TECHNIQUES ----------------------------------------------------------------------------------------- 2.1 INFORMED SEARCH AND EXPLORATION 2.1.1 Informed(Heuristic) Search Strategies 2.1.2 Heuristic Functions 2.1.3 Local Search Algorithms and Optimization Problems 2.1.4 Local Search in Continuous Spaces 2.1.5 Online Search Agents and Unknown Environments ----------------------------------------------------------------------------------------------------------------------- 2.2 CONSTRAINT
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Abstracts and Keywords List for each chapter for the Wiley new book: System Simulation Techniques with MATLAB and Simulink Dingyü Xue‚ YangQuan Chen ISBN: 978-1-118-64792-9 Hardcover 488 pages Chapter-01 Introduction to System Simulation Techniques and Applications Abstract: This introductory chapter presents a concise overview of system simulation techniques and developments of simulation software including some historical early simulation softwares and programs. Then‚ MATLAB history and characteristics
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Optimization Note Examples 1. Farmer McDonald has 800 m of fencing and wishes to enclose a rectangular field. One side of the field is against a river and does not need fencing. Find the dimensions of the field if the fenced area is to be a maximum. A county fair has a holding area for prize sheep that are entered in a contest. The holding area is made up of 12 identical pens arranged in a two by six grid. If 100 m of fencing is available‚ what dimensions of each pen would maximize the total holding
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number of edges. Raghavan mentioned that 95% of nodes or more are classified correctly by the end of iteration 5 [14]. So it is applicable to large-scale networks due to this simple local and decentralized process. In addition‚ neither does LPA need optimization of the predefined objective function nor does it need priori information on numbers and scales of the community. Furthermore‚ there is no limit on the size of the community and the division effect is very ideal. So LPA has become one of the most
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Society 2003;54(12):1283–95. [35] Donaldson H‚ Johnson EL‚ Ratliff‚ HD‚ Zhang M. Schedule-driven crossdocking networks. Technical report 9904‚ Atlanta‚ GA‚ USA: Georgia Institute of Technology; 1999. [36] Musa R‚ Arnaout JP‚ Jung H. Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering 2010;59(1):85–92. [37] Sung CS‚ Yang W. An exact algorithm for a cross-docking supply chain network design problem. Journal of the Operational
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Vol. 41‚ No. 6‚ November–December 2011‚ pp. 534–547 issn 0092-2102 eissn 1526-551X 11 4106 0534 http://dx.doi.org/10.1287/inte.1110.0544 © 2011 INFORMS Designing New Electoral Districts for the City of Edmonton Burcin Bozkaya Sabanci School of Management‚ Sabanci University‚ Orhanlı-Tuzla‚ 34956 Istanbul‚ Turkey‚ bbozkaya@sabanciuniv.edu Erhan Erkut Ozyegin University‚ 34662 Istanbul‚ Turkey‚ erhan.erkut@ozyegin.edu.tr Dan Haight Centre for Excellence in Operations‚ University of
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produce. To specify the objective function‚ we need to be Lecture 2 Shelby Shelving Case Understanding the optimizer sensitivity report ! Dual prices ! Righthand side ranges ! Objective coefficient ranges If time permits: Distribution / Network Optimization Models Summary and Preparation for next class LX ) Net profit 39S 55LX 400 and 1 So for the current production plan of S 1400‚ we get Net profit = $61‚400. Selling Price Standard cost Profit contribution able to compute net profit for any production
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1. Locating fire protection services: the location set covering model One problem faced by many communities is where to locate fire stations. Even though most people do not want to live next to a station‚ they do want fire protection services nearby. The value of fire services is time critical. If a crew at a station takes more than 10 minutes to reach a house fire‚ there are significant chances that the fire will consume major portions of the house and threaten occupants. Most fires can be easily
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Mathematical Programming: An Overview 1 Management science is characterized by a scientific approach to managerial decision making. It attempts to apply mathematical methods and the capabilities of modern computers to the difficult and unstructured problems confronting modern managers. It is a young and novel discipline. Although its roots can be traced back to problems posed by early civilizations‚ it was not until World War II that it became identified as a respectable and well defined body of
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