Simchi-Levi D., Chen X., Bramel J. The Logic of Logistics: Theory, Algorithms, and Applications for Logistics and Supply Chain Management

Springer – 2005, 355 pages
ISBN: 0387221999Fierce competition in today's global market provides a powerful motivation for developing ever more sophisticated logistics systems. This book, written for the logistics manager and researcher, presents a survey of the modern theory and application of logistics. The goal of the book is to present the state-of-the-art in the science of logistics management. As a result, the authors have written a timely and authoritative survey of this field that many practitioners and researchers will find makes an invaluable companion to their work.What Is Logistics Management?
Managing Cost and Uncertainty
Examples
Modeling Logistics Problems
Logistics in Practice
Evaluation of Solution Techniques
Additional Topics
Book Overview
I Performance Analysis Techniques
Convexity and SupermodularityConvex Analysis
Convex Sets and Convex Functions
Continuity and Differentiability Properties
Characterization of Convex Functions
Convexity and Optimization
Supermodularity
Exercises
Worst-Case AnalysisThe Bin-Packing Problem
First-Fit and Best-Fit
First-Fit Decreasing and Best-Fit Decreasing
The Traveling Salesman Problem
AMinimum Spanning Tree Based Heuristic
The Nearest Insertion Heuristic
Christofides’ Heuristic
Local Search Heuristics
Exercises
Average-Case AnalysisThe Bin-Packing Problem
The Traveling Salesman Problem
Exercises
Mathematical Programming Based BoundsAn Asymptotically Tight Linear Program
Lagrangian Relaxation
Lagrangian Relaxation and the Traveling Salesman Problem
The -Tree Lower Bound
The -Tree Lower Bound and Lagrangian Relaxation
TheWorst-Case Effectiveness of the -tree Lower Bound
Exercises
II Inventory Models
Economic Lot Size Models with Constant DemandsThe Economic Lot SizeModel
The Finite HorizonModel
Power of Two Policies
Multi-Item InventoryModelsNotation and Assumptions
Worst-Case Analyses
A SingleWarehouseMulti-RetailerModelNotation and Assumptions
Exercises
Economic Lot Size Models with Varying Demands
TheWagner-WhitinModel
Models with Capacity Constraints
Multi-Item InventoryModels
Contents xiii
Exercises
Stochastic Inventory ModelsSingle PeriodModels
TheModel
Finite HorizonModels
Model Description
K-Convex Functions
Main Results
Quasiconvex Loss Functions
Infinite HorizonModels
Multi-Echelon Systems
Exercises
Integration of Inventory and PricingSingle PeriodModels
Finite HorizonModels
Model Description
Symmetric K-Convex Functions
Additive Demand Functions
General Demand Functions
Special Case: Zero Fixed Ordering Cost
Extensions and Challenges
Risk Averse InventoryModels
Expected utility risk averse models
Exponential utility risk averse models
Exercises
III Design and Coordination Models
Procurement ContractsWholesale Contracts
Buy Back Contracts
Revenue Sharing Contracts
Portfolio Contracts
Exercises
Supply Chain Planning ModelsThe Shipper Problem
The ShipperModel
A Set-Partitioning Approach
Structural Properties
Solution Procedure
Computational Results
Safety Stock Optimization
Exercises
Facility Location ModelsAn Algorithm for the p-Median Problem
An Algorithm for the Single-Source Capacitated Facility Location
Problem
A Distribution System Design Problem
The Structure of the Asymptotic Optimal Solution
Exercises
IV Vehicle Routing Models
The Capacitated VRP with Equal DemandsWorst-Case Analysis of Heuristics
The Asymptotic Optimal Solution Value
Asymptotically Optimal Heuristics
Exercises
The Capacitated VRP with Unequal DemandsHeuristics for the CVRP
Worst-Case Analysis of Heuristics
The Asymptotic Optimal Solution Value
A Lower Bound
An Upper Bound
Probabilistic Analysis of Classical Heuristics
A Lower Bound
The UOP(α) Heuristic
The UniformModel
The Location-Based Heuristic
Rate of Convergence to the Asymptotic Value
Exercises
The VRP with Time Window ConstraintsTheModel
The Asymptotic Optimal Solution Value
Contents xv
An Asymptotically Optimal Heuristic
The Location-Based Heuristic
A SolutionMethod for CVLPTW
Implementation
Numerical Study
Exercises
Solving the VRP Using a Column Generation ApproachSolving a Relaxation of the Set-Partitioning Formulation
Solving the Set-Partitioning Problem
Identifying Violated Clique Constraints
Identifying Violated Odd Hole Constraints
The Effectiveness of the Set-Partitioning Formulation
Motivation
Proof of Theorem
Exercises
V Logistics Algorithms in Practice
Network PlanningNetwork Design
Strategic Safety Stock
Resource AllocationExercises
A Case Study: School Bus RoutingThe Setting
Literature Review
The Problem in New York City
Distance and Time Estimation
The Routing Algorithm
Additional Constraints and Features
The InteractiveMode
Data, Implementation and Results