion and Summary13.5 Case Study 4: Crisis Management Using SSM for Bhopal Gas Tragedy13.5.1 Introduction13.5.2 Crisis Management13.5.3 The Bhopal Gas Tragedy13.5.4 Application of SSM13.5.4.1 Formulation of Root Definitions13.5.4.2 Conceptual Models13.5.4.3 Comparison of Conceptual Models and Root Definitions13.5.5 Lessons Learned and Actions to be Taken13.5.6 The Crisis Management Program13.5.7 Discussion and Summary13.6 Concluding RemarksReferencesChapter 14: SUSTAINABLE DEVELOPMENT, SUSTAINABILITY, ENGINEERING AND PLANNING14.1 Introduction to Sustainable Development and Sustainability14.1.1 Important Sustainability Issues for the Engineering Community14.1.2 ASCE Code of Ethics and Sustainable Development14.1.3 Sustainability and Systems Thinking14.1.4 System Interconnections and Interdependencies14.1.5 Strong versus Weak Sustainability14.1.6 Shallow and Deep Ecology14.2 Models of Sustainable Development and Sustainability14.2.1 The Triple Bottom Line Framework14.2.2 The IPAT Model14.2.3 The Ecological Footprint Model14.2.4 Triaxial Representation of Technological Sustainability14.2.5 The Quality of Life/Natural Capital Model14.2.6 The Sustainability Footprint14.2.7 The True Sustainability Index14.3 Planning and Designing for Sustainable Development and Sustainability14.3.1 Planning and Project Development Methodologies for Sustainable Development and SustainabilityReferencesExercisesChapter 15: CASE STUDIES IN ENGINEERING AND PLANNING FOR SUSTAINABILITY15.1 Introduction15.2 The Interface Company's Approach to Sustainability15.2.1 Interface's Seven-Front Approach to Sustainability15.2.2 Measuring Progress toward Sustainability15.2.2.1 Verification and Certification15.2.3 Concluding Remarks: Interface, Inc.'s Journey to Sustainability15.2 The New Zealand Transportation Strategy15.2.1 Defining Sustainable Transportation15.2.2 Defining a Vision, Objectives and Targets15.2.3 A Multi-Sector Approach15.2.4 Implementation: Turning Strategy into Action15.2.5 Monitoring, Reporting and Review15.2.6 Concluding Remarks: New Zealand's Transportation Strategy15.3 Sustainability Evaluation of Transportation Plan Alternatives Using Multiple Attribute Decision Making (MADM) Methodology15.3.1 MADM Approach for Sustainability Evaluation of Plan Alternatives15.3.2 Applying MADM and the Sustainability Diamond: Identifying Superior Plans15.3.3. Concluding Remarks: Applying MADM and Visualization for Selecting Plan Alternatives15.4 ConclusionReferencesExercises INDEX