OR 350 Projects 1998. 24 Hours Operations Research - Evacuation planning. Shortest dipath problem In this section we present the solution approaches developed for discrete time dynamic network flow problem and the maximum dynamic flow problem.
We start with some concepts and well known problems from the graph theory and network flows. Our approach is to demonstrate simple examples to introduce the algorithms. For the theoretical work we refer to the references listed in the additional information. The directed graph shown below represents an evacuation area consists of rooms and corridors. The rooms and corridors are modelled as nodes and arcs respectively. Residual networks and augmenting paths Having introduced the shortest dipath problem, we consider the case that there is more than one evacuee in room 1 (source node) and we try to send as much evacuees as possible to the safety exit (sink node) without exceeding the capacities of the corridors.
Maximum flow problems can be solved by using several algorithms. Maximum dynamic flow problem. Science: Math: Operations Research. EE392o: Optimization Projects. Stanford University, Autumn Quarter 2003-2004 Professor Stephen Boyd and Professor Z.
-Q. Luo In 2006-07, EE392 was turned into a permanent course, EE364b: Convex Optimization II. Lecture topics and notes Introduction. Top of EE392o page Project topics Project presentation schedule (Tentative) Dec. 4th, Thursday: 1:00-1:30 pm: Vivek Farias (Scheduling Projects with Shared Resources) 1:30-2:00 pm: Ritesh Madan (A Distributed Algorithm for Maximum Lifetime Routing in Ad Hoc Wireless Networks)2:00-2:30 pm: Erik Stauffer (Maximizing Outage Capacity)2:30-3:00 pm: Unscheduled Dec. 11th, Thursday, Session 1 (Packard 277): Dec. 11th, Thursday, Session 2 (Packard 277): Dec. 12th, Friday, Session 1 (Packard 277) : Dec. 11th, Friday, Session 2 (Packard 277):
EE392o: Convex Optimization Links. Research homepages Books Software the SDP homepages of Christoph Helmberg, Henry Wolkowicz and Farid AlizadehLMITOOL: a Matlab interface for solving LMI problemsLMI control toolbox: a Matlab toolbox for solving LMI problems arising in controlSDPT3: a Matlab software package for semidefinite programming, by K.C. Toh, M.J. Todd, and R.H. Interactive guides/Case Studies Various Daniel Palomar's PhD oral defense slides. ( pdf)Daniel Palomar, Optimum Linear Joint Transmit-Receive Processing for MIMO Channels with QoS Constraints. ( pdf)Daniel Palomar, Joint Tx-Rx Beamforming Design for Multicarrier MIMO Channels: A Unified Framework for Convex Optimization ( pdf)Daniel Palomar, A Unified Framework for Communications through MIMO Channels ( pdf)Journal of Convex AnalysisRobust Convex Optimization.A.
Optimization - Projects. A A A English Projektsite Zur Navigation springen|Zum Inhalt springen Home » Forschung » Diskrete Mathematik » Optimierung Forschung Quicklinks Dokumentenserver Quickview.
GA Playground - Java Genetic Algorithms Toolkit. A general GA toolkit implemented in Java, for experimenting with genetic algorithms and handling optimization problems Contents The GAA Applet/Application Examples and Test Problems Overview The GA Playground is a general purpose genetic algorithm toolkit where the user can define and run his own optimization problems.
The GA Playground is primarily designed to be used as an application and not as an applet, since it requires re-compiling of at least one class and use of local file I/O. Browser Requirements and Loading Times The applet is written in JDK 1.1.5 and uses the new event model. Updated info (2003): The program requires JDK between 1.1.5 and 1.4 (it will not run under JDK 1.4 or higher).
The applet is large and takes a relatively long time to load. General Notes Alphabet The implementation of the genetic algorithm uses a high alphabet to encode the chromosome's genes. Problem Definition - Definition Files Problem Definition - Source Modifications Special GA Mechanisms Graphic Display.