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by Zuo-Jun Max Shen, Lawrence V. Snyder
Fundamentals of Supply Chain Theory, 2nd Edition
Cover
Preface
Goals of This Book
Who Should Read This Book
Organization of This Book
New in the Second Edition
Resources for Instructors
Acknowledgments
Chapter 1: Introduction
1.1 The Evolution of Supply Chain Theory
1.2 Definitions and Scope
1.3 Levels of Decision‐Making in Supply Chain Management
Chapter 2: Forecasting and Demand Modeling
2.1 Introduction
2.2 Classical Demand Forecasting Methods
2.3 ForecastAccuracy
2.4 Machine Learning in Demand Forecasting
2.5 Demand Modeling Techniques
2.6 Bass Diffusion Model
2.7 Leading Indicator Approach
2.8 Discrete ChoiceModels
PROBLEMS
Chapter 3: Deterministic Inventory Models
3.1 Introduction to Inventory Modeling
3.2 Continuous Review: The Economic Order Quantity Problem
3.3 Power‐of‐Two Policies
3.4 The EOQ with Quantity Discounts
3.5 The EOQ with PlannedBackorders
3.6 The Economic Production Quantity Model
3.7 Periodic Review: The Wagner–WhitinModel
PROBLEMS
Chapter 4: Stochastic Inventory Models: PeriodicReview
4.1 Inventory Policies
4.2 Demand Processes
4.3 Periodic Review with Zero Fixed Costs: Base‐Stock Policies
4.4 Periodic Review with Nonzero Fixed Costs: Policies
4.5 Policy Optimality
4.6 Lost Sales
PROBLEMS
Chapter 5: Stochastic Inventory Models: Continuous Review
5.1 Policies
5.2 Exact Problem with Continuous Demand Distribution
5.3 Approximations for Problem with Continuous Distribution
5.4 Exact Problemwith Continuous Distribution: Properties of Optimal and Q
5.5 Exact Problem with Discrete Distribution
PROBLEMS
Chapter 6: Multiechelon InventoryModels
6.1 Introduction
6.2 Stochastic‐ServiceModels
6.3 Guaranteed‐ServiceModels
6.4 Closing Thoughts
PROBLEMS
Chapter 7: Pooling and Flexibility
7.1 Introduction
7.2 The Risk‐Pooling Effect
7.3 Postponement
7.4 Transshipments
7.5 Process Flexibility
7.6 A Process Flexibility Optimization Model
PROBLEMS
Chapter 8: Facility Location Models
8.1 Introduction
8.2 The Uncapacitated Fixed‐Charge Location Problem
8.3 Other Minisum Models
8.4 Covering Models
8.5 Other Facility Location Problems
8.6 Stochastic and Robust Location Models
8.7 Supply Chain Network Design
PROBLEMS
Chapter 9: Supply Uncertainty
9.1 Introduction to Supply Uncertainty
9.2 Inventory Models withDisruptions
9.3 Inventory Models with YieldUncertainty
9.4 A MultisupplierModel
9.5 The Risk‐Diversification Effect
9.6 A Facility Location Model withDisruptions
PROBLEMS
Chapter 10: The Traveling Salesman Problem
10.1 Supply Chain Transportation
10.2 Introduction to the TSP
10.3 Exact Algorithms for the TSP
10.4 Construction Heuristics for theTSP
10.5 Improvement Heuristics for theTSP
10.6 Bounds and Approximations for the TSP
10.7 World Records
PROBLEMS
Chapter 11: The Vehicle Routing Problem
11.1 Introduction to the VRP
11.2 Exact Algorithms for the VRP
11.3 Heuristics for the VRP
11.4 Bounds and Approximations for the VRP
11.5 Extensions of the VRP
PROBLEMS
Chapter 12: Integrated Supply Chain Models
12.1 Introduction
12.2 A Location–InventoryModel
12.3 A Location–Routing Model
12.4 An Inventory–Routing Model
PROBLEMS
Chapter 13: The Bullwhip Effect
13.1 Introduction
13.2 Proving the Existence of the Bullwhip Effect
13.3 Reducing the Bullwhip Effect
13.4 Centralizing Demand Information
PROBLEMS
Chapter 14: Supply Chain Contracts
14.1 Introduction
14.2 Introduction to Game Theory
14.3 Notation
14.4 Preliminary Analysis
14.5 The Wholesale Price Contract
14.6 The Buyback Contract
14.7 The Revenue Sharing Contract
14.8 The Quantity Flexibility Contract
PROBLEMS
Chapter 15: Auctions
15.1 Introduction
15.2 The English Auction
15.3 Combinatorial Auctions
15.4 The Vickrey–Clarke–GrovesAuction
PROBLEMS
Chapter 16: Applications of Supply Chain Theory
16.1 Introduction
16.2 Electricity Systems
16.3 Health Care
16.4 Public Sector Operations
PROBLEMS
Appendix A: Multiple‐Chapter Problems
PROBLEMS
Appendix B: How to Write Proofs: A Short Guide
B.1 How to Prove Anything
B.2 Types of Things You May Be Asked to Prove
B.3 Proof Techniques
B.4 Other Advice
Appendix C: Helpful Formulas
C.1 Positive and Negative Parts
C.2 Standard Normal Random Variables
C.3 Loss Functions
C.4 Differentiation of Integrals
C.5 Geometric Series
C.6 Normal Distributions in Excel and MATLAB
C.7 Partial Expectations
Appendix D: Integer Optimization Techniques
D.1 Lagrangian Relaxation
D.2 Column Generation
Reference
Subject Index
Author Index
End User License Agreement
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Dedication
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List of Tables
List of Figures
Figure 1.1 Schematic diagram of supply chain network.
Figure 1.2 Supply “chain.”
Figure 2.1 Weight distribution for single exponential smoothing.
Figure 2.2 Random demands with trend and seasonality.
Figure 2.3 Observed demands for
The TSP Mystery
and best‐fit line for Exam...
Figure 2.4 Regression tree for baseball jerseys for Example 2.8.
Figure 2.5 A simple neural network.
Figure 2.6 Color TVs in the 1960s: Forecasts from Bass model and actual de...
Figure 2.7 Bass diffusion curve.
Figure 2.8 An example of a leading‐indicator product.
Figure 3.1 Inventory level curve.
Figure 3.2 EOQ inventory level curve.
Figure 3.3 Fixed, holding, and total costs as a function of
Q
.
Figure 3.4 Total purchase cost
under quantity discounts.
Figure 3.5Figure 3.5 Total purchase cost
for Example 3.5.
Figure 3.6 Total cost curves for all‐units quantity discount structure.
Figure 3.7 Total cost curves for incremental quantity discount structure....
Figure 3.8Figure 3.8 Total purchase cost
for modified all‐units discou...
Figure 3.9 EOQB inventory curve.
Figure 3.10 Inventory–backorder trade‐off in EOQB.
Figure 3.11 EPQ inventory level curve.
Figure 3.12 Wagner–Whitin network.
Figure 3.13 Shortest path network for Problem 3.7.
Figure 3.14 Inventory level curve for Problem 3.23.
Figure 3.15 Inventory level curve for Problem 3.26.
Figure 4.1 Optimal solution to newsvendor problem plotted on demand distri...
Figure 4.2 Optimal solution to newsvendor problem plotted on normal demand...
Figure 4.3
and
.
Figure 4.4 DP results,
:
.
Figure 4.5 Inventory dynamics. All items on order or on hand in period
t
h...
Figure 4.6 DP results,
:
.
Figure 4.7Figure 4.7 Hypothetical shapes of the function
.
Figure 4.8 Nonconvexity of
.
Figure 4.9
for
;
,
,
,
,
,
,
,
.
Figure 4.10
K
‐convexity.
Figure 4.11 Properties of
K
‐convex functions from Lemma 4.16.
Figure 4.12Figure 4.12 Proof of Lemma 4.16.
Figure 4.13 Process for handling returned drives at Hitachi.
Figure 4.14
functions for Problem 4.38, with fixed cost
and order...
Figure 4.15
function for Problem 4.39, with fixed cost
.
Figure 5.1 Inventory level (solid line) and inventory position (dashed lin...
Figure 5.2 Inventory costs are equal at start and end of replenishment cyc...
Figure 5.3 Expected inventory curve for
policy.
Figure 5.4 Relative error of
approximations.
Figure 5.5
and
.
Figure 5.6
and
.
Figure 5.7
and
.
Figure 5.8 Determining which
y
‐values are optimal given
.
Figure 6.1 Multiechelon network topologies.
Figure 6.2 Interpretation of stockout penalties.
Figure 6.3
N
‐stage serial system in stochastic‐service model.
Figure 6.4 3‐Stage serial system for Example 6.1.
Figure 6.5 Functions from Theorem 6.3 for Example 6.1.
Figure 6.6 Digital camera supply chain network..
Figure 6.7 Single‐stage network.
Figure 6.8
N
‐stage serial system in guaranteed‐service model.
Figure 6.9 Feasible region for two‐stage system.
Figure 6.10 Example network for SSSPP DP algorithm for serial systems.
Figure 6.11 A counterexample to the “all‐or‐nothing” claim for tree system...
Figure 6.12 Relabeling the network.
Figure 6.13 Example network for SSSPP DP algorithm for tree systems.
Figure 6.14 Digital camera supply chain network, with holding costs and pr...
Figure 6.15 Optimal CSTs and inventories for digital camera supply chain (...
Figure 6.16 Optimal CSTs and inventories for digital camera supply chain (...
Figure 6.17 SSSPP trade‐off curve: expected cost vs. end‐customer CST.
Figure 6.18 Baseball‐hat supply chain for Problem 6.9.
Figure 7.1 The risk‐pooling effect with identical retailers.
Figure 7.2 Possible realizations of transshipment and ending inventories.....
Figure 7.3 Examples of flexibility configurations.
Figure 7.4 Two chaining structures.
Figure 7.5
structure for
and
.
Figure 7.6 Examples of different chaining structures for nonhomogeneous ...
Figure 7.7 Three‐stage flexibility structure for Problem 7.10.
Figure 8.1 Facility location configurations. Squares represent facilities;...
Figure 8.2Figure 8.2 Optimal solution to 88‐node UFLP instance. Total cost...
Figure 8.3 Customer and facility layout for Example 8.2.
Figure 8.4 Sorted facility positions for Example 8.2.
Figure 8.5 Considering each facility for iteration 1 of greedy algorithm f...
Figure 8.6 Considering each facility for iteration 2 of greedy algorithm f...
Figure 8.7 Solutions from iterations 3, 4, and 5 of greedy algorithm for U...
Figure 8.8 Optimal solution to 88‐node
p
MP instance with
. Total cost =...
Figure 8.9 Greedy and swap solutions for 88‐node
p
MP instance with
.
Figure 8.10 Neighborhood of Springfield, IL in greedy solution to 88‐node ...
Figure 8.11 400‐mile coverage radii around facilities in 6‐median solution...
Figure 8.12 Optimal SCLP solution for 88‐node instance with coverage radiu...
Figure 8.13 Optimal MCLP solution for 88‐node instance with coverage radiu...
Figure 8.14 Coverage vs.
p
for 88‐node data set with 400‐mile coverage rad...
Figure 8.15 Optimal
p
CP solution for 88‐node instance with
. Maximum as...
Figure 8.16 Three echelons in node design problem: plants (
), DCs (
)...
Figure 8.17 Optimal solution to 98‐node node design instance. Total cost =...
Figure 8.18 Sequential‐optimization solution to 98‐node node design instan...
Figure 8.19 Simple arc design problem instance. Grey and black bars inside...
Figure 8.20 Hungary cities arc design problem instance. Grey and black bar...
Figure 8.21 Solutions to Hungary cities arc design problem instance. Arcs ...
Figure 8.22 10‐node facility location instance for Problems 8.2–8.11.
Figure 8.23 UFLP instance for Problem 8.20. Distances use Manhattan metric...
Figure 8.24 UFLP instance for Problem 8.21. Distances use Manhattan metric...
Figure 9.1 EOQ inventory curve with disruptions.
Figure 9.2 Exact EOQD cost
for Example 9.1.
Figure 9.3 Approximate and exact EOQD costs
and
for Example 9.2.
Figure 9.4 EOQ inventory curve with yield uncertainty.
Figure 9.5 pdf/pmf of quantity received from each supplier in Example 9.9....
Figure 9.6 pdf of quantity received from each supplier in Example 9.10. As...
Figure 9.7
for Example 9.10, varying
and
while keeping
fi...
Figure 9.8 UFLP solution for 49‐node data set..
Figure 9.9 UFLP solution for 49‐node data set, after disruption of facilit...
Figure 9.10 Reliable solution for 49‐node data set.
Figure 9.11 Sample RFLP trade‐off curve..
Figure 10.1 Hamiltonian cycles.
Figure 10.2
Car 54
TSP instance.
Figure 10.3 Optimal solution to
Car 54
TSP instance. Total distance = 10,8...
Figure 10.4 Subtours.
Figure 10.5 TSP instance for examples. If no edge is present between nodes...
Figure 10.6 Cutting planes.
Figure 10.7 Handle and teeth for 2‐matching inequality.
Figure 10.8 Handle and teeth for comb inequality.
Figure 10.9 Nearest‐neighbor tour beginning at node 1 for the example inst...
Figure 10.10 Nearest‐neighbor solution to
Car 54
TSP instance. Total dista...
Figure 10.11 Nearest‐insertion tour beginning at node 1 for example instan...
Figure 10.12 Nearest‐insertion solution to
Car 54
TSP instance. Total dist...
Figure 10.13 TSP instance for proof that NI bound is tight.
Figure 10.14 Cheapest‐insertion solution to
Car 54
TSP instance. Total dis...
Figure 10.15 Farthest‐insertion tour beginning at node 1 for the example i...
Figure 10.16 Farthest‐insertion solution to
Car 54
TSP instance. Total dis...
Figure 10.17 Convex‐hull tour for the example instance in Figure 10.5. Tot...
Figure 10.18 Convex‐hull solution to
Car 54
TSP instance. Total distance =...
Figure 10.19 Inserting a node between two nonadjacent nodes in the GENI he...
Figure 10.20 GENI Type I insertion.
Figure 10.21 GENI Type II insertion.
Figure 10.22 4‐neighborhood of node 5.
Figure 10.23 Tour generated by minimum spanning tree heuristic for instanc...
Figure 10.24 Tour obtained by applying MST heuristic to
Car 54
instance. T...
Figure 10.25 Matchings.
Figure 10.26 Tour generated by Christofides' heuristic for instance in Fig...
Figure 10.27 Tour obtained by applying Christofides' heuristic to
Car 54
i...
Figure 10.28 Optimal TSP tour on odd‐degree nodes in MST in Christofides' ...
Figure 10.29 Two possible ways to reconnect tour during 2‐opt exchange.
Figure 10.30 Tour obtained by applying 2‐opt heuristic to nearest‐neighbor...
Figure 10.31 Eight possible ways to reconnect tour during 3‐opt exchange....
Figure 10.32 Or‐opt exchanges.
Figure 10.33 Tour obtained by applying Or‐opt heuristic to nearest‐neighbo...
Figure 10.34 US Type I unstringing of node
k
.
Figure 10.35 US Type II unstringing of node
k
.
Figure 10.36 Minimum spanning tree for
Car 54
instance. Total distance = 9...
Figure 10.37 Optimal 1‐trees for
Car 54
instance.
Figure 10.38 1‐tree example.
Figure 10.39 Optimal 1‐tree for instance in Figure 10.5 with
. Resultin...
Figure 10.40 Optimal 1‐tree for instance in Figure 10.5 with
. Resultin...
Figure 10.41 Control zones.
Figure 10.42 Moats.
Figure 10.43 TSP world records.
Figure 10.44 Space‐filling curve heuristic.
Figure 10.45 TSP instance for Problems 10.1 and 10.2. Edges that are not pic...
Figure 10.46 TSP instance for Problems 10.3 and 10.4. Edges that are not pic...
Figure 10.47 TSP instance for Problems 10.5 and 10.6. Distances are Euclid...
Figure 10.48 TSP instance for Problem 10.16. Edges that are not pictured h...
Figure 10.49 TSP instance for Problem 10.17. Edges that are not pictured h...
Figure 10.50 TSP instance for Problem 10.32. Distances are Euclidean.
Figure 11.1 Optimal solution to
eil51
VRP instance. Total distance = 521...
Figure 11.2 Clarke–Wright savings heuristic.
Figure 11.3 VRP instance for examples. Distances are Euclidean.
Figure 11.4 Clarke–Wright savings heuristic for instance in Figure 11.1.
Figure 11.5 Clarke–Wright solution to
eil51
VRP instance. Total distance...
Figure 11.6 Sweep heuristic for instance in Figure 11.1.
Figure 11.7 Sweep heuristic solution to
eil51
VRP instance. Total dist...
Figure 11.8 Approximating the VRP (thick lines) by the CCLP (thin lines) i...
Figure 11.9 LBH solution for instance in Figure 11.1.
Figure 11.10 Feasible solution for CCLP in proof of Theorem 11.5.
Figure 11.11 Node exchange for VRP.
Figure 11.12 A few possible
‐interchange moves for VRP, with
. • = n...
Figure 11.13 Ejection chain move for VRP. • = node originally on route A, ...
Figure 11.14 A simple genetic algorithm for the UFLP. In (b) and (c), gene...
Figure 11.15 One‐point crossover for VRP leading to infeasible solution.
Figure 11.16 PMX‐based crossover operator (Van Breedam, 1996).
Figure 11.17 EAX‐based crossover operator (Nagata and Bräysy, 2009).
Figure 11.18 Figures for proof of Theorem 11.6.
Figure 11.19 Approximate lower and upper bounds on
vs.
n
for points in...
Figure 11.20 Asymptotic behavior of VRP solution as
.
Figure 11.21 VRP instance for Problem 11.1. Distances are Euclidean.
Figure 11.22 VRP instance for Problem 11.2. Distances are great circle dis...
Figure 11.23 VRP instance for Problem 11.4.
Figure 11.24 VRP instance for Problem 11.5.
Figure 12.1 Proof of Theorem 12.1(c). Filled circles represent
; open c...
Figure 12.2 Solution to problem
. Filled circles represent
; open ci...
Figure 12.3 Optimal solution to 88‐node LMRP instance. Total cost = $1,057...
Figure 12.4 UFLP‐based solution to 88‐node LMRP instance. Total cost = $1,...
Figure 12.5 3‐Node LMRP instance for Problem 12.5.
Figure 13.1 Increase in order variability in upstream supply chain stages....
Figure 13.2 Serial supply chain network.
Figure 13.3 BWE spreadsheet simulation.
Figure 13.4 BWE caused by price fluctuations.
Figure 13.5 Philips Optical Storage's DVD supply chain network.
Figure 13.6 One‐warehouse, multiple‐retailer system for Problem 13.2.
Figure 14.1 Supplier's expected profit function
for Example 14.1.
Figure 14.2 Wholesale price and profits as a function of buyback credit.
Figure 15.1 Bidders' valuations in double auction in Problem 15.6.
Figure 16.1 Two arcs with different capacities.
Figure 16.2 Exact and approximate
function for epidemiological model u...
Figure 16.3 Typical disaster‐relief supply chain.
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