Benjamin Melamed

Benjamin Melamed is a Professor II in the Department of Management Science and Information Systems, Rutgers Business School–Newark and New Brunswick, a Research Fellow at the Rutgers Center for Supply Chain Management, and a Member of RUTCOR (Rutgers Center for Operations Research). Melamed received his B.S. degree in mathematics and statistics from Tel Aviv University in 1972, and his M.S. and Ph.D. degrees in computer science from the University of Michigan in 1973 and 1976, respectively. From 1977 to 1981 he taught in the Department of Industrial Engineering and Management Science at Northwestern University. He joined the Performance Analysis Department at Bell Laboratories in 1981, and later became an AT&T Fellow. Melamed moved to NEC in 1989 and served there as a Deputy Director, Head of the Performance Analysis Department, and NEC Fellow. He consulted at Bellcore in 1995 and joined Rutgers University in 1996. Melamed’s research interests include system modeling and analysis (especially supply chains and telecommunications systems), simulation, stochastic processes, and visual modeling environments. He authored or coauthored over 100 papers and coauthored two books: Modern Modeling and Simulation, with R. Rubinstein, (John Wiley and Sons, 1998) and Simulation Modeling and Analysis with Arena, with T. Altiok, (Cyber Research and Enterprise Technology Solutions, 2001). His research has been supported by DARPA and NSF. Melamed was awarded an AT&T Fellow in 1988 and an IEEE Fellow in 1994. He became an IFIP WG7.3 Member in 1997 and was elected to Beta Gamma Sigma in 1998.

Biography Updated on 22 January 2007

Articles in Scholarly Journals [Incomplete List]

  1. Simulation of IPA gradients in hybrid network systems
    Computers & Mathematics with Applications, vol. 54, no. 2, pp. 161–182, 2007
  2. Stopping Problems of Certain Multiplicative Functionals and Optimal Investment with Transaction Costs
    Applied Mathematics and Optimization, vol. 55, no. 3, pp. 359–384, 2006
  3. Deferred Assignment Scheduling in Cluster-Based Servers
    Cluster Computing, vol. 9, no. 1, pp. 57–65, 2006
  4. IPA Derivatives for Make-to-Stock Production-Inventory Systems with Backorders
    Methodology and Computing in Applied Probability, vol. 8, no. 2, pp. 191–222, 2006
  5. Mean Waiting Time Approximations in the G/G/1 Queue
    Queueing Systems, vol. 46, no. 3/4, pp. 481–506, 2004
  6. HNS
    ACM Transactions on Modeling and Computer Simulation, vol. 14, no. 3, pp. 251–277, 2004
  7. Methodology And Computing In Applied Probability, vol. 5, no. 2, pp. 159–181, 2003
  8. Journal of Optimization Theory and Applications, vol. 115, no. 2, pp. 369–405, 2002
  9. Perturbation analysis for online control and optimization of stochastic fluid models
    IEEE Transactions on Automatic Control, vol. 47, no. 8, pp. 1234–1248, 2002
  10. Modeling financial time series using ARM processes
    Nonlinear Analysis, vol. 47, no. 3, pp. 2035–2048, 2001
  11. IIE Transactions, vol. 33, no. 9, pp. 779–791, 2001
  12. Discrete Event Dynamic Systems, vol. 11, no. 3, pp. 249–282, 2001
  13. The effect of extrinsic motivation on user behavior in a collaborative information finding system
    Journal of the American Society for Information Science and Technology, vol. 52, no. 11, pp. 879–887, 2001
  14. Capturing human intelligence in the net
    Communications of the ACM, vol. 43, no. 8, pp. 112–115, 2000
  15. Arm processes and modeling methodology
    Stochastic Models, vol. 15, no. 5, pp. 903–929, 1999
  16. Modeling full-length VBR video using Markov-renewal-modulated TES models
    IEEE Journal on Selected Areas in Communications, vol. 16, no. 5, pp. 600–611, 1998
  17. Analysis of a control mechanism for a variable speed processor
    IEEE Transactions on Computers, vol. 45, no. 7, pp. 793–801, 1996
  18. The QTES/PH/1 queue
    Performance Evaluation, vol. 26, no. 1, pp. 1–20, 1996
  19. Algorithmic modeling of TES processes
    IEEE Transactions on Automatic Control, vol. 40, no. 7, pp. 1305–1312, 1995
  20. A Survey of TES Modeling Applications
    SIMULATION, vol. 64, no. 6, pp. 353–370, 1995
  21. Regenerative simulation of TES processes
    Acta Applicandae Mathematicae, vol. 34, no. 1-2, pp. 237–260, 1994
  22. TES-based video source modeling for performance evaluation of integrated networks
    IEEE Transactions on Communications, vol. 42, no. 10, pp. 2773–2777, 1994
  23. TES modeling for analysis of a video multiplexer
    Performance Evaluation, vol. 16, no. 1-3, pp. 21–34, 1992
  24. Numerical Computation of Sojourn-Time Distributions in Queuing Networks
    Journal of the ACM, vol. 31, no. 4, pp. 839–854, 1984
  25. On the reversibility of queueing networks
    Stochastic Processes and their Applications, vol. 13, no. 2, pp. 227–234, 1982
  26. On Poisson Traffic Processes in Discrete-State Markovian Systems with Applications to Queueing Theory
    Advances in Applied Probability, vol. 11, no. 1, p. 218, 1979
  27. Characterizations of Poisson Traffic Streams in Jackson Queueing Networks
    Advances in Applied Probability, vol. 11, no. 2, p. 422, 1979