Lawrence V. Snyder, PhD, is Professor in the Department of Industrial and Systems Engineering and Co-Director of the Institute for Data, Intelligent Systems, and Computation at Lehigh University. He has written numerous journal articles and tutorials on optimization models for supply chains and other infrastructure systems, with a focus on decision-making under uncertainty. Zuo-Jun Max Shen, PhD, is Professor in the Department of Industrial Engineering and Operations Research and the Department of Civil and Environmental Engineering at the University of California at Berkeley. He is an INFORMS Fellow and has published and consulted extensively in the areas of integrated supply chain design and management, data driven decision making, and systems optimization.

• List of Figures xxiList of Tables xxviiList of Algorithms xxixPreface xxxi1 Introduction 11.1 The Evolution of Supply Chain Theory 11.2 Definitions and Scope 21.3 Levels of Decision Making in Supply Chain Management 42 Forecasting and Demand Modeling 52.1 Introduction 52.2 Classical Demand Forecasting Methods 62.3 Forecast Accuracy 152.4 Machine Learning in Demand Forecasting 172.5 Demand Modeling Techniques 232.6 Bass Diffusion Model 242.7 Leading Indicator Approach 302.8 Discrete Choice Models 33Case Study: Semiconductor Demand Forecasting at Intel 38Problems 393 Deterministic Inventory Models 453.1 Introduction to Inventory Modeling 453.2 Continuous Review: The Economic Order Quantity Problem 513.3 Power of Two Policies 573.4 The EOQ with Quantity Discounts 603.5 The EOQ with Planned Backorders 673.6 The Economic Production Quantity Model 703.7 Periodic Review: The Wagner–Whitin Model 72Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76Problems 774 Stochastic Inventory Models: Periodic Review 874.1 Inventory Policies 874.2 Demand Processes 894.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 894.4 Periodic Review with Nonzero Fixed Costs: (s; S) Policies 1144.5 Policy Optimality 1234.6 Lost Sales 136Case Study: Optimization of Warranty Inventory at Hitachi 138Problems 1405 Stochastic Inventory Models: Continuous Review 1555.1 (r; Q) Policies 1555.2 Exact (r; Q) Problem with Continuous Demand Distribution 1565.3 Approximations for (r; Q) Problem with Continuous Distribution 1615.4 Exact (r; Q) Problem with Continuous Distribution: Properties of Optimal r and Q 1705.5 Exact (r; Q) Problem with Discrete Distribution 177Case Study: (r; Q) Inventory Optimization at Dell 180Problems 1826 Multiechelon Inventory Models 1876.1 Introduction 1876.2 Stochastic-Service Models 1916.3 Guaranteed-Service Models 2036.4 Closing Thoughts 217Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222Problems 2237 Pooling and Flexibility 2297.1 Introduction 2297.2 The Risk-Pooling Effect 2307.3 Postponement 2367.4 Transshipments 2377.5 Process Flexibility 2437.6 A Process Flexibility Optimization Model 253Case Study: Risk Pooling and Inventory Management at Yedioth Group 257Problems 2598 Facility Location Models 2678.1 Introduction 2678.2 The Uncapacitated Fixed-Charge Location Problem 2698.3 Other Minisum Models 2958.4 Covering Models 3058.5 Other Facility Location Problems 3148.6 Stochastic and Robust Location Models 3178.7 Supply Chain Network Design 321Case Study: Locating Fire Stations in Istanbul 332Problems 3359 Supply Uncertainty 3559.1 Introduction to Supply Uncertainty 3559.2 Inventory Models with Disruptions 3569.3 Inventory Models with Yield Uncertainty 3659.4 A Multisupplier Model 3729.5 The Risk-Diversification Effect 3849.6 A Facility Location Model with Disruptions 387Case Study: Disruption Management at Ford 395Problems 39610 The Traveling Salesman Problem 40310.1 Supply Chain Transportation 40310.2 Introduction to the TSP 40410.3 Exact Algorithms for the TSP 40810.4 Construction Heuristics for the TSP 41610.5 Improvement Heuristics for the TSP 43610.6 Bounds and Approximations for the TSP 44210.7 World Records 452Case Study: Routing Meals on Wheels Deliveries 453Problems 45511 The Vehicle Routing Problem 46311.1 Introduction to the VRP 46311.2 Exact Algorithms for the VRP 46811.3 Heuristics for the VRP 47511.4 Bounds and Approximations for the VRP 49511.5 Extensions of the VRP 498Case Study: ORION: Optimizing Delivery Routes at UPS 501Problems 50212 Integrated Supply Chain Models 51112.1 Introduction 51112.2 A Location–Inventory Model 51212.3 A Location–Routing Model 52912.4 An Inventory–Routing Model 531Case Study: Inventory–Routing at Frito-Lay 534Problems 53513 The Bullwhip Effect 53913.1 Introduction 53913.2 Proving the Existence of the Bullwhip Effect 54113.3 Reducing the Bullwhip Effect 55213.4 Centralizing Demand Information 555Case Study: Reducing the Bullwhip Effect at Philips Electronics 556Problems 55914 Supply Chain Contracts 56314.1 Introduction 56314.2 Introduction to Game Theory 56414.3 Notation 56514.4 Preliminary Analysis 56614.5 The Wholesale Price Contract 56814.6 The Buyback Contract 57414.7 The Revenue Sharing Contract 57814.8 The Quantity Flexibility Contract 581Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584Problems 58615 Auctions 59115.1 Introduction 59115.2 The English Auction 59315.3 Combinatorial Auctions 59515.4 The Vickrey–Clarke–Groves Auction 599Case Study: Procurement Auctions for Mars 608Problems 61016 Applications of Supply Chain Theory 61516.1 Introduction 61516.2 Electricity Systems 61516.3 Health Care 62516.4 Public Sector Operations 632Case Study: Optimization of the Natural Gas Supply Chain in China 639Problems 641Appendix A: Multiple-Chapter Problems 643Problems 643Appendix B: How to Write Proofs: A Short Guide 651B.1 How to Prove Anything 651B.2 Types of Things You May Be Asked to Prove 653B.3 Proof Techniques 655B.4 Other Advice 657Appendix C: Helpful Formulas 661C.1 Positive and Negative Parts 661C.2 Standard Normal Random Variables 662C.3 Loss Functions 662C.4 Differentiation of Integrals 665C.5 Geometric Series 666C.6 Normal Distributions in Excel and MATLAB 666C.7 Partial Expectations 667Appendix D: Integer Optimization Techniques 669D.1 Lagrangian Relaxation 669D.2 Column Generation 675References 681Subject Index 712Author Index 725

Lawrence V. Snyder, PhD, is Professor in the Department of Industrial and Systems Engineering and Co-Director of the Institute for Data, Intelligent Systems, and Computation at Lehigh University. He has written numerous journal articles and tutorials on optimization models for supply chains and other infrastructure systems, with a focus on decision-making under uncertainty. Zuo-Jun Max Shen, PhD, is Professor in the Department of Industrial Engineering and Operations Research and the Department of Civil and Environmental Engineering at the University of California at Berkeley. He is an INFORMS Fellow and has published and consulted extensively in the areas of integrated supply chain design and management, data driven decision making, and systems optimization.

• List of Figures xxiList of Tables xxviiList of Algorithms xxixPreface xxxi1 Introduction 11.1 The Evolution of Supply Chain Theory 11.2 Definitions and Scope 21.3 Levels of Decision Making in Supply Chain Management 42 Forecasting and Demand Modeling 52.1 Introduction 52.2 Classical Demand Forecasting Methods 62.3 Forecast Accuracy 152.4 Machine Learning in Demand Forecasting 172.5 Demand Modeling Techniques 232.6 Bass Diffusion Model 242.7 Leading Indicator Approach 302.8 Discrete Choice Models 33Case Study: Semiconductor Demand Forecasting at Intel 38Problems 393 Deterministic Inventory Models 453.1 Introduction to Inventory Modeling 453.2 Continuous Review: The Economic Order Quantity Problem 513.3 Power of Two Policies 573.4 The EOQ with Quantity Discounts 603.5 The EOQ with Planned Backorders 673.6 The Economic Production Quantity Model 703.7 Periodic Review: The Wagner–Whitin Model 72Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76Problems 774 Stochastic Inventory Models: Periodic Review 874.1 Inventory Policies 874.2 Demand Processes 894.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 894.4 Periodic Review with Nonzero Fixed Costs: (s; S) Policies 1144.5 Policy Optimality 1234.6 Lost Sales 136Case Study: Optimization of Warranty Inventory at Hitachi 138Problems 1405 Stochastic Inventory Models: Continuous Review 1555.1 (r; Q) Policies 1555.2 Exact (r; Q) Problem with Continuous Demand Distribution 1565.3 Approximations for (r; Q) Problem with Continuous Distribution 1615.4 Exact (r; Q) Problem with Continuous Distribution: Properties of Optimal r and Q 1705.5 Exact (r; Q) Problem with Discrete Distribution 177Case Study: (r; Q) Inventory Optimization at Dell 180Problems 1826 Multiechelon Inventory Models 1876.1 Introduction 1876.2 Stochastic-Service Models 1916.3 Guaranteed-Service Models 2036.4 Closing Thoughts 217Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222Problems 2237 Pooling and Flexibility 2297.1 Introduction 2297.2 The Risk-Pooling Effect 2307.3 Postponement 2367.4 Transshipments 2377.5 Process Flexibility 2437.6 A Process Flexibility Optimization Model 253Case Study: Risk Pooling and Inventory Management at Yedioth Group 257Problems 2598 Facility Location Models 2678.1 Introduction 2678.2 The Uncapacitated Fixed-Charge Location Problem 2698.3 Other Minisum Models 2958.4 Covering Models 3058.5 Other Facility Location Problems 3148.6 Stochastic and Robust Location Models 3178.7 Supply Chain Network Design 321Case Study: Locating Fire Stations in Istanbul 332Problems 3359 Supply Uncertainty 3559.1 Introduction to Supply Uncertainty 3559.2 Inventory Models with Disruptions 3569.3 Inventory Models with Yield Uncertainty 3659.4 A Multisupplier Model 3729.5 The Risk-Diversification Effect 3849.6 A Facility Location Model with Disruptions 387Case Study: Disruption Management at Ford 395Problems 39610 The Traveling Salesman Problem 40310.1 Supply Chain Transportation 40310.2 Introduction to the TSP 40410.3 Exact Algorithms for the TSP 40810.4 Construction Heuristics for the TSP 41610.5 Improvement Heuristics for the TSP 43610.6 Bounds and Approximations for the TSP 44210.7 World Records 452Case Study: Routing Meals on Wheels Deliveries 453Problems 45511 The Vehicle Routing Problem 46311.1 Introduction to the VRP 46311.2 Exact Algorithms for the VRP 46811.3 Heuristics for the VRP 47511.4 Bounds and Approximations for the VRP 49511.5 Extensions of the VRP 498Case Study: ORION: Optimizing Delivery Routes at UPS 501Problems 50212 Integrated Supply Chain Models 51112.1 Introduction 51112.2 A Location–Inventory Model 51212.3 A Location–Routing Model 52912.4 An Inventory–Routing Model 531Case Study: Inventory–Routing at Frito-Lay 534Problems 53513 The Bullwhip Effect 53913.1 Introduction 53913.2 Proving the Existence of the Bullwhip Effect 54113.3 Reducing the Bullwhip Effect 55213.4 Centralizing Demand Information 555Case Study: Reducing the Bullwhip Effect at Philips Electronics 556Problems 55914 Supply Chain Contracts 56314.1 Introduction 56314.2 Introduction to Game Theory 56414.3 Notation 56514.4 Preliminary Analysis 56614.5 The Wholesale Price Contract 56814.6 The Buyback Contract 57414.7 The Revenue Sharing Contract 57814.8 The Quantity Flexibility Contract 581Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584Problems 58615 Auctions 59115.1 Introduction 59115.2 The English Auction 59315.3 Combinatorial Auctions 59515.4 The Vickrey–Clarke–Groves Auction 599Case Study: Procurement Auctions for Mars 608Problems 61016 Applications of Supply Chain Theory 61516.1 Introduction 61516.2 Electricity Systems 61516.3 Health Care 62516.4 Public Sector Operations 632Case Study: Optimization of the Natural Gas Supply Chain in China 639Problems 641Appendix A: Multiple-Chapter Problems 643Problems 643Appendix B: How to Write Proofs: A Short Guide 651B.1 How to Prove Anything 651B.2 Types of Things You May Be Asked to Prove 653B.3 Proof Techniques 655B.4 Other Advice 657Appendix C: Helpful Formulas 661C.1 Positive and Negative Parts 661C.2 Standard Normal Random Variables 662C.3 Loss Functions 662C.4 Differentiation of Integrals 665C.5 Geometric Series 666C.6 Normal Distributions in Excel and MATLAB 666C.7 Partial Expectations 667Appendix D: Integer Optimization Techniques 669D.1 Lagrangian Relaxation 669D.2 Column Generation 675References 681Subject Index 712Author Index 725

Lawrence V. Snyder, PhD, is Professor in the Department of Industrial and Systems Engineering and Co-Director of the Institute for Data, Intelligent Systems, and Computation at Lehigh University. He has written numerous journal articles and tutorials on optimization models for supply chains and other infrastructure systems, with a focus on decision-making under uncertainty. Zuo-Jun Max Shen, PhD, is Professor in the Department of Industrial Engineering and Operations Research and the Department of Civil and Environmental Engineering at the University of California at Berkeley. He is an INFORMS Fellow and has published and consulted extensively in the areas of integrated supply chain design and management, data driven decision making, and systems optimization.

• List of Figures xxiList of Tables xxviiList of Algorithms xxixPreface xxxi1 Introduction 11.1 The Evolution of Supply Chain Theory 11.2 Definitions and Scope 21.3 Levels of Decision Making in Supply Chain Management 42 Forecasting and Demand Modeling 52.1 Introduction 52.2 Classical Demand Forecasting Methods 62.3 Forecast Accuracy 152.4 Machine Learning in Demand Forecasting 172.5 Demand Modeling Techniques 232.6 Bass Diffusion Model 242.7 Leading Indicator Approach 302.8 Discrete Choice Models 33Case Study: Semiconductor Demand Forecasting at Intel 38Problems 393 Deterministic Inventory Models 453.1 Introduction to Inventory Modeling 453.2 Continuous Review: The Economic Order Quantity Problem 513.3 Power of Two Policies 573.4 The EOQ with Quantity Discounts 603.5 The EOQ with Planned Backorders 673.6 The Economic Production Quantity Model 703.7 Periodic Review: The Wagner–Whitin Model 72Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76Problems 774 Stochastic Inventory Models: Periodic Review 874.1 Inventory Policies 874.2 Demand Processes 894.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 894.4 Periodic Review with Nonzero Fixed Costs: (s; S) Policies 1144.5 Policy Optimality 1234.6 Lost Sales 136Case Study: Optimization of Warranty Inventory at Hitachi 138Problems 1405 Stochastic Inventory Models: Continuous Review 1555.1 (r; Q) Policies 1555.2 Exact (r; Q) Problem with Continuous Demand Distribution 1565.3 Approximations for (r; Q) Problem with Continuous Distribution 1615.4 Exact (r; Q) Problem with Continuous Distribution: Properties of Optimal r and Q 1705.5 Exact (r; Q) Problem with Discrete Distribution 177Case Study: (r; Q) Inventory Optimization at Dell 180Problems 1826 Multiechelon Inventory Models 1876.1 Introduction 1876.2 Stochastic-Service Models 1916.3 Guaranteed-Service Models 2036.4 Closing Thoughts 217Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222Problems 2237 Pooling and Flexibility 2297.1 Introduction 2297.2 The Risk-Pooling Effect 2307.3 Postponement 2367.4 Transshipments 2377.5 Process Flexibility 2437.6 A Process Flexibility Optimization Model 253Case Study: Risk Pooling and Inventory Management at Yedioth Group 257Problems 2598 Facility Location Models 2678.1 Introduction 2678.2 The Uncapacitated Fixed-Charge Location Problem 2698.3 Other Minisum Models 2958.4 Covering Models 3058.5 Other Facility Location Problems 3148.6 Stochastic and Robust Location Models 3178.7 Supply Chain Network Design 321Case Study: Locating Fire Stations in Istanbul 332Problems 3359 Supply Uncertainty 3559.1 Introduction to Supply Uncertainty 3559.2 Inventory Models with Disruptions 3569.3 Inventory Models with Yield Uncertainty 3659.4 A Multisupplier Model 3729.5 The Risk-Diversification Effect 3849.6 A Facility Location Model with Disruptions 387Case Study: Disruption Management at Ford 395Problems 39610 The Traveling Salesman Problem 40310.1 Supply Chain Transportation 40310.2 Introduction to the TSP 40410.3 Exact Algorithms for the TSP 40810.4 Construction Heuristics for the TSP 41610.5 Improvement Heuristics for the TSP 43610.6 Bounds and Approximations for the TSP 44210.7 World Records 452Case Study: Routing Meals on Wheels Deliveries 453Problems 45511 The Vehicle Routing Problem 46311.1 Introduction to the VRP 46311.2 Exact Algorithms for the VRP 46811.3 Heuristics for the VRP 47511.4 Bounds and Approximations for the VRP 49511.5 Extensions of the VRP 498Case Study: ORION: Optimizing Delivery Routes at UPS 501Problems 50212 Integrated Supply Chain Models 51112.1 Introduction 51112.2 A Location–Inventory Model 51212.3 A Location–Routing Model 52912.4 An Inventory–Routing Model 531Case Study: Inventory–Routing at Frito-Lay 534Problems 53513 The Bullwhip Effect 53913.1 Introduction 53913.2 Proving the Existence of the Bullwhip Effect 54113.3 Reducing the Bullwhip Effect 55213.4 Centralizing Demand Information 555Case Study: Reducing the Bullwhip Effect at Philips Electronics 556Problems 55914 Supply Chain Contracts 56314.1 Introduction 56314.2 Introduction to Game Theory 56414.3 Notation 56514.4 Preliminary Analysis 56614.5 The Wholesale Price Contract 56814.6 The Buyback Contract 57414.7 The Revenue Sharing Contract 57814.8 The Quantity Flexibility Contract 581Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584Problems 58615 Auctions 59115.1 Introduction 59115.2 The English Auction 59315.3 Combinatorial Auctions 59515.4 The Vickrey–Clarke–Groves Auction 599Case Study: Procurement Auctions for Mars 608Problems 61016 Applications of Supply Chain Theory 61516.1 Introduction 61516.2 Electricity Systems 61516.3 Health Care 62516.4 Public Sector Operations 632Case Study: Optimization of the Natural Gas Supply Chain in China 639Problems 641Appendix A: Multiple-Chapter Problems 643Problems 643Appendix B: How to Write Proofs: A Short Guide 651B.1 How to Prove Anything 651B.2 Types of Things You May Be Asked to Prove 653B.3 Proof Techniques 655B.4 Other Advice 657Appendix C: Helpful Formulas 661C.1 Positive and Negative Parts 661C.2 Standard Normal Random Variables 662C.3 Loss Functions 662C.4 Differentiation of Integrals 665C.5 Geometric Series 666C.6 Normal Distributions in Excel and MATLAB 666C.7 Partial Expectations 667Appendix D: Integer Optimization Techniques 669D.1 Lagrangian Relaxation 669D.2 Column Generation 675References 681Subject Index 712Author Index 725