Fundamentals of Supply Chain Theory (Hardcover, 2)

· 제목 : Fundamentals of Supply Chain Theory (Hardcover, 2) 
· 분류 : 외국도서 > 경제경영 > 제품생산관리
· ISBN : 9781119024842
· 쪽수 : 784쪽

List of Figures xxi

List of Tables xxvii

List of Algorithms xxx

Preface xxxi

1 Introduction 1

1.1 The Evolution of Supply Chain Theory 1

1.2 Definitions and Scope 2

1.3 Levels of Decision Making in Supply Chain Management 4

2 Forecasting and Demand Modeling 5

2.1 Introduction 5

2.2 Classical Demand Forecasting Methods 6

2.2.1 Moving Average 6

2.2.2 Exponential Smoothing 7

2.2.3 Linear Regression 13

2.3 Forecast Accuracy 15

2.3.1 MAD, MSE, and MAPE 15

2.3.2 Forecast Errors for Moving Average and Exponential Smoothing 16

2.4 Machine Learning in Demand Forecasting 17

2.4.1 Introduction 17

2.4.2 Machine Learning 18

2.5 Demand Modeling Techniques 24

2.6 Bass Diffusion Model 24

2.6.1 The Model 25

2.6.2 Discrete-Time Version 28

2.6.3 Parameter Estimation 28

2.6.4 Extensions 30

2.7 Leading Indicator Approach 30

2.8 Discrete Choice Models 33

2.8.1 Introduction to Discrete Choice 33

2.8.2 The Multinomial Logit Model 34

2.8.3 Example Application to Supply Chain Management 37

Case Study: Semiconductor Demand Forecasting at Intel 38

Problems 39

3 Deterministic Inventory Models 45

3.1 Introduction to Inventory Modeling 45

3.1.1 Why Hold Inventory? 45

3.1.2 Classifying Inventory Models 47

3.1.3 Costs 48

3.1.4 Inventory Level and Inventory Position 50

3.1.5 Roadmap 50

3.2 Continuous Review: The Economic Order Quantity Problem 51

3.2.1 Problem Statement 51

3.2.2 Cost Function 52

3.2.3 Optimal Solution 53

3.2.4 Sensitivity Analysis 55

3.2.5 Order Lead Times 56

3.3 Power-of-Two Policies 57

3.3.1 Analysis 57

3.3.2 Error Bound 58

3.4 The EOQ with Quantity Discounts 60

3.4.1 All-Units Discounts 62

3.4.2 Incremental Discounts 64

3.4.3 Modified All-Units Discounts 66

3.5 The EOQ with Planned Backorders 67

3.6 The Economic Production Quantity Model 70

3.7 Periodic Review: The Wagner–Whitin Model 72

3.7.1 Problem Statement 72

3.7.2 MIP Formulation 72

3.7.3 Dynamic Programming Algorithm 73

3.7.4 Extensions 76

Case Study: Ice Cream Production and Inventory at Scotsburn Dairy Group 76

Problems 77

4 Stochastic Inventory Models: Periodic Review 87

4.1 Inventory Policies 87

4.2 Demand Processes 89

4.3 Periodic Review with Zero Fixed Costs: Base-Stock Policies 89

4.3.1 Base-Stock Policies 90

4.3.2 Single Period: The Newsvendor Problem 90

4.3.3 Finite Horizon 102

4.3.4 Infinite Horizon 105

4.4 Periodic Review with Non-Zero Fixed Costs: (s; S) Policies 114

4.4.1 (s; S) Policies 114

4.4.2 Single Period 115

4.4.3 Finite Horizon 116

4.4.4 Infinite Horizon 117

4.5 Policy Optimality 123

4.5.1 Zero Fixed Costs: Base-Stock Policies 124

4.5.2 Non-Zero Fixed Costs: (s; S) Policies 129

4.6 Lost Sales 136

4.6.1 Zero Lead Time 136

4.6.2 Non-Zero Lead Time 137

Case Study: Optimization of Warranty Inventory at Hitachi 138

Problems 140

5 Stochastic Inventory Models: Continuous Review 155

5.1 (r;Q) Policies 155

5.2 Exact (r;Q) Problem with Continuous Demand Distribution 156

5.2.1 Expected Cost Function 157

5.2.2 Optimality Conditions 159

5.3 Approximations for (r;Q) Problem with Continuous Distribution 161

5.3.1 Expected-Inventory-Level Approximation 161

5.3.2 EOQB Approximation 166

5.3.3 EOQ+SS Approximation 166

5.3.4 Loss-Function Approximation 167

5.3.5 Performance of Approximations 169

5.4 Exact (r;Q) Problem with Continuous Distribution: Properties of Optimal r and Q 170

5.4.1 Optimization of r and Q 172

5.4.2 Non-Controllable and Controllable Costs 172

5.4.3 Relationship to EOQB 173

5.5 Exact (r;Q) Problem with Discrete Distribution 177

Case Study: (r;Q) Inventory Optimization at Dell 180

Problems 182

6 Multi-Echelon Inventory Models 187

6.1 Introduction 187

6.1.1 Multi-Echelon Network Topologies 188

6.1.2 Stochastic vs. Guaranteed Service 189

6.2 Stochastic-Service Models 191

6.2.1 Serial Systems 191

6.2.2 Exact Approach for Serial Systems 193

6.2.3 Heuristic Approach for Serial Systems 197

6.2.4 Other Network Topologies 202

6.3 Guaranteed-Service Models 203

6.3.1 Introduction 203

6.3.2 Demand 204

6.3.3 Single-Stage Network 204

6.3.4 Serial Systems 207

6.3.5 Tree Systems 210

6.3.6 Solution Method 211

6.4 Closing Thoughts 217

Case Study: Multiechelon Inventory Optimization at Procter & Gamble 222

Problems 223

7 Pooling and Flexibility 229

7.1 Introduction 229

7.2 The Risk-Pooling Effect 230

7.2.1 Overview 230

7.2.2 Problem Statement 231

7.2.3 Decentralized System 231

7.2.4 Centralized System 231

7.2.5 Comparison 232

7.2.6 Magnitude of Risk-Pooling Effect 234

7.2.7 Closing Thoughts 235

7.3 Postponement 236

7.4 Transshipments 237

7.4.1 Introduction 237

7.4.2 Problem Statement 237

7.4.3 Expected Cost 239

7.4.4 Benefits of Transshipments 242

7.5 Process Flexibility 244

7.5.1 Introduction 244

7.5.2 Flexibility Design Guidelines 245

7.5.3 Optimality of the Chaining Structure 249

7.6 A Process Flexibility Optimization Model 253

7.6.1 Formulation 253

7.6.2 Lagrangian Relaxation 255

Case Study: Risk Pooling and Inventory Management at Yedioth Group 257

Problems 259

8 Facility Location Models 267

8.1 Introduction 267

8.2 The Uncapacitated Fixed-Charge Location Problem 269

8.2.1 Problem Statement 269

8.2.2 Formulation 270

8.2.3 Lagrangian Relaxation 272

8.2.4 The DUALOC Algorithm 282

8.2.5 Heuristics for the UFLP 291

8.3 Other Minisum Models 295

8.3.1 The Capacitated Fixed-Charge Location Problem (CFLP) 296

8.3.2 The p-Median Problem (pMP) 298

8.4 Covering Models 305

8.4.1 The Set Covering Location Problem (SCLP) 306

8.4.2 The Maximal Covering Location Problem (MCLP) 307

8.4.3 The p-Center Problem (pCP) 309

8.5 Other Facility Location Problems 314

8.5.1 Undesirable Facilities 314

8.5.2 Competitive Location 315

8.5.3 Hub Location 316

8.5.4 Dynamic Location 317

8.6 Stochastic and Robust Location Models 317

8.6.1 Introduction 317

8.6.2 The Stochastic Fixed-Charge Location Problem 318

8.6.3 The Minimax Fixed-Charge Location Problem 320

8.7 Supply Chain Network Design 321

8.7.1 Node Design 322

8.7.2 Arc Design 329

Case Study: Locating Fire Stations in Istanbul 332

Problems 335

9 Supply Uncertainty 355

9.1 Introduction to Supply Uncertainty 355

9.2 Inventory Models with Disruptions 356

9.2.1 The EOQ Model with Disruptions 357

9.2.2 The Newsvendor Problem with Disruptions 360

9.3 Inventory Models with Yield Uncertainty 365

9.3.1 The EOQ Model with Yield Uncertainty 366

9.3.2 The Newsvendor Problem with Yield Uncertainty 369

9.4 A Multi-Supplier Model 372

9.4.1 Problem Statement 373

9.4.2 Expected Profit 374

9.4.3 Optimality Conditions 375

9.4.4 Supplier Selection 377

9.4.5 Closing Thoughts 383

9.5 The Risk-Diversification Effect 384

9.5.1 Problem Statement 384

9.5.2 Notation 384

9.5.3 Optimal Solution 385

9.5.4 Mean and Variance of Optimal Cost 385

9.5.5 Supply Disruptions and Stochastic Demand 386

9.6 A Facility Location Model with Disruptions 387

9.6.1 Introduction 387

9.6.2 Notation 390

9.6.3 Formulation 391

9.6.4 Lagrangian Relaxation 392

9.6.5 Tradeoff Curves 393

Case Study: Disruption Management at Ford 394

Problems 396

10 The Traveling Salesman Problem 403

10.1 Supply Chain Transportation 403

10.2 Introduction to the TSP 404

10.2.1 Overview 404

10.2.2 Formulation of the TSP 406

10.3 Exact Algorithms for the TSP 408

10.3.1 Dynamic Programming 408

10.3.2 Branch-and-Bound 408

10.3.3 Branch-and-Cut 410

10.4 Construction Heuristics for the TSP 416

10.4.1 Nearest Neighbor 417

10.4.2 Nearest Insertion 419

10.4.3 Farthest Insertion 424

10.4.4 Convex Hull 424

10.4.5 GENI 426

10.4.6 Minimum Spanning Tree Heuristic 430

10.4.7 Christofides’ Heuristic 433

10.5 Improvement Heuristics for the TSP 436

10.5.1 k-Opt Exchanges 437

10.5.2 Or-Opt Exchanges 440

10.5.3 Unstringing and Stringing 440

10.6 Bounds and Approximations for the TSP 442

10.6.1 The Held–Karp Bound 442

10.6.2 Control Zones 449

10.6.3 Integrality Gap 450

10.6.4 Approximation Bounds 451

10.6.5 Tour Length as a Function of n 451

10.7 World Records 452

Case Study: Routing Meals on Wheels Deliveries 453

Problems 455

11 The Vehicle Routing Problem 463

11.1 Introduction to the VRP 463

11.1.1 Overview 463

11.1.2 Notation and Assumptions 465

11.1.3 Formulation of the VRP 465

11.2 Exact Algorithms for the VRP 468

11.2.1 Dynamic Programming 468

11.2.2 Branch-and-Bound 470

11.2.3 Branch-and-Cut 471

11.2.4 Set Covering 472

11.3 Heuristics for the VRP 475

11.3.1 The Clarke–Wright Savings Heuristic 475

11.3.2 The Sweep Heuristic 480

11.3.3 The Location-Based Heuristic 481

11.3.4 Improvement Heuristics 487

11.3.5 Metaheuristics 488

11.4 Bounds and Approximations for the VRP 493

11.4.1 TSP-Based Bounds 493

11.4.2 Optimal Objective Function Value as a Function of n 497

11.5 Extensions of the VRP 498

11.5.1 Distance-Constrained VRP 498

11.5.2 VRP with Time Windows 499

11.5.3 VRP with Backhauls 499

11.5.4 VRP with Pickups and Deliveries 500

11.5.5 Periodic VRP 500

Case Study: ORION: Optimizing Delivery Routes at UPS 501

Problems 502

12 Integrated Supply Chain Models 509

12.1 Introduction 509

12.2 A Location–Inventory Model 510

12.2.1 Introduction 510

12.2.2 Problem Statement 512

12.2.3 Notation 512

12.2.4 Objective Function 513

12.2.5 NLIP Formulation 514

12.2.6 Lagrangian Relaxation 515

12.2.7 Column Generation 522

12.2.8 Conic Optimization 525

12.3 A Location–Routing Model 527

12.4 An Inventory–Routing Model 529

Case Study: Inventory–Routing at Frito-Lay 532

Problems 533

13 The Bullwhip Effect 537

13.1 Introduction 537

13.2 Proving the Existence of the Bullwhip Effect 539

13.2.1 Introduction 539

13.2.2 Demand Signal Processing 540

13.2.3 Rationing Game 544

13.2.4 Order Batching 546

13.2.5 Price Speculation 549

13.3 Reducing the Bullwhip Effect 550

13.3.1 Demand Signal Processing 550

13.3.2 Rationing Game 552

13.3.3 Order Batching 552

13.3.4 Price Speculation 552

13.4 Centralizing Demand Information 553

13.4.1 Centralized System 553

13.4.2 Decentralized System 554

Case Study: Reducing the Bullwhip Effect at Philips Electronics 554

Problems 557

14 Supply Chain Contracts 563

14.1 Introduction 563

14.2 Introduction to Game Theory 564

14.3 Notation 565

14.4 Preliminary Analysis 566

14.5 The Wholesale Price Contract 568

14.6 The Buyback Contract 574

14.7 The Revenue Sharing Contract 578

14.8 The Quantity Flexibility Contract 581

Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 584

Problems 586

15 Auctions 591

15.1 Introduction 591

15.2 The English Auction 593

15.3 Combinatorial Auctions 595

15.3.1 The Combinatorial Auction Problem 596

15.3.2 Solving the Set-Packing Problem 597

15.3.3 Truthful Bidding 598

15.4 The Vickrey-Clarke-Groves Auction 599

15.4.1 Introduction 599

15.4.2 Weaknesses of the VCG Auction 602

15.4.3 VCG Auction as a Cooperative Game 605

Case Study: Procurement Auctions for Mars 608

Problems 610

16 Applications of Supply Chain Theory 615

16.1 Introduction 615

16.2 Electricity Systems 615

16.2.1 Energy Storage 616

16.2.2 Transmission Capacity Planning 621

16.2.3 Electricity Network Design 623

16.3 Health Care 625

16.3.1 Production Planning and Contracting for Influenza Vaccines 625

16.3.2 Inventory Management for Blood Platelets 628

16.4 Public-Sector Operations 632

16.4.1 Disaster Relief Routing 632

16.4.2 Passenger Screening 635

16.4.3 Public Housing Location 637

Case Study: Optimization of the Natural Gas Supply Chain in China 639

Problems 641

Appendix A: Multiple-Chapter Problems 643

Problems 643

Appendix B: How to Write Proofs: A Short Guide 651

B.1 How to Prove Anything 651

B.2 Types of Things You May Be Asked to Prove 653

B.3 Proof Techniques 655

B.3.1 Direct Proof 655

B.3.2 Proof by Contradiction 655

B.3.3 Proof by Mathematical Induction 656

B.3.4 Proof by Cases 657

B.4 Other Advice 657

Appendix C: Helpful Formulas 661

C.1 Positive and Negative Parts 661

C.2 Standard Normal Random Variables 662

C.3 Loss Functions 662

C.3.1 General Continuous Distributions 662

C.3.2 Standard Normal Distribution 663

C.3.3 Non-Standard Normal Distributions 664

C.3.4 General Discrete Distributions 664

C.3.5 Poisson Distribution 665

C.4 Differentiation of Integrals 665

C.4.1 Variable of Differentiation Not in Integral Limits 665

C.4.2 Variable of Differentiation in Integral Limits 665

C.5 Geometric Series 666

C.6 Normal Distributions in Excel and MATLAB 666

C.7 Partial Expectations 667

Appendix D: Integer Optimization Techniques 669

D.1 Lagrangian Relaxation 669

D.1.1 Overview 669

D.1.2 Bounds 670

D.1.3 Subgradient Optimization 672

D.1.4 Stopping Criteria 674

D.1.5 Other Problem Types 674

D.1.6 Branch-and-Bound 675

D.1.7 Algorithm Summary 675

D.2 Column Generation 675

D.2.1 Overview 675

D.2.2 Master Problem and Subproblem 677

D.2.3 An Example: The Cutting Stock Problem 678

D.2.4 Column Generation for Integer Programs 680

Bibliography 681

Subject Index 712

Author Index 725