Benjamin Melamed
Distinguished Professor & Program Director, Rutgers Stackable Business Innovation (rSBI) Program
Distinguished Professor & Program Director, Rutgers Stackable Business Innovation (rSBI) Program
Benjamin Melamed (Ph.D., Computer and Communication Sciences, University of Michigan, 1976) is a Distinguished Professor at the Department of Supply Chain Management, Rutgers Business School—Newark and New Brunswick (RBS), Program Director of the Rutgers Stackable Business Innovation (rSBI) program, former Senior Associate Dean for Strategic Planning and Implementation—New Brunswick at RBS, and former Director of the PhD Program in Management at RBS.
Melamed’s research interests include
Melamed authored or co-authored over 120 papers and co-authored two books. He was awarded an AT&T Fellow in 1988 and an IEEE Fellow in 1994.
Ph.D., University of Michigan; Computer and Communications Sciences
Ph.D. Research Areas
My current broad research area with PhD students is supply chain management. This work calls for students with strong analytical and computer skills. I am interested in modeling, simulation and analysis of production-inventory systems, including the following aspects:
Publications with PhD Students and Alumni
B. Melamed, S. Pan and Y. Wardi, “Hybrid Discrete-Continuous Fluid-Flow Simulation”, Proc. of the SPIE International Symposium on Information Technologies and Communications (ITCOM 01), Scalability and Traffic Control in IP Networks, 263--270, Denver, Colorado, August 22-24, 2001.
B. Melamed, S. Pan and Y. Wardi, “HNS: A Streamlined Hybrid Network Simulator”, ACM Transactions on Modeling and Computer Simulation (TOMACS), Vol. 14, No. 3, 1-27, 2004.
B. Melamed and S. Singh, “Parallelization Algorithms for Modeling ARM Processes”, J. of Applied Mathematics and Stochastic Analysis, Vol. 13, No. 4, 393--410, 2000.
D. Jagerman, A. Altiok, B. Melamed, and B. Balcioglu, “Mean Waiting Time Approximations in the G/G/1 Queue”, QUESTA, Vol. 46, 481-506, 2004.
Dissertations Supervised:
Name: S. Pan
Rutgers Center for Operations Research (RUTCOR)
Graduation Date: 2005
Thesis Title: "Hybrid Network Simulation"
Name: Shi, Junmin
Rutgers Business School, Supply Chain Management
Graduation Date: 2010/October
Thesis Title: Make-to-Stock Production-Inventory Systems with Compound Poisson Demands, Constant Continuous Replenishment and Lost Sales.
Postdoctoral Research Supervised:
S. Singh, "Parallelization Algorithms for Modeling QTES Processes", postdoc supervisor, Rutgers Business School, Rutgers University, 1998-2000.
Early Summer Research Projects of Current PhD Students:
Name: Dinesh Pai
Project Title: The Impact of RFID on the Security and Integrity of the US Pharmaceutical Supply Chain
The complexity of US pharmaceutical supply chains is increasing rapidly. Demographic changes, growing Internet pharmacies, counterfeit drugs, product diversion and the issues of drug importation and re-importation have added to this complexity. Growing cases of counterfeit drugs and product diversion have posed a serious challenge to drug manufacturers as well as other supply chain partners striving to ensure that the end patient receives authentic product. These have served to raise American awareness of the problem.
The FDA Counterfeit Drug Task Force has recommended a combination of rapidly improving track-and-trace and product authentication technologies to protect the US pharmaceutical supply chain. RFID technology, combined with recent AutoID initiatives led by MIT, is gaining momentum. RFID provides visibility of products along the supply chain, and accurate and timely information, which will help detect counterfeit drugs in the supply chain.
This paper overviews RFID technology and describes how RFID technology could help improve the security and integrity of the drug supply chain. A special section focuses on the concerns of drug manufacturers as well as other supply chain partners regarding the implementation of this technology.
General Research
Yihong Fang: IPA derivatives is make-to-stock systems
Junmin Shi: ARM (Autoregressive Modular) process forecasting of financial time series.