Fundamentals of supply chain theory:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken
Wiley
2019
|
| Ausgabe: | 2nd edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 681-711 |
| Beschreibung: | xxxix, 733 Seiten Diagramme |
| ISBN: | 9781119024842 |
Internformat
MARC
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| adam_text | CONTENTS List of Figures List of Tables List of Algorithms Preface About the Companion Website 1 2 xxi xxvii xxix xxxi xxxix Introduction 1 l. I 1.2 1.3 1 2 4 The Evolution of Supply Chain Theory Definitions and Scope Levels of Decision-Making in Supply Chain Management Forecasting and Demand Modeling 5 2.1 2.2 5 6 6 7 13 15 15 2.3 2.4 Introduction Classical Demand Forecasting Methods 2.2.1 Moving Average 2.2.2 Exponential Smoothing 2.2.3 Linear Regression Forecast Accuracy 2.3.1 MAD, MSE, and МАРЕ 2.3.2 Forecast Errors for MovingAverage and Exponential Smoothing Machine Learning in Demand Forecasting 2.4.1 Introduction 16 17 17 IX
x CONTENTS 2.5 2.6 2.7 2.8 3 2.4.2 Machine Learning Demand Modeling Techniques Bass Diffusion Model 2.6.1 The Model 2.6.2 Discrete-Time Version 2.6.3 Parameter Estimation 2.6.4 Extensions Leading Indicator Approach Discrete Choice Models 2.8.1 Introduction to Discrete Choice 2.8.2 The Multinomial Logit Model 2.8.3 Example Application to Supply ChainManagement Case Study: Semiconductor Demand Forecasting at Intel Problems Deterministic Inventory Models 3.1 3.2 3.3 3.4 3.5 3.6 3.7 18 23 24 25 28 29 29 30 33 33 34 37 38 39 45 Introduction to Inventory Modeling 3.1.1 Why Hold Inventory? 3.1.2 Classifying Inventory Models 3.1.3 Costs 3.1.4 Inventory Level and Inventory Position 45 45 47 48 50 3.1.5 Roadmap Continuous Review: The Economic Order Quantity Problem 3.2.1 Problem Statement 3.2.2 Cost Function 3.2.3 Optimal Solution 3.2.4 Sensitivity Analysis 3.2.5 Order Lead Times Power-of-Two Policies 3.3.1 Analysis 3.3.2 Error Bound The EOQ with Quantity Discounts 3.4.1 All-Units Discounts 3.4.2 Incremental Discounts 3.4.3 Modified All-Units Discounts The EOQ with Planned Backorders The Economic Production Quantity Model Periodic Review: The Wagner-Whitin Model 3.7.1 Problem Statement 3.7.2 МІР Formulation 50 51 51 52 53 55 56 57 57 58 60 62 64 66 67 70 72 72 72 3.7.3 3.7.4 Dynamic Programming Algorithm Extensions 73 76
CONTENTS Case Study. Ice Cream Production and Inventory at Scotsburn Dairy Group Problems 4 76 77 Stochastic Inventory Models: Periodic Review 87 4.1 4.2 4.3 Inventory Policies Demand Processes Periodic Review with Zero Fixed Costs: Base-Stock Policies 87 89 89 4.3.1 4.3.2 Base-Stock Policies Single Period: The Newsvendor Problem 90 90 4.3.3 Finite Horizon 4.3.4 Infinite Horizon Periodic Review with Nonzero Fixed Costs: (s. S) Policies 102 105 114 4.4.1 4.4.2 114 115 4.4 4.5 4.6 5 ХІ (s. S) Policies Single Period 4.4.3 Finite Horizon 4.4.4 Infinite Horizon Policy Optimality 4.5.1 Zero Fixed Costs:Base-Stock Policies 116 117 123 124 4.5.2 Nonzero Fixed Costs: (s. S) Policies Lost Sales 4.6.1 Zero Lead Time 4.6.2 Nonzero Lead Time Case Study : Optimization of Warranty Inventory at Hitachi 129 136 136 137 138 Problems 140 Stochastic Inventory Models: Continuous Review 155 5.1 5.2 (r. Q) Policies Exact (r, Q) Problem with Continuous Demand Distribution 5.2.1 Expected Cost Function 155 156 157 5.2.2 Optimality Conditions Approximations for (r, Q) Problemwith Continuous Distribution 159 161 5.3.1 5.3.2 5.3.3 5.3.4 161 166 166 167 5.3 5.4 Expected-Invcntory-Level Approximation EOQB Approximation EOQ+SS Approximation Loss-Function Approximation 5.3.5 Performance of Approximations Exact (r.Q) Problem with Continuous Distribution: Properties of Optimal?՝ and Q 169 5.4.1 5.4.2 172 !72 Optimization of r and Q Noncontrollable and Controllable Costs 170
xii CONTENTS 5.5 5.4.3 Relationship to EOQB Exact (r , Q) Problem with Discrete Distribution Case Study: (r. Q) Inventory Optimization at Dell Problems Multiechelon Inventory Models 6.1 6.2 173 177 180 182 187 Introduction 187 Multiechelon Network Topologies 6.1.1 6.1.2 Stochastic vs. Guaranteed Service Stochastic-Service Models 6.2.1 Serial Systems 188 189 191 191 6.2.2 6.2.3 6.2.4 6.3 6.4 Exact Approach for Serial Systems 193 Heuristic Approach for Serial Systems 197 Other Network Topologies 202 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 Closing Thoughts 217 ??? Case Study: Multiechelon Inventory Optimization at Procter Gamble Problems 223 Pooling and Flexibility 229 7.1 7.2 9?9 7.3 7.4 Introduction The Risk-Pooling Effect Overview 7.2.1 7.2.2 Problem Statement Decentralized System 7.2.3 7.2.4 Centralized System 7.2.5 Comparison 7.2.6 Magnitude of Risk-Pooling Effect 7.2.7 Closing Thoughts Postponement Transshipments 7.4.1 Introduction 7.4.2 Problem Statement 7.4.3 Expected Cost 7.4.4 Benefits of Transshipments 230 230 231 231 231 232 234 235 236 237 237 237 240 241
CONTENTS 7.5 7.6 8 Process Flexibility 243 7.5.1 Introduction 243 7.5.2 Flexibility Design Guidelines 245 7.5.3 Optimality of the Chaining Structure 248 A Process Flexibility Optimization Model 253 7.6.1 Formulation 253 7.6.2 Lagrangian Relaxation 254 Case Study: Risk Pooling and Inventory Management at Yedioth Group 257 Problems 259 Facility Location Models 267 8.1 8.2 267 269 269 270 272 282 291 295 296 298 305 306 307 309 314 314 315 316 317 317 317 318 320 321 322 329 332 335 8.3 8.4 8.5 8.6 8.7 9 хш Introduction The Uncapacitated Fixed-Charge Location Problem 8.2.1 Problem Statement 8.2.2 Formulation 8.2.3 Lagrangian Relaxation 8.2.4 The DUALOC Algorithm 8.2.5 Heuristics for the UFLP Other Minisum Models 8.3.1 The Capacitated Fixed-Charge Location Problem (CFLP) 8.3.2 The p-Median Problem (pMP) Covering Models 8.4.1 The Set Covering Location Problem (SCLP) 8.4.2 The Maximal Covering Location Problem (MCLP) 8.4.3 The p-Center Problem (pCP) Other Facility Location Problems 8.5.1 Undesirable Facilities 8.5.2 Competitive Location 8.5.3 Hub Location 8.5.4 Dynamic Location Stochastic and Robust Location Models 8.6.1 Introduction 8.6.2 The Stochastic Fixed-Charge Location Problem 8.6.3 The Minimax Fixed-Charge Location Problem Supply Chain Network Design 8.7.1 Node Design 8.7.2 Arc Design Case Study: Locating Fire Stationsin Istanbul Problems Supply Uncertainty 355
XIV CONTENTS 9.1 9.2 9.3 9.4 9.5 9.6 10 Introduction to Supply Uncertainty Inventory Models with Disruptions 9.2.1 The EOQ Model with Disruptions 9.2.2 The Newsvendor Problem with Disruptions Inventory Models with Yield Uncertainty 355 356 357 360 365 9.3.1 9.3.2 366 369 The EOQ Model with Yield Uncertainty The Newsvendor Problem with Yield Uncertainty A Multisupplier Model 9.4.1 Problem Statement 9.4.2 Expected Profit 9.4.3 Optimality Conditions 9.4.4 Supplier Selection 9.4.5 Closing Thoughts The Risk-Diversification Effect 9.5.1 Problem Statement 9.5.2 Notation 9.5.3 Optimal Solution 9.5.4 Mean and Variance of Optimal Cost 9.5.5 Supply Disruptions and Stochastic Demand A Facility Location Model with Disruptions 9.6.1 Introduction 9.6.2 Notation 9.6.3 Formulation 9.6.4 Lagrangian Relaxation 9.6.5 Trade-off Curves Case Study. Disruption Management at Ford Problems 372 373 374 375 377 383 384 384 384 385 385 386 387 387 390 391 392 393 395 396 The Traveling Salesman Problem 403 10.1 10.2 403 404 404 406 408 408 408 410 416 417 419 423 425 10.3 10.4 Supply Chain Transportation Introduction to the TSP 10.2.1 Overview 10.2.2 Formulation of the TSP Exact Algorithms for the TSP 10.3.1 Dynamic Programming 10.3.2 Branch-and-Bound 10.3.3 Branch-and-Cut Construction Heuristics for the TSP 10.4.1 Nearest Neighbor 10.4.2 Nearest Insertion 10.4.3 Farthest Insertion 10.4.4 Convex Hull
CONTENTS 10.5 10.6 10.7 10.4.5 GENI 10.4.6 Minimum Spanning Tree Heuristic 10.4.7 Chri stofides ’ Heuristic Improvement Heuristics for the TSP 10.5.1 /¡•-Opt Exchanges 10.5.2 Or-Opt Exchanges 10.5.3 Unstringing and Stringing Bounds and Approximations for the TSP 10.6.1 The Held-Karp Bound 10.6.2 Control Zones 10.6.3 Integrality Gap 10.6.4 Approximation Bounds 10.6.5 Tour Length as a Function of n World Records Case Study: Routing Meals on Wheels Deliveries Problems The Vehicle Routing Problem 11.1 Introduction to the VRP 11 1.1 Overview 11.1.2 Notation and Assumptions 11.1.3 Formulation oflhe VRP Exact Algorithms for the VRP 11.2.1 Dynamic Programming 11.2.2 Branch-and-Bound 11.2.3 Branch-and-Cut 1 1.2.4 Set Covering Heuristics for the VRP 1 1.3.1 The Clarke-Wright Savings Heuristic 11.3.2 The Sweep Heuristic 11.3.3 The Location-Based Heuristic 11.3.4 Improvement Heuristics 11.3.5 Metaheuristics Bounds and Approximations for the VRP 11.4.1 TSP-Based Bounds 11.4.2 Optimal Objective Function Value as a Function of n Extensions of the VRP 11.5.1 Distance-Constrained VRP 11.5,2 VRP with Time Windows 11.5.3 VRP with Backhauls 11.5.4 VRP with Pickups and Deliveries 11.5.5 Periodic VRP . 11.2 11.3 11.4 11.5 XV 427 430 433 436 436 438 440 442 442 449 450 451 451 452 453 455 463 463 463 465 465 468 468 470 471 472 475 475 480 481 488 488 495 495 497 498 499 499 500 500 500
xvi 12 CONTENTS 501 Problems 502 Integrated Supply Chain Models 511 12.1 Introduction 511 12.2 A Location-Inventory Model 12.2.1 Introduction 12.2.2 Problem Statement 512 512 514 12.2.3 Notation 12.2.4 Objective Function 12.2.5 NLIP Formulation 12.2.6 Lagrangian Relaxation 12.2.7 Column Generation 12.2.8 Conic Optimization A Location-Routing Model An Inventory-Routing Model Case Study: Inventory-Routing at Frito-Lay Problems 514 515 516 517 524 527 529 531 534 535 12.3 12.4 13 Case Study: ORION: Optimizing Delivery Routes at UPS The 13.1 13.2 13.3 13.4 Bullwhip Effect Introduction Proving the Existence of the Bullwhip Effect 13.2.1 Introduction 13.2.2 Demand Signal Processing 13.2.3 Rationing Game 13.2.4 Order Batching 13.2.5 Price Speculation Reducing the Bullwhip Effect 13.3.1 Demand Signal Processing 13.3.2 Rationing Game 13.3.3 Order Batching 13.3.4 Price Speculation Centralizing Demand Information 13.4.1 Centralized System 13.4.2 Decentralized System Case Study: Reducing the Bullwhip Effect at Philips Electronics Problems 14Supply Chain Contracts 14.1 14.2 Introduction Introduction to Game Theory 539 539 541 541 542 546 548 551 552 552 554 554 554 555 555 556 556 559 563 563 564
CONTENTS 14.3 14.4 14.5 14.6 14.7 14.8 15 16 xvii Notation Preliminary Analysis The Wholesale Price Contract The Buyback Contract The Revenue Sharing Contract The Quantity Flexibility Contract Case Study: Designing a Shared-Savings Contract at McGriff Treading Company 565 566 568 574 578 581 Problems 586 584 Auctions 591 15.1 15.2 15.3 Introduction The English Auction Combinatorial Auctions 15.3.1 The Combinatorial Auction Problem 15.3.2 Solving the Set-Packing Problem 591 593 595 595 597 15.3.3 Truthful Bidding 598 15.4 The Vickrey-Clarkc-Grovcs Auction 15.4.1 Introduction 15.4.2 Weaknesses of the VCG Auction 599 599 602 15.4.3 VCG Auction as a Cooperative Game Case Study : Procurement Auctions for Mars 605 608 Problems 610 Applications of Supply Chain Theory 615 16.1 16.2 Introduction Electricity Systems 16.2.1 Energy Storage 16.2.2 Transmission Capacity Planning 615 615 616 621 16.2.3 Electricity Network Design HealthCare 623 625 16.3 16.3.1 16.4 Production Planning and Contracting for Influenza Vaccines 625 16.3.2 Inventory Management for BloodPlatelets Public Sector Operations 628 632 16.4.1 Disaster Relief Routing 16.4.2 Passenger Screening 632 635 16.4.3 Public Housing Location Case Study: Optimization of the Natural Gas Supply Chain in China Problems 637 639 641 Appendix A: Multiple-Chapter Problems 643
xviii CONTENTS Problems Appendix В: How to Write Proofs: A Short Guide B.l B.2 B.3 B.4 How to Prove Anything Types of Things You May Be Asked to Prove Proof Techniques Direct Proof B.3.1 Proof by Contradiction B.3.2 Proof by Mathematical Induction B.3.3 Proof by Cases B.3.4 Other Advice Appendix C: Helpful Formulas C.l C.2 C.3 C.4 C.5 C.6 C.l Positive and Negative Parts Standard Normal Random Variables Loss Functions General Continuous Distributions C.3.1 Standard Normal Distribution C.3.2 C.3.3 Nonstandard Normal Distributions C.3.4 General Discrete Distributions C.3.5 Poisson Distribution Differentiation of Integrals C.4.1 Variable of Differentiation Not in Integral Limits C.4.2 Variable of Differentiation in Integral Limits Geometric Series Normal Distributions in Excel and MATLAB Partial Expectations Appendix D: Integer Optimization Techniques D.l D.2 Lagrangian Relaxation D. 1.1 Overview D.l .2 Bounds D.1.3 Subgradient Optimization D.l.4 Stopping Criteria D.l.5 Other Problem Types D.l.6 Branch-and-Bound D.l .7 Algorithm Summary Column Generation D.2.1 Overview D.2.2 Master Problem and Subproblem D.2.3 An Example: The Cutting Stock Problem 643 651 651 653 655 655 655 656 657 657 661 661 662 662 662 663 664 664 665 665 665 665 666 666 667 669 669 669 670 672 674 674 675 675 675 675 677 678
CONTENTS D.2.4 Column Generation for Integer Programs ХІХ 680 References 681 Subject Index 712 Author Index 725
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| id | DE-604.BV046099246 |
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| indexdate | 2024-08-01T11:24:14Z |
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| isbn | 9781119024842 |
| language | English |
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| title | Fundamentals of supply chain theory |
| title_auth | Fundamentals of supply chain theory |
| title_exact_search | Fundamentals of supply chain theory |
| title_full | Fundamentals of supply chain theory Lawrence Snyder, Lehigh University, Zuo-Jun Max Shen, University of California, Berkeley |
| title_fullStr | Fundamentals of supply chain theory Lawrence Snyder, Lehigh University, Zuo-Jun Max Shen, University of California, Berkeley |
| title_full_unstemmed | Fundamentals of supply chain theory Lawrence Snyder, Lehigh University, Zuo-Jun Max Shen, University of California, Berkeley |
| title_short | Fundamentals of supply chain theory |
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Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
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2000 QP 530 S675(2) |
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| Exemplar 1 | ausleihbar Verfügbar Bestellen |