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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8472卷 166-174页

作者： Chamoret, Dominique Salmon, SáEbastien Cesare, Noelie Di Xu, Yingjie J IRTES-M3M-EA7274 Universitée de Technologie de Belfort Montbáeliard Belfort France Optimization Command and Control Systems Besançcon France Engineering Simulation and Aerospace Computing Key Laboratory of Contemporary Design and Integrated Manufacturing Technology Northwestern Polytechnical University Xi’an710072 China

The thermal residual stresses (TRS) induced in ceramic matrix composites (CMCs) with multi-layered interphases when cooling down from the processing temperature, have a significant influence on the mechanical behavior and lifetime of CMCs. The objective of this work is to minimize the TRS of the unidirectional CMCs with multilayered interphases by controlling the interphases thicknesses. A new Particle Swarm optimization (PSO) algorithm is interfaced with a finite element code to find an optimal design and thereby significantly reduce the TRS within CMCs. This new PSO allows a faster convergence rate and gets a new effective stopping criteria based on real physical limits. © 2014 Springer International Publishing Switzerland. All Rights Reserved.

关键词： Finite element method

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Implementation of nrmo to study the airlift problem at amc 7th

Implementation of nrmo to study the airlift problem at amc

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7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and optimization, 1998

作者： Rink, Katherine Ann Matheu, Amanda L. Rodin, Ervin Y. Whisman, Alan Massoud Amin, S. Center for Optimization and Semantic Control Department of Systems Science and Mathematics Washington University Campus Box 1040 St. LouisMO63130-4899 United States United States Air Force Air Mobility Command HQ AMC/XPY Scott Air Force Base IL62225-5307 United States 3412 Hill view Avenue Palo AltoCA94304-1395 United States

The airlift problem is a complex transportation problem. The objective is to fit the maximum amount of cargo efficiently on a number of aircraft with different capabilities and transport it from one region to another in the shortest time possible. The available resources and parameters to be modeled within this problem are described. This problem has typically been solved at the Air Mobility command (AMC) using a simulation tool, AFM (the Airlift Flow Model). NRMO (the NPS/RAND Mobility Optimizer) is a linear optimization model developed by the Naval Postgraduate School and the RAND Corporation for the solution of this problem. Some crucial decision variables and constraints are discussed, as well as how it has been used in airlift analysis at AMC. Finally, analysis of some alternate objective functions is presented. The results produced by NRMO not only provided meaningful insights to airlift operations, but also called into question some assumptions used by AFM. In our experience, NRMO is a solution methodology which can provide quick, invaluable answers to crucial airlift mobility planning questions. These answers can be applied to aid in scheduling and other analyses. © 1998 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Linear programming

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Object oriented modeling of the strategic brigade airdrop operation (WIP) 7th

Object oriented modeling of the strategic brigade airdrop op...

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7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and optimization, 1998

作者： Grindey, Gregory J. Rodin, Ervin Y. Perry, Hans J. Massoud Amin, S. Center for Optimization and Semantic Control Dept. Of Systems Science and Mathematics Washington University Campus Box 1040 St. LouisMO63130-4899 United States Studies and Analysis Flight Air Mobility Command Scott Air Force Base IL62225-5307 United States 3412 Hill view Avenue Palo AltoCA4304-1395 United States Mobility Analyst United States Air Force United States

The Strategic Brigade Airdrop operation is a large-scale strategic airlift operation in which the United States Air Force moves a brigade-sized force of United States Army troops and their equipment to a remote airfield located virtually anywhere in the world. To better study the decision-making process, an object-oriented simulation model has been created that captures the essential dynamics of the process. The decision-making rules used in this initial simulation are those heuristic rules developed by planners within the Air Mobility command, the division inside the Air Force responsible for the operation. An important question is whether these rules are the best way to accomplish the mission or not, and if not, how can the decisions inside the operation be made more efficiently to improve the probability of mission success. To that end an additional component to the simulation is being added which will make the decisions based on experience gained over previous runs of the simulation through the use of a cost functional analysis that assigns costs and benefits to the results of various decisions. Over time the algorithm should converge to a solution that minimizes this cost functional and presumably make the best set of decisions for this stochastic dynamic problem. © 1998 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Military aviation

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PUTTING AN OIL-SPILL CLEANUP COMPUTER-MODEL TO WORK FOR THE NAVY

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NAVAL ENGINEERS JOURNAL 1983年第3期95卷 165-172页

作者： NYHART, JD PSARAFTIS, HN YAROSCHAK, PJ Mr. J. D. Nyhart:is professor of management at the Sloan School of Management and the Department of Ocean Engineering at the Massachusetts Institute of Technology. His current research and writing focus on the use of scientific and technical material in formal judicial and administrative proceedings as well as the development of economic and regulatory models appropriate for deep ocean mining and oil-spill control. Harilaos N. Psaraftis:is Assistant Professor of Marine Systems at the Department of Ocean Engineering at the Operations Research Center at M.I.T. Professor Psaraftis received two M.Sc. degrees in 1977 (in Naval Architecture and Marine Engineering and in Shipping and Shipbuilding Management) and a Ph.D. in 1979 (in Ocean Systems Operations Research) all from M.I.T. Professor Psaraftis has been conducting research in problem areas such as the probabilistic modeling of underwater detection and optimal sensor allocation (project sponsored by ONR) the optimization of oil spill cleanup operations (project sponsored by a consortium of government and industry organizations) the development of routing and scheduling algorithms in transportation problems (project sponsored by DOT) and the analysis and solution algorithms of sealift routing and scheduling problems (project sponsored by ONR and the Military Sealift Command). Professor Psaraftis has published in various journals and is currently the Chairman of the Ocean Systems Management Program at M.I.T. Mr. Paul J. Yaroschak:received his Bachelors Degree in Civil Engineering from Villanova University and his Masters Degree in Environmental Engineering from Northeastern University. He has served in the Civil Engineer Corps U.S. Navy in Public Works and Construction Management and as a Project Engineer for Navy-wide Water and Wastewater Treatment Projects for the Naval Facilities Engineering Command. He is currently Head of the Environmental Engineering Branch of the Naval Facilities Engineering Command. Mr. Yaroschak is a Registered Professional Engineer i

A research group at the Massachusetts Institute of Technology has completed the first phase of the development of a computer assisted model for analyzing complex decisions and policies regarding oil spill cleanup. The model is the product of an ongoing MIT Sea Grant project, sponsored by a consortium of government and industry organizations, including the National Oceanic and Atmospheric Administration, the U.S. C oast G uard , the U.S. N avy , the Commonwealth of Massachusetts, the Spill control Association of America, JFB Scientific Corporation, the Doherty Foundation, Petro-Canada and Texaco. The model can be used, among other things, in strategic planning for the long-term oil spill response needs of a region, in assisting On Scene Coordinators in responding to a specific spill (tactical/operational setting), in evaluating the environmental and economic damages of a spill versus the cost of cleanup, in simulation and training, and in the analysis of various policy and regulatory issues such as the effects of delays, the use of dispersants and the investigation of liability and compensation issues. The paper describes the model in detail, focuses on its potential uses and presents experience with its application in conjunction with pollution control efforts of the U.S. Navy. Specifically, we outline the application of the model in the Port of Charleston, South Carolina, an ongoing project sponsored by the Naval Facilities Engineering command. The difficulty of gathering data for such an application is discussed.

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