...ID | Batch | Md. Al-Mamun Riyadh | 61427-20-079 | 20th | Abdullah-Al-Kashem | 61427-20-006 | 20th | Submission date: 31st August, 2014 Management Science Management Science is concerned with developing and applying models and concepts that help to clarify management issues and solve managerial problems. The models used can often be represented mathematically, but sometimes computer-based, visual or verbal representations are used. The range of problems and issues to which management science has contributed insights and solutions is vast. It includes scheduling airlines, both planes and crew, deciding the appropriate place to site new facilities such as a warehouse or factory, managing the flow of water from reservoirs, identifying possible future development paths for parts of the telecommunications industry, establishing the information needs and appropriate systems to supply them within the health service, and identifying and understanding the strategies adopted by companies for their information systems. Scientific Planning Successful management relies on careful coordination, often using scientific methods in project planning. For example, critical path analysis allows us to identify which tasks in a project will take the longest or adversely affect the length of other tasks, permitting us to focus on those tasks. Computer models can also help we determine utilization and recommend more effective usage. In addition, this type analysis...
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...OPERATION RESEARCH Credits: 4 SYLLABUS Development Definition, Characteristics and phase of Scientific Method, Types of models. General methods for solving operations research models. Allocation: Introduction to linear programming formulation, graphical solution, Simplex ethod, artificial variable technique, Duality principle. Sensitivity analysis. Transportation Problem Formulation optimal solution. Unbalanced transportation problems, Degeneracy. Assignment problem, Formulation optimal solution, Variation i.e., Non-square (m x n) matrix restrictions. Sequencing Introduction, Terminology, notations and assumptions, problems with n-jobs and two machines, optimal sequence algorithm, problems with n-jobs and three machines, problems with n-jobs and m-machines, graphic solutions. Travelling salesman problem. Replacement Introduction, Replacement of items that deteriorate with time – value of money unchanging and changing, Replacement of items that fail completely. Queuing Models M.M.1 & M.M.S. system cost considerations. Theory of games introduction, Two-person zero-sum games, The Maximum –Minimax principle, Games without saddle points – Mixed Strategies, 2 x n and m x 2 Games – Graphical solutions, Dominance property, Use of L.P. to games, Algebraic solutions to rectangular games. Inventory Introduction, inventory costs, Independent demand systems: Deterministic models – Fixed order size systems – Economic order quantity (EOQ) – Single items, back ordering...
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...Chapter 7 Linear Programming: Maximization Models © 2008 Prentice-Hall, Inc. Introduction Many management decisions involve trying to make the most effective use of limited resources Machinery, labor, money, time, warehouse space, raw materials Linear programming (LP) is a widely used mathematical modeling technique designed to help managers in planning and decision making relative to resource allocation Belongs to the broader field of mathematical programming In this sense, programming refers to modeling and solving a problem mathematically © 2009 Prentice-Hall, Inc. 7–2 Requirements of a Linear Programming Problem LP has been applied in many areas over the past 50 years All LP problems have 4 properties in common 1. All problems seek to maximize or minimize some quantity (the objective function) 2. The presence of restrictions or constraints that limit the degree to which we can pursue our objective 3. There must be alternative courses of action to choose from 4. The objective and constraints in problems must be expressed in terms of linear equations or inequalities © 2009 Prentice-Hall, Inc. 7–3 LP Properties and Assumptions PROPERTIES OF LINEAR PROGRAMS 1. One objective function 2. One or more constraints 3. Alternative courses of action 4. Objective function and constraints are linear ASSUMPTIONS OF LP 1. Certainty 2. Proportionality 3. Additivity 4. Divisibility 5. Nonnegative variables Table 1 © 2009 Prentice-Hall, Inc. 7–4 Basic Assumptions...
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...many more applications is the assumption of divisibility (see Sec. 3.3), which requires that noninteger values be permissible for decision variables. In many practical problems, the decision variables actually make sense only if they have integer values. For example, it is often necessary to assign people, machines, and vehicles to activities in integer quantities. If requiring integer values is the only way in which a problem deviates from a linear programming formulation, then it is an integer programming (IP) problem. (The more complete name is integer linear programming, but the adjective linear normally is dropped except when this problem is contrasted with the more esoteric integer nonlinear programming problem, which is beyond the scope of this book.) The mathematical model for integer programming is the linear programming model (see Sec. 3.2) with the one additional restriction that the variables must have integer values. If only some of the variables are required to have integer values (so the divisibility assumption holds for the rest), this model is referred to as mixed integer programming (MIP). When distinguishing the all-integer problem from this mixed case, we call the former pure integer programming. For example, the Wyndor Glass Co. problem presented in Sec. 3.1 actually would have been an IP problem if the two decision variables x1 and x2 had represented the total number of units to be produced of products 1 and 2, respectively, instead of the production rates....
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...constructs a mathematical programming model. The models created by the add-in are solved with the Excel Solver, the Jensen Network Solver or the Jensen LP/IP Solver. All are Excel add-ins. Documentation for these programs can be reached by clicking the links on the lower left. The Solver add-in comes with Excel, and it can solve linear programming, integer programming and nonlinear programming models. The Math Programming add-in automatically builds Solver models and calls the computational procedures that solve the problems. All four model types can be can be solved in this way. The Jensen LP/IP Solver solves linear or integer programming problems. It is available for the Linear/Integer Programming and Network Flow Programming model types. The Jensen Network Solver can solve pure or generalized network flow models. Both linear and integer problems can be solved. It is available for the Network Flow Programming or Transportation model types. Parametric analysis can be applied to any of the math programming models. Here one parameter is allowed to vary within a specified range and the model is solved for each value. The results are provided by a table and a chart. Side Models provide an additional form on which math programming models may be constructed. They are much more compact representation than the forms provided earlier. A model worksheet includes buttons that change the model or call the solution algorithms. When...
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...Natural Computing Series Series Editors: G. Rozenberg Th. Bäck A.E. Eiben J.N. Kok H.P. Spaink Leiden Center for Natural Computing Advisory Board: S. Amari G. Brassard K.A. De Jong C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz Z. Michalewicz M.C. Mozer E. Oja G. P˘ un J. Reif H. Rubin A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras a D. Whitley E. Winfree J.M. Zurada For further volumes: www.springer.com/series/4190 Franz Rothlauf Design of Modern Heuristics Principles and Application Prof. Dr. Franz Rothlauf Chair of Information Systems and Business Administration Johannes Gutenberg Universität Mainz Gutenberg School of Management and Economics Jakob-Welder-Weg 9 55099 Mainz Germany rothlauf@uni-mainz.de Series Editors G. Rozenberg (Managing Editor) rozenber@liacs.nl Th. Bäck, J.N. Kok, H.P. Spaink Leiden Center for Natural Computing Leiden University Niels Bohrweg 1 2333 CA Leiden, The Netherlands A.E. Eiben Vrije Universiteit Amsterdam The Netherlands ISSN 1619-7127 Natural Computing Series ISBN 978-3-540-72961-7 e-ISBN 978-3-540-72962-4 DOI 10.1007/978-3-540-72962-4 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011934137 ACM Computing Classification (1998): I.2.8, G.1.6, H.4.2 © Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations...
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...Op"erations Research This page intentionally left blank Copyright © 2007, 2005 New Age International (P) Ltd., Publishers Published by New Age International (P) Ltd., Publishers All rights reserved. No part of this ebook may be reproduced in any form, by photostat, microfilm, xerography, or any other means, or incorporated into any information retrieval system, electronic or mechanical, without the written permission of the publisher. All inquiries should be emailed to rights@newagepublishers.com ISBN (13) : 978-81-224-2944-2 PUBLISHING FOR ONE WORLD NEW AGE INTERNATIONAL (P) LIMITED, PUBLISHERS 4835/24, Ansari Road, Daryaganj, New Delhi - 110002 Visit us at www.newagepublishers.com PREFACE I started my teaching career in the year 1964. I was teaching Production Engineering subjects till 1972. In the year 1972 I have registered my name for the Industrial Engineering examination at National Institution of Industrial Engineering, Bombay. Since then, I have shifted my field for interest to Industrial Engineering subjects and started teaching related subjects. One such subject is OPERATIONS RESEARCH. After teaching these subjects till my retirement in the year 2002, it is my responsibility to help the students with a book on Operations research. The first volume of the book is LINEAR PORGRAMMING MODELS. This was published in the year 2003. Now I am giving this book OPERATIONS RESEARCH, with other chapters to students, with a hope that it will help them to understand...
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...Module B Transportation and Assignment Solution Methods B-1 B-2 Module B Transportation and Assignment Solution Methods Solution of the Transportation Model The following example was used in Chapter 6 of the text to demonstrate the formulation of the transportation model. Wheat is harvested in the Midwest and stored in grain elevators in three different cities—Kansas City, Omaha, and Des Moines. These grain elevators supply three flour mills, located in Chicago, St. Louis, and Cincinnati. Grain is shipped to the mills in railroad cars, each of which is capable of holding one ton of wheat. Each grain elevator is able to supply the following number of tons (i.e., railroad cars) of wheat to the mills on a monthly basis: Grain Elevator 1. Kansas City 2. Omaha 3. Des Moines Total Supply 150 175 275 600 tons Each mill demands the following number of tons of wheat per month. Mill A. Chicago B. St. Louis C. Cincinnati Total Demand 200 100 300 600 tons The cost of transporting one ton of wheat from each grain elevator (source) to each mill (destination) differs according to the distance and rail system. These costs are shown in the following table. For example, the cost of shipping one ton of wheat from the grain elevator at Omaha to the mill at Chicago is $7. Mill Grain Elevator 1. Kansas City 2. Omaha 3. Des Moines A. Chicago $6 7 4 B. St. Louis $ 8 11 5 C. Cincinnati $10 11 12 The problem is to determine how many tons of wheat to transport from each...
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...Transportation and Assignment Solution Methods B-1 B-2 Module B Transportation and Assignment Solution Methods Solution of the Transportation Model The following example was used in chapter 6 of the text to demonstrate the formulation of the transportation model. Wheat is harvested in the Midwest and stored in grain elevators in three different cities—Kansas City, Omaha, and Des Moines. These grain elevators supply three flour mills, located in Chicago, St. Louis, and Cincinnati. Grain is shipped to the mills in railroad cars, each car capable of holding one ton of wheat. Each grain elevator is able to supply the following number of tons (i.e., railroad cars) of wheat to the mills on a monthly basis. Grain Elevator 1. Kansas City 2. Omaha 3. Des Moines Total Supply 150 175 275 600 tons Each mill demands the following number of tons of wheat per month. Mill A. Chicago B. St. Louis C. Cincinnati Total Demand 200 100 300 600 tons The cost of transporting one ton of wheat from each grain elevator (source) to each mill (destination) differs according to the distance and rail system. These costs are shown in the following table. For example, the cost of shipping one ton of wheat from the grain elevator at Omaha to the mill at Chicago is $7. Mill Grain Elevator A. Chicago B. St. Louis C. Cincinnati 1. Kansas City 2. Omaha 3. Des Moines $6 7 4 $ 8 11 5 $10 11 12 The problem is to determine how many tons of...
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...markets, the trade barriers began falling during the 1980’s and continued throughout the 1990’s. This development lead to organizations having a supply chain, that criss-crossed the whole globe. The proliferation of trade agreements has thus changed the global business scenarios. The Integrated Supply Chain Management (ISCM) is now not only a problem of integrated logistics (as a process) but also demands that the supply chain management (SCM) must look into the ramifications of these arrangements on the cost of transportation (including tariffs or duties) of products within a trade zone and outside it, besides, developing logistics strategies. The field has thus developed in the last few years for bridging the gap between demand and supply vis-à-vis efficiency and cost trade-offs. The SCM now not only involves the “management of logistic function”, as was done in the past (to achieve internal efficiency of operations) but, includes the management and co-ordination of activities, upstream and downstream linkage(s) in the supply chain. The integrated supply chain management, in particular include : Planning and Managing supply and demand; Warehouse Management; Optimal Inventory control; Transportation and Distribution, Delivery and customer’s delight following the basic principles of supply chain management viz. working together; Enhancing revenue; Cost control; Assets utilization besides, customer’s satisfaction. The last two decade has seen the rise of a plethora of acronyms always...
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...markets, the trade barriers began falling during the 1980’s and continued throughout the 1990’s. This development lead to organizations having a supply chain, that criss-crossed the whole globe. The proliferation of trade agreements has thus changed the global business scenarios. The Integrated Supply Chain Management (ISCM) is now not only a problem of integrated logistics (as a process) but also demands that the supply chain management (SCM) must look into the ramifications of these arrangements on the cost of transportation (including tariffs or duties) of products within a trade zone and outside it, besides, developing logistics strategies. The field has thus developed in the last few years for bridging the gap between demand and supply vis-à-vis efficiency and cost trade-offs. The SCM now not only involves the “management of logistic function”, as was done in the past (to achieve internal efficiency of operations) but, includes the management and co-ordination of activities, upstream and downstream linkage(s) in the supply chain. The integrated supply chain management, in particular include : Planning and Managing supply and demand; Warehouse Management; Optimal Inventory control; Transportation and Distribution, Delivery and customer’s delight following the basic principles of supply chain management viz. working together; Enhancing revenue; Cost control; Assets utilization besides, customer’s satisfaction. The last two decade has seen the rise of a plethora of acronyms always...
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...markets, the trade barriers began falling during the 1980’s and continued throughout the 1990’s. This development lead to organizations having a supply chain, that criss-crossed the whole globe. The proliferation of trade agreements has thus changed the global business scenarios. The Integrated Supply Chain Management (ISCM) is now not only a problem of integrated logistics (as a process) but also demands that the supply chain management (SCM) must look into the ramifications of these arrangements on the cost of transportation (including tariffs or duties) of products within a trade zone and outside it, besides, developing logistics strategies. The field has thus developed in the last few years for bridging the gap between demand and supply vis-à-vis efficiency and cost trade-offs. The SCM now not only involves the “management of logistic function”, as was done in the past (to achieve internal efficiency of operations) but, includes the management and co-ordination of activities, upstream and downstream linkage(s) in the supply chain. The integrated supply chain management, in particular include : Planning and Managing supply and demand; Warehouse Management; Optimal Inventory control; Transportation and Distribution, Delivery and customer’s delight following the basic principles of supply chain management viz. working together; Enhancing revenue; Cost control; Assets utilization besides, customer’s satisfaction. The last two decade has seen the rise of a plethora of acronyms always...
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...defined around the central term value chain. Chapter 2 presents research concepts to manage the value chain structured by their area of specialization either on supply, demand or values. Secondly, within an integrated framework, the results of the specialized disciplines are combined with the objective to manage sales and supply by values and volume. Value chain management is defined and positioned with respect to other authors’ definitions. A value chain management framework is established with a strategy process on the strategic level, a planning process on the tactical level and operations processes on the operational level. These management levels are detailed and interfaces between the levels are defined. Since the considered problem is a planning problem, the framework serves for structuring planning requirements as well as the model development in the following chapters. 2.1 Value Chain Value chain as a term was created by Porter (1985), pp. 33-40. A value chain “disaggregates a firm into its strategically relevant activities in order to understand the behavior of costs and the existing and potential sources of differentiation”. Porter’s value chain consists of a “set of activities that are performed to design, produce and market, deliver and support its product”. Porter distinguishes between • primary activities: inbound logistics, operations, outbound logistics, marketing and sales, service in the core value chain creating directly value • support activities: procurement...
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... | |Paper |Title of the Paper |Duration |Maximum Marks |Total | |No. | |Of Exam | | | | | | |Theory |Sessional* | | |MCA-101 |Computer Fundamentals and Problem Solving Using C |3 Hours |80 |20 |100 | |MCA-102 |Computer Organisation |3 Hours |80 |20 |100 | |MCA-103 |Discrete Mathematical Structures |3 Hours |80 |20 |100 | |MCA-104 |Software Engineering |3 Hours |80 |20 |100 | |MCA-105 |Computer Oriented Numerical and Statistical Methods |3 Hours |80 |20 |100 | |MCA-106 |Software Laboratory - I |3 Hours | | |100 | | |C (Based on MCA-101) | | | | | |MCA-107 |Software Laboratory – II |3 Hours | | |100 | |...
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.... . . . . . 2. Discrete and Continuous Compound Interest . . . . . . 3. Ordinay Annuity, Future Value and Sinking Fund . . . 4. Present Value of an Ordinay Annuity and Amortization . . . . Matrices and Systems of Linear Equations 5. Solving Linear Systems Using Augmented Matrices . . . . 6. Gauss-Jordan Elimination . . . . . . . . . . . . . . . . . . 7. The Algebra of Matrices . . . . . . . . . . . . . . . . . . 8. Inverse Matrices and their Applications to Linear Systems . . . . . . . . . . . . . . . . . . . . . . . . Linear Programming 9. Solving Systems of Linear Inequalities . . . . . . . . . . . . . . 10. Geometric Method for Solving Linear Programming Problems 11. Simplex Method for Solving Linear Programming Problems . 12. The Dual Problem: Minimization with ≥ Constraints . . . . . Counting Principles, Permuations, and 13. Sets . . . . . . . . . . . . . . . . . . 14. Counting Principles . . . . . . . . . 15. Permutations and Combinations . . . . . . 5 5 12 19 26 . . . . 34 34 42 53 62 . . . . 69 69 77 86 97 Combinations 106 . . . . . . . . . . . . . . . 106 . . . . . . . . . . . . . . . 117 . . . . . . . . . . . . . . . 123 Probability 129 16. Sample Spaces, Events, and Probability . . . . . . . . . . . . . 129 17. Probability of Unions and Intersections; Odds . . . . . . . . . . 141 18. Conditional Probability and Independent Events . . . . . . . . 148 19...
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