QUEUING THEORY INTRODUCTION Waiting lines are the most frequently encountered problems in everyday life. For example‚ queue at a cafeteria‚ library‚ bank‚ etc. Common to all of these cases are the arrivals of objects requiring service and the attendant delays when the service mechanism is busy. Waiting lines cannot be eliminated completely‚ but suitable techniques can be used to reduce the waiting time of an object in the system. A long waiting line may result in loss of customers to an organization
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Example 1: Does the curve y = 2x3 – 2 crosses the x-axis between x = 0 and x = 2? Solution: Solving for the given values of x‚ at x = 0‚ then y = 2(0)3 – 2 = -2‚ the curve is below the x-axis at x = 2‚ y = 2(2)3 – 2 = 16 – 2 = 14‚ the curve is above the x-axis‚ So the curve crosses the x-axis between x = 0 and x = 2 since y = 2x3 – 2 has a solution found between this interval. Example 2: From the curve y = x5 - 2x3 – 2‚ is there a solution between x = 1 and x = 2? Solution: Using the given
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REAL-TIME OBJECT DETECTION FOR "SMART" VEHICLES D.M. Gavrila V. Philomin Image Understanding Systems DaimlerChrysler Research Ulm 89081‚ Germany dariu.gavrila@DaimlerChrysler.com Computer Vision Laboratory University of Maryland College Park‚ MD 20742‚ U.S.A. vasi@cs.umd.edu ABSTRACT This paper presents an e cient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety
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and passes through the point (5; 1). 7. [2 marks] Consider the parabola given by y = 2x2 (a) Determine the x-intercepts of the parabola. 8x 1. (b) Determine the coordinates of the parabola’ vertex. s 4 8. [4 marks] Consider the functions f (x) = p x+3 and g (x) = x x2 4 3. (a) State the domain of f and the domain of g. (b) Determine f (2 + h) and simplify the resulting expression. (c) Determine (f g) (x) and then evaluate (f g) (12). 5 9. [6 marks] Solve
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planning‚ production planning and budgeting‚ cash budgeting‚ analyzing various operating plans. 6. There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. 7. Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8. MAD‚ MSE‚ and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model‚ forecast
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the SUM function to total the range B5:B10. Use AutoFill to fill the range C11:F11 using the formula you just created in cell B11. 7. Edit the text in cell A13 so that it reads Projected Admissions 2016-2019. 8. Enter the information shown in Table 1 below into the worksheet. Table 1 Values for range B20:E20 Cell Value B20 30277 C20 31791 D20 33380 E20 35049 9. Use AutoFill to fill the range F16:F20 using the formula in cell F15. 10. In cell B21‚ enter a formula that uses the SUM function to total
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facility location e. job assignments Forecasts are usually classified by time horizon into three categories a. short-range‚ medium-range‚ and long-range b. finance/accounting‚ marketing‚ and operations c. strategic‚ tactical‚ and operational d. exponential smoothing‚ regression‚ and time series e. departmental‚ organizational‚ and industrial A forecast with a time horizon of about 3 months to 3 years is typically called a a. long-range forecast b. medium-range forecast c. short-range forecast d. weather
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SCM 485 Exam 1 Review Forecast Notes Supply Chain Management Sequence of activities and organizations involved in producing and delivering a good or service SCM Define by Council of Supply Chain Management Professionals (CSCMP) Supply Chain Management encompasses the planning and management of all activity involved in sourcing and procurement‚ conversion‚ and all logistics management activities. Importantly‚ it also includes coordination and collaboration with channel partners‚ which can
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Having A Successful Relationship There’s several key “ingredients” and steps to a building and keeping successful relationship. You can skip a step or two at your own risk. Some steps you absolutely cannot omit. I’ll be discussing and describing each of these steps and elaborate on what will likely happen if you don’t follow directions. Successful relationships are mandatory in this lifetime. The first step‚ which you absolutely
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X is period) MAD ≈ 32.403 Exponential Smoothing or ES (adjusted for trend and seasonality) MAD ≈ 13.258 6 Q2: Forecast for January – April 2012 Month Mean Base Period MR SR ES January 0.61 49 825.27 745.12 720.56 February 0.88 50 1107.05 1082.68 1039.50 March 0.87 51 1105.66 1078.04 1027.69 April 1.05 52 1296.43 1310.32 1240.31 7 Q3: Best Forecast: Exponential smoothing forecast has lowest MAD Disadvantages: the exponential smoothing forecast should be updated
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