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Full Description
Reporting on cutting-edge research in production, distribution, and transportation, The Supply Chain in Manufacturing, Distribution, and Transportation: Modeling, Optimization, and Applications provides the understanding needed to tackle key problems within the supply chain. Viewing the supply chain as an integrated process with regard to tactical and operational planning, it details models to help you address the wide range of organizational issues that can adversely affect your supply chain.
This compilation of scholarly research work from academia and industry considers high-level production schedules, product sourcing, network alignment, distribution center layouts, transportation operations with stochastic demand, inventory planning, and day-to-day operations planning. The book is divided into three sections:
Industrial and Service Applications of the Supply Chain
Analytic Probabilistic Models in Supply Chain Problems
Optimization Models of Supply Chain Problems
Because tactical and operational models rely on quality forecasts of demand, the text examines stochastic customer demand, coordination of supply chain functions, and solution algorithms. It reviews real-world business applications and case studies that illustrate the modeling solutions discussed.
Contents
Analytic Probabilistic Models of Supply Chain Problems. A Determination of the Optimal Level of Collaboration between a Contractor and Its Suppliers under Demand Uncertainty. Online Auction Models and Their Impact on Sourcing and Supply Management. Analytical Models for Integrating Supplier Selection and Inventory Decisions. Inventory Optimization of Small Business Supply Chains with Stochastic Demand. Optimization Models of Supply Chain Problems. A Dynamic Programming Approach to the Stochastic Truckload Routing Problem. Modeling Data Envelopment Analysis (DEA) Efficient Location/Allocation Decisions. Sourcing Models for End-of-Use Products in a Closed-Loop Supply Chain. A Bi-Objective Supply Chain Scheduling. Applying Data Envelopment Analysis and Multiple Objective Data Envelopment Analysis to Identify Successful Pharmaceutical Companies.
