Management Technology IPEDR vol.13 (2011) © (2011) IACSIT Press‚ Singapore Optimal Scheduling in a Milk Production Line Based on Mixed Integer Linear Programming Habibollah Javanmard1‚+ ‚ Kianehkandi2 1‚+ Assistant professor‚ Islamic Azad University‚ Arak Branch‚ Arak‚ Iran 2 Islamic Azad university ‚ Arak Branch‚ Arak‚ Iran Abstract - A Mixed Integer Linear Programming (MILP) model is proposed in this paper that targets the optimal production scheduling in a single milk production line. The model
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CHAPTER 1 INTRODUCTION 1.1 INTRODUCTION Transport along with communication‚ forms the core of day to day life of modern world. Conventional rail transport through wide spread is now being considered inefficient in terms of fuel consumption and is time consuming. A genuine replacement for railways which is not only fuel efficient but also highly comfortable and can attain unimaginable velocities of around 450 – 500kms/hr are Maglev Trains whose idea was given by Robert Goddard‚ an American
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loft angle and linear velocity From the Figure 7‚ there is a negative relationship between the loft angle of golf club and the linear velocity of golf ball. Which means as the loft angle increases‚ the linear velocity of ball decreases. The golf ball travels with relatively fast velocity at 9.2ms-1‚ when the loft angle is 0 degree‚ but it travels relatively slower at 3.7ms-1 when the loft angle is 80 degree. Also‚ the shape of graph seems to be a non linear graph rather than linear graph‚ because
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This paper is submitted by Anupam Basu LAB (Linear Alkyl Benzene):- For more than 30 years‚ UOP has been a leading innovator in the production of LAB. During that time‚ we’ve developed the state-of-the-art technology you’ll need to ensure that your new or revamped LAB complex operates most efficiently and produces the highest quality product.LAB is the primary raw material used in the production of linear alkyl benzene sulfonate (LAS). LAB is produced from benzene and long chain normal paraffins
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01: Introduction to linear control systems Definition 1 (Control system). an interconnection of subsystems and processes forming a configuration that will lead to the desired output‚ given a specified input. Desired output Reference input Control system plant reference input Actual output system response controlled variable The control system manipulates the input of the process to get the desired response 2 EE352: Lecture 01: Introduction to linear control systems
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NC. It’s a resort town in the Blue Ridge Mountains. The attractions here are mostly outdoor activities taking place in the secluded wilderness. The population is only about 1500 and the average cost of a house from my data is $485‚839.50. For my linear regression‚ I am modeling the relationship between the price of homes‚ being my dependent variable‚ and some characteristics of the homes‚ being my explanatory variables. Originally my data consisted of the following for real estate in Blowing Rock
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Chapter 2: Linear Functions Chapter one was a window that gave us a peek into the entire course. Our goal was to understand the basic structure of functions and function notation‚ the toolkit functions‚ domain and range‚ how to recognize and understand composition and transformations of functions and how to understand and utilize inverse functions. With these basic components in hand we will further research the specific details and intricacies of each type of function in our toolkit and use
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Project 1: Linear Correlation and Regression Analysis Gross Revenue and TV advertising: Pfizer Inc‚ along with other pharmaceutical companies‚ has begun investing more promotion dollars into television advertising. Data collected over a two year period‚ shows the amount of money Pfizer spent on television advertising and the revenue generated‚ all on a monthly bases. |Month |TV advertising |Gross Revenue | |1 |17 |4.1 | |2
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= −2 1 5 1 0 1 0 1 0 2 7 2 −3 2 1 0 1 −2 11 −2 21 −2 −7 12 −7 −3x2 = 21 x1 + 2 x2 = −2 x2 = −7 x1 = 12 x2 = −7 1 2 CHAPTER 1 • Linear Equations in Linear Algebra 3. The point of intersection satisfies the system of two linear equations: x1 + 5 x2 = 7 x1 − 2 x2 = −2 1 1 5 −2 7 −2 x1 + 5 x2 = 7 Replace R2 by R2 + (–1)R1 and obtain: Scale R2 by –1/7: Replace R1 by R1 + (–5)R2: The point of intersection is
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This article considers the relationship between two variables in two ways: (1) by using regression analysis and (2) by computing the correlation coefficient. By using the regression model‚ we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example‚ an economist can estimate the amount of change in food expenditure due to a certain change in the income of a household by using the regression model. A sociologist may want to estimate the increase
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