<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Siddarth Misra</style></author><author><style face="normal" font="default" size="100%">Jayendran Venkateswaran</style></author><author><style face="normal" font="default" size="100%">Young-Jun Son</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Framework for Adaptive Time Synchronization Method for Integration of Distributed, Heterogeneous, Supply Chain Simulations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of The American Society of Engineering Management Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October 15-18</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ieor.iitb.ac.in/files/faculty/jayendran/2003_ASEM.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">St Louis, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In forming a federation of distributed simulations, their simulation clocks must be synchronized to ensure that events in each simulation are executed correctly, resulting in correct simulation results. We first discuss three time synchronization methods: 1) conservative, 2) optimistic, and 3) scaled real-time. To overcome problems of each of these static approaches, we propose a neural network based adaptive approach, which will react to the dynamic federation environment. In this paper, the framework of the neural network is discussed, and partial experimental results conducted for a distributed supply chain simulation are investigated which will be used as input to train the neural network.&lt;/p&gt;</style></abstract></record></records></xml>