The Supply Chain in Manufacturing, Distribution, and Transportation Modeling, Optimization, and Applications

Reporting on cutting-edge research in production, distribution, and transportation, The Supply Chain in Manufacturing, Distribution, and Transportation: Modeling, Optimization, and Applicationsprovides 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.

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.

Kenneth D. Lawrence, Ph.D., is a professor of Management and Marketing Science and Decision Support Systems in the School of Management at the New Jersey Institute of Technology. His professional employment includes over 20 years of technical management experience with AT&T as Director, Decision Support Systems and Marketing Demand Analysis; Hoffmann-La Roche, Inc.; Prudential Insurance; and the U.S. Army in forecasting, marketing planning and research, statistical analysis, and operations research. He is a full member of the Graduate Doctoral Faculty of Management at Rutgers, the State University of New Jersey, in the Department of Management Science and Information Systems. Lawrence has served as the doctoral chairman and thesis advisor for four Rutgers doctoral students. He is a member of the graduate faculty at NJIT in management, transportation, statistics, and industrial engineering. He is also an active participant in professional associations, including the Decision Sciences Institute, Institute of Management Science, Institute of Industrial Engineers, American Statistical Association, and Institute of Forecasters. He has conducted significant funded research projects in health care and transportation.Lawrence is the Associate Editor of the Journal of Statistical Computation and Simulation and the Review of Quantitative Finance and Accounting, and also serves on the editorial boards of Computers and Operations Research and the Journal of Operations Management. His research work has been cited hundreds of times in 74 different journals, including Computers and Operations Research, International Journal of Forecasting, Journal of Marketing, Sloan Management Review, Management Science, Sloan Management Review, Technometrics, Applied Statistics, Interfaces, International Journal of Physical Distribution and Logistics, and the Journal of the Academy of Mar