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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Optimization Processes Network Optimization process involves the following activities: • FIRST SET THE CRITERION (GOAL) OF OPTIMIZATION PROCESS o BASELINE & TARGET KPI’s. o DELIVERABLES • • • • • CONDUCTING A BASELINE PHYSICAL AUDIT REMOVING ALL SERVICE AFFECTING ALARMS IDENTIFYING POOR COVERAGE AREAS IDENTIFYING CAPACITY CONSTRAINTS & OVERUTILIZED CELLS VARIOUS KPIs with Root-Cause-Analysis of problems. o o Frequency Plan (BCCH & TCH) Neighbor plan • • CONDUCTING A GSM SYSTEM
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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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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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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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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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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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Explain how the applications of integer programming differ from those of linear programming. Integer programming is concerned with optimization problems in which some of the variables are required to take on discrete values. Rather than allow a variable to assume all real values in a given range‚ only predetermined discrete values within the range are permitted. In most cases‚ these values are the integers‚ giving rise to the name of this class of models. Models with integer variables are very
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MSL Horns Case Observations: 1. The process is unorganized and not well laid out. Production is based on customer orders. Demand & Forecasting tools are not being used. 2. Manpower utilization is not optimal. Task specialization is not the buzzword here. Everybody does everything. 3. Material management as seen from the video was unorganized and cluttered. The assembly parts were haphazardly laid out. This obviously leads to poor material management and pilferage. 4. Consistency
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