Efficient Simulation Algorithms For Optimization Of Discrete Event Systems Based On Measure Valued Differentation

Efficient simulation algorithms for optimization of discrete event systems based on measure-valued differentation

Network Control and Optimization

We are proud to present the proceedings of NET-COOP 2009, the inter- tionalconferenceonnetworkcontrolandoptimization,co-organizedbyEURAN- DOM/Eindhoven University of Technology and CWI. This year’s conference at EURANDOM, held November 23–25, was the third in line after previous e- tions in Avignon (2007) and Paris (2008). NET-COOP 2009 was organized in conjunction with the Euro-NF workshop on “New Trends in Modeling, Quan- tative Methods, and Measurements. ” While organized within the framework of Euro-NF, NET-COOP enjoys great interest beyond Euro-NF, as is attested by the geographic origins of the papers in these proceedings. TheNET-COOPconferencefocusesonperformanceanalysis,controland- timization of communication networks, including wired networks, wireless n- works, peer to peer networks and delay tolerant networks. In each of these domains network operators and service providers face the challenging task to e?ciently provide service at their customer’s standards in a highly dynamic - vironment. Internet tra?c continues to grow tremendously in terms of volume as well as diversity. This development is fueled by the increasing availability of high-bandwidth access (both wired and wireless) to end users, opening new ground for evolving and newly emerging wide-band applications. The increase in network complexity, as well as the plurality of parties involved in network operation, calls for e?cient distributed control. New models and techniques for the control and optimization of networks are needed to address the challenge of allocating communication resources e?ciently and fairly, while accounting for non-cooperative behavior.

Stochastic Simulation Optimization for Discrete Event Systems

Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a hard nut to crack. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions.