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| optimization techniques in operation research: Optimization Techniques in Operation Research C. B Gupta, 2008 Special features of the book 1. A very comprehensive and accessible approach in the presentation of the material. 2. A variety of solved examples to illustrate the theoretical results. 3. A large number of unsolved exercises for the students are given for practice at the end of each section. 4. Solution to each unsolved examples are given at the end of each exercise. |
| optimization techniques in operation research: Optimization in Operations Research Ronald L. Rardin, 2014-01-01 For first courses in operations research, operations management Optimization in Operations Research, Second Edition covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. This dynamic text emphasizes the importance of modeling and problem formulation andhow to apply algorithms to real-world problems to arrive at optimal solutions. Use a program that presents a better teaching and learning experience-for you and your students. Prepare students for real-world problems: Students learn how to apply algorithms to problems that get them ready for their field. Use strong pedagogy tools to teach: Key concepts are easy to follow with the text's clear and continually reinforced learning path. Enjoy the text's flexibility: The text features varying amounts of coverage, so that instructors can choose how in-depth they want to go into different topics. |
| optimization techniques in operation research: Integrated Methods for Optimization John N. Hooker, 2011-11-13 The first edition of Integrated Methods for Optimization was published in January 2007. Because the book covers a rapidly developing field, the time is right for a second edition. The book provides a unified treatment of optimization methods. It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume. There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together. (1) There is much in common among them intellectually, and to a large degree they can be understood as special cases of a single underlying solution technology. (2) A growing literature reports how they can be profitably integrated to formulate and solve a wide range of problems. (3) Several software packages now incorporate techniques from two or more of these fields. The book provides a unique resource for graduate students and practitioners who want a well-rounded background in optimization methods within a single course of study. Engineering students are a particularly large potential audience, because engineering optimization problems often benefit from a combined approach—particularly where design, scheduling, or logistics are involved. The text is also of value to those studying operations research, because their educational programs rarely cover CP, and to those studying computer science and artificial intelligence (AI), because their curric ula typically omit MP and GO. The text is also useful for practitioners in any of these areas who want to learn about another, because it provides a more concise and accessible treatment than other texts. The book can cover so wide a range of material because it focuses on ideas that arerelevant to the methods used in general-purpose optimization and constraint solvers. The book focuses on ideas behind the methods that have proved useful in general-purpose optimization and constraint solvers, as well as integrated solvers of the present and foreseeable future. The second edition updates results in this area and includes several major new topics: Background material in linear, nonlinear, and dynamic programming. Network flow theory, due to its importance in filtering algorithms. A chapter on generalized duality theory that more explicitly develops a unifying primal-dual algorithmic structure for optimization methods. An extensive survey of search methods from both MP and AI, using the primal-dual framework as an organizing principle. Coverage of several additional global constraints used in CP solvers. The book continues to focus on exact as opposed to heuristic methods. It is possible to bring heuristic methods into the unifying scheme described in the book, and the new edition will retain the brief discussion of how this might be done. |
| optimization techniques in operation research: Deterministic Operations Research David J. Rader, 2013-06-07 Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations research: modeling real-world problems as linear optimization problem; designing the necessary algorithms to solve these problems; and using mathematical theory to justify algorithmic development. Treating real-world examples as mathematical problems, the author begins with an introduction to operations research and optimization modeling that includes applications form sports scheduling an the airline industry. Subsequent chapters discuss algorithm design for continuous linear optimization problems, covering topics such as convexity. Farkas’ Lemma, and the study of polyhedral before culminating in a discussion of the Simplex Method. The book also addresses linear programming duality theory and its use in algorithm design as well as the Dual Simplex Method. Dantzig-Wolfe decomposition, and a primal-dual interior point algorithm. The final chapters present network optimization and integer programming problems, highlighting various specialized topics including label-correcting algorithms for the shortest path problem, preprocessing and probing in integer programming, lifting of valid inequalities, and branch and cut algorithms. Concepts and approaches are introduced by outlining examples that demonstrate and motivate theoretical concepts. The accessible presentation of advanced ideas makes core aspects easy to understand and encourages readers to understand how to think about the problem, not just what to think. Relevant historical summaries can be found throughout the book, and each chapter is designed as the continuation of the “story” of how to both model and solve optimization problems by using the specific problems-linear and integer programs-as guides. The book’s various examples are accompanied by the appropriate models and calculations, and a related Web site features these models along with MapleTM and MATLAB® content for the discussed calculations. Thoroughly class-tested to ensure a straightforward, hands-on approach, Deterministic Operations Research is an excellent book for operations research of linear optimization courses at the upper-undergraduate and graduate levels. It also serves as an insightful reference for individuals working in the fields of mathematics, engineering, computer science, and operations research who use and design algorithms to solve problem in their everyday work. |
| optimization techniques in operation research: Optimization Techniques in Operations Research B. D. Sivazlian, L. E. Stanfel, 1974 |
| optimization techniques in operation research: Optimization Methods in Operations Research and Systems Analysis K. V. Mital, 1983 |
| optimization techniques in operation research: Advanced Optimization and Operations Research Asoke Kumar Bhunia, Laxminarayan Sahoo, Ali Akbar Shaikh, 2020-01-09 This textbook provides students with fundamentals and advanced concepts in optimization and operations research. It gives an overview of the historical perspective of operations research and explains its principal characteristics, tools, and applications. The wide range of topics covered includes convex and concave functions, simplex methods, post optimality analysis of linear programming problems, constrained and unconstrained optimization, game theory, queueing theory, and related topics. The text also elaborates on project management, including the importance of critical path analysis, PERT and CPM techniques. This textbook is ideal for any discipline with one or more courses in optimization and operations research; it may also provide a solid reference for researchers and practitioners in operations research. |
| optimization techniques in operation research: Optimization Techniques and Applications with Examples Xin-She Yang, 2018-09-19 A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization. |
| optimization techniques in operation research: Optimization Theory, Decision Making, and Operations Research Applications Athanasios Migdalas, Angelo Sifaleras, Christos K Georgiadis, Jason Papathanasiou, Emmanouil Stiakakis, 2012-11-28 These proceedings consist of 30 selected research papers based on results presented at the 10th Balkan Conference & 1st International Symposium on Operational Research (BALCOR 2011) held in Thessaloniki, Greece, September 22-24, 2011. BALCOR is an established biennial conference attended by a large number of faculty, researchers and students from the Balkan countries but also from other European and Mediterranean countries as well. Over the past decade, the BALCOR conference has facilitated the exchange of scientific and technical information on the subject of Operations Research and related fields such as Mathematical Programming, Game Theory, Multiple Criteria Decision Analysis, Information Systems, Data Mining and more, in order to promote international scientific cooperation. The carefully selected and refereed papers present important recent developments and modern applications and will serve as excellent reference for students, researchers and practitioners in these disciplines. |
| optimization techniques in operation research: Simulation-Based Optimization Abhijit Gosavi, 2003-06-30 Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to convergence analysis of some of the methods enumerated above. *Computer programs for many algorithms of simulation-based optimization. |
| optimization techniques in operation research: Mathematical Optimization Theory and Operations Research Panos Pardalos, Michael Khachay, Alexander Kazakov, 2021-06-14 This book constitutes the proceedings of the 20th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2021, held in Irkutsk, Russia, in July 2021. The 29 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 102 submissions. Additionally, 2 full invited papers are presented in the volume. The papers are grouped in the following topical sections: combinatorial optimization; mathematical programming; bilevel optimization; scheduling problems; game theory and optimal control; operational research and mathematical economics; data analysis. |
| optimization techniques in operation research: Stochastic Optimization Methods Kurt Marti, 2015-02-21 This book examines optimization problems that in practice involve random model parameters. It details the computation of robust optimal solutions, i.e., optimal solutions that are insensitive with respect to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures and differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods. In particular, it now shows how to apply stochastic optimization methods to the approximate solution of important concrete problems arising in engineering, economics and operations research. |
| optimization techniques in operation research: Operations Research, 4th Edition Kalavathy S., Operations Research is the discipline of applying advanced analytical methods to help make better decisions. It helps the management to achieve its goals by using scientific techniques, making the study and understanding of operations research even more important in the present day scenario. This book has been written with the objective of providing students with a comprehensive textbook on the subject. It follows a simple algorithmic approach to explain each concept, often giving different steps. This approach stems from the author’s experience in teaching undergraduate and postgraduate students of Madras University and Anna University, Chennai, over many years. One of the highlights of this book is the solved-problems approach, as each chapter in the book is substantiated by a large number of solved problems. Many of the questions that have been incorporated are from previous examination papers of various universities. In addition, each chapter has numerous exercise problems at the end and a section on short questions with answers. |
| optimization techniques in operation research: Optimization in Operations Research Ronald L. Rardin, 1998 Covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. Emphasizes the importance of modeling and problem formulation, this text teaches students how to apply algorithms to real-world problems to arrive at optimal solutions. |
| optimization techniques in operation research: Optimization Methods in Operations Research and Systems Analysis K V Mital, 1996 The Mathematical Aspects Of Operations Research And Systems Analysis Concerned With Optimization Of Objectives Form The Subject Of This Book. In Its Revised, Updated And Enlarged Third Edition, Discussion On Linear Programming Has Been Expanded And Recast With Greater Emphasis On Duality Theory, Sensitivity Analysis, Parametric Programming, Multiobjective And Goal Programming And Formulation And Solution Of Practical Problems. Chapters On Nonlinear Programming Include Integer Programming, Kuhn-Tucker Theory, Separable And Quadratic Programming, Dynamic Programming, Geometric Programming And Direct Search And Gradient Methods. A Chapter On Theory Of Games Is Also Included. A Short Note On Karmarkars Projective Algorithm Is Given In The Appendix.The Book Keeps In View The Needs Of The Student Taking A Regular Course In Operations Research Or Mathematical Programming, And Also Of Research Scholars In Other Disciplines Who Have A Limited Objective Of Learning The Practical Aspects Of Various Optimization Methods To Solve Their Special Problems. For The Former, Illustrative Solved Examples And Unsolved Examples At The End Of Each Chapter, Small Enough To Be Solved By Hand, Would Be Of Greater Interest, While For He Latter, Summaries Of Computational Algorithms For Various Methods Which Would Help Him To Write Computer Programmes To Solve Larger Problems Would Be More Helpful. A Few Computer Programmes In Fortran Iv Have Also Been Given In The Appendix. |
| optimization techniques in operation research: Operations Research and Optimization Samarjit Kar, Ujjwal Maulik, Xiang Li, 2018-04-06 This book discusses recent developments in the vast domain of optimization. Featuring papers presented at the 1st International Conference on Frontiers in Optimization: Theory and Applications (FOTA 2016), held at the Heritage Institute of Technology, Kolkata, on 24–26 December 2016, it opens new avenues of research in all topics related to optimization, such as linear and nonlinear optimization; combinatorial-, stochastic-, dynamic-, fuzzy-, and uncertain optimization; optimal control theory; as well as multi-objective, evolutionary and convex optimization and their applications in intelligent information and technology, systems science, knowledge management, information and communication, supply chain and inventory control, scheduling, networks, transportation and logistics and finance. The book is a valuable resource for researchers, scientists and engineers from both academia and industry. |
| optimization techniques in operation research: Fundamentals of Optimization Techniques with Algorithms Sukanta Nayak, 2020-08-25 Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. |
| optimization techniques in operation research: OPERATIONS RESEARCH R. PANNEERSELVAM, 2006-01-01 The second edition of this well-organized and comprehensive text continues to provide an in-depth coverage of the theory and applications of operations research. It emphasizes the role of operations research not only as an effective decision-making tool, but also as an essential productivity improvement tool to deal with real-world management problems. This New Edition includes new carefully designed numerical examples that help in understanding complex mathematical concepts better. The book is an easy read, explaining the basics of operations research and discussing various optimization techniques such as linear and non-linear programming, dynamic programming, goal programming, parametric programming, integer programming, transportation and assignment problems, inventory control, and network techniques. It also gives a comprehensive account of game theory, queueing theory, project management, replacement and maintenance analysis, and production scheduling. NEW TO THIS EDITION Inclusion of quantity discount models for transportation problem. Updated inventory control model and detailed discussion on application of dynamic programming in the fields of cargo loading and single-machine scheduling. Numerous new examples that explain the operations research concepts better. New questions with complete solutions to selected problems. This book, with its many student friendly features, would be eminently suitable as a text for students of engineering (mechanical, production and industrial engineering), management, mathematics, statistics, and postgraduate students of commerce and computer applications (MCA). |
| optimization techniques in operation research: Search Methodologies Edmund K. Burke, Graham Kendall, 2013-10-18 The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this tradition. Search Methodologies has been expanded and brought completely up to date, including new chapters covering scatter search, GRASP, and very large neighborhood search. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world’s leading authorities in their field. The book provides useful guidelines for implementing the methods and frameworks described and offers valuable tutorials to students and researchers in the field. “As I embarked on the pleasant journey of reading through the chapters of this book, I became convinced that this is one of the best sources of introductory material on the search methodologies topic to be found. The book’s subtitle, “Introductory Tutorials in Optimization and Decision Support Techniques”, aptly describes its aim, and the editors and contributors to this volume have achieved this aim with remarkable success. The chapters in this book are exemplary in giving useful guidelines for implementing the methods and frameworks described.” Fred Glover, Leeds School of Business, University of Colorado Boulder, USA “[The book] aims to present a series of well written tutorials by the leading experts in their fields. Moreover, it does this by covering practically the whole possible range of topics in the discipline. It enables students and practitioners to study and appreciate the beauty and the power of some of the computational search techniques that are able to effectively navigate through search spaces that are sometimes inconceivably large. I am convinced that this second edition will build on the success of the first edition and that it will prove to be just as popular.” Jacek Blazewicz, Institute of Computing Science, Poznan University of Technology and Institute of Bioorganic Chemistry, Polish Academy of Sciences |
| optimization techniques in operation research: Optimization Techniques Chander Mohan, Kusum Deep, 2009 Suitable for various disciplines where a systematic course on optimization techniques is considered necessary, and also for research scholars as well as for specialists working in optimization related problems. |
| optimization techniques in operation research: An Introduction to Optimization Edwin K. P. Chong, Stanislaw H. Żak, 2013-02-05 Praise for the Third Edition . . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail. —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business. |
| optimization techniques in operation research: Modern Optimization Techniques with Applications in Electric Power Systems Soliman Abdel-Hady Soliman, Abdel-Aal Hassan Mantawy, 2011-12-15 This book presents the application of some AI related optimization techniques in the operation and control of electric power systems. With practical applications and examples the use of functional analysis, simulated annealing, Tabu-search, Genetic algorithms and fuzzy systems for the optimization of power systems is discussed in detail. Preliminary mathematical concepts are presented before moving to more advanced material. Researchers and graduate students will benefit from this book. Engineers working in utility companies, operations and control, and resource management will also find this book useful. |
| optimization techniques in operation research: Operations Research in Development Sector Arabinda Tripathy, Rabi Narayan Subudhi, Srikanta Patnaik, Jyotiranjan Nayak, 2018-08-28 This book analyzes the underlying theoretical principles of multi-objective linear programming problems with multi-choice parameters. It studies transportation problems on the same domain with extension to fuzzy stochastic criteria, and offers insights into sensitivity analysis through symmetric duality and complementarity using non-convex programming. These analytical presentations provide ample scope for researchers to contemplate real-world problems with an innovative vision. The formulation, analysis and solution procedures on inventory control models in the book use both deterministic and fuzzy parameters, and provide novel optimal inventory policies. The book discusses a wide range of optimal operational techniques for policy makers, government and private agencies in the fields of e-governance and agricultural crop insurance, which are crucial for developing countries. The recommendations address the gaps and remedies in various schemes that influence decision-making in the context of the economic development of such countries. |
| optimization techniques in operation research: Introductory Operations Research Harvir Singh Kasana, Krishna Dev Kumar, 2013-03-14 This introductory text provides undergraduate and graduate students with a concise and practical introduction to the primary concepts and techniques of optimization. Practicing engineers and managers will also find useful its concentration on problems and examples relevant to them. With a strong emphasis on basic concepts and techniques throughout, the book explains the theory behind each technique as simply as possible, along with illustrations and worked examples. It gives a balanced treatment of both the linear and nonlinear programming, plus search techniques, geometric programming, and game theory. Some typical problems varying in difficulty level are solved so readers can appreciate intricacies of the underlying concepts useful for practical problem solving. Suitable for individual or group learning, the book also includes numerous end-of-chapter problems for study and review. |
| optimization techniques in operation research: Nonlinear Optimization H. A. Eiselt, Carl-Louis Sandblom, 2019-11-09 This book provides a comprehensive introduction to nonlinear programming, featuring a broad range of applications and solution methods in the field of continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth of applications from a diverse mix of fields, e.g. location analysis, traffic planning, and water quality management, to name but a few. In turn, the book presents a formal description of optimality conditions, followed by an in-depth discussion of the main solution techniques. Each method is formally described, and then fully solved using a numerical example. |
| optimization techniques in operation research: Process Optimization Enrique del Castillo, 2007-09-14 PROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other noisy systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. |
| optimization techniques in operation research: Operations Research Katta G. Murty, 1995 Basic text on deterministic optimization methods. Techniques of modeling real world decision making problems, modeling examples that illustrate the use of modeling techniques, and a variety of problem classes are presented. Various types of algorithms with explanations of how each algorithm works and what conclusion can be drawn from its output, and a review of Matrix Algebra and Geometry and a chapter on Heuristic Methods. |
| optimization techniques in operation research: Operations Research P. Rama Murthy, 2007 |
| optimization techniques in operation research: Mathematics for Operations Research W. H. Marlow, 1993-01-01 Practical and applications-oriented, this text explains effective procedures for performing mathematical tasks that arise in many fields, including operations research, engineering, systems sciences, statistics, and economics. Most of the examples and many of the 1,300 problems illustrate techniques, and nearly all of the tables display reference material for procedures. 1978 edition. |
| optimization techniques in operation research: Economics and Operational Research M. H. Beilby, 2014-05-10 Economics and Operational Research explores the possible connections of the organization of human and material resources by concentrating on the interpretations of management decisions at various levels in the economy. This book discusses economics and mathematics as an analytical tool. Organized into 10 chapters, this book begins with an overview of how consumers manage their own budgets and how manufacturers select their production processes. This text then described generally how consumers and producers react to each other. Other chapters consider the problem of the transportation of goods through busy road networks and the efficiency attained through central planning. This book discusses as well the control of congestion that arises through decentralization and the construction of an overall planning model. The final chapter discusses the important aspects of national planning, wherein the collection of all consumers and producers makes up one large economic system. This book is a valuable resource for management and engineering personnel. |
| optimization techniques in operation research: Nonlinear Multiobjective Optimization Kaisa Miettinen, 2012-12-06 Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers. |
| optimization techniques in operation research: Introduction to Stochastic Programming John R. Birge, François Louveaux, 2006-04-06 This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject. |
| optimization techniques in operation research: Operations Research N. V. R. Naidu, G. Rajendra, 2013-12-30 Operation Research has emerged as the most spectacular aspect of optimization techniques. Practising professionals usually rate operations research as the most useful subjects studied in college. Operations Research is designed for the students of industrial engineering and management. This book comprises 12 chapters and provides the introduction of each chapter and various problems of real practical situation in the organizations as well as in daily life. |
| optimization techniques in operation research: Operations Research Michael Carter, Camille C. Price, Ghaith Rabadi, 2018-08-06 Operations Research: A Practical Introduction is just that: a hands-on approach to the field of operations research (OR) and a useful guide for using OR techniques in scientific decision making, design, analysis and management. The text accomplishes two goals. First, it provides readers with an introduction to standard mathematical models and algorithms. Second, it is a thorough examination of practical issues relevant to the development and use of computational methods for problem solving. Highlights: All chapters contain up-to-date topics and summaries A succinct presentation to fit a one-term course Each chapter has references, readings, and list of key terms Includes illustrative and current applications New exercises are added throughout the text Software tools have been updated with the newest and most popular software Many students of various disciplines such as mathematics, economics, industrial engineering and computer science often take one course in operations research. This book is written to provide a succinct and efficient introduction to the subject for these students, while offering a sound and fundamental preparation for more advanced courses in linear and nonlinear optimization, and many stochastic models and analyses. It provides relevant analytical tools for this varied audience and will also serve professionals, corporate managers, and technical consultants. |
| optimization techniques in operation research: Construction Program Management – Decision Making and Optimization Techniques Ali D. Haidar, 2015-09-12 Exploring complex and intelligent analytical and mathematical methods, this book examines how different approaches can be used to optimize program management in the construction industry. It presents an in-depth study of the different program management methods, ranging from simple decision-making techniques and statistics analysis to the more complex linear programming and demonstrates how knowledge-base systems and genetic algorithms can be used to optimize resources and meet time, budget and quality criteria. It addresses topics including decision-making principles, planning and scheduling, mathematical forecasting models, optimization techniques programming and artificial intelligence techniques. Providing a valuable resource for anyone managing multiple projects in the construction industry, this book is intended for civil and construction engineering students, project managers, construction managers and senior engineers. |
| optimization techniques in operation research: Operations Planning Joseph Geunes, 2014-09-18 A reference for those working at the interface of operations planning and optimization modeling, Operations Planning: Mixed Integer Optimization Models blends essential theory and powerful approaches to practical operations planning problems. It presents a set of classical optimization models with widespread application in operations planning. The |
| optimization techniques in operation research: Operations Research, 2/e A. M. Natarajan, 2014 Operations research, 2e is the study of optimization techniques. Designed to cater to the syllabi requirements of Indian universities, this book on operations research reinforces the concepts discussed in each chapter with solved problems. A unique feature of this book is that with its focus on coherence and clarity, it hand-holds students through the solutions, each step of the way. |
| optimization techniques in operation research: Applications of Operational Research and Mathematical Models in Management Miltiadis Chalikias, 2020-11-17 This book, Applications of Operational Research and Mathematical Models in Management, includes all the papers published in the Mathematics Special Issue with the same title. All the published papers are of high quality and were subjected to rigorous peer review. Mathematics is included in the Science Citation Index (Web of Science), and its current Impact Factor is 1.747. The papers in this book deal with on R&D performance models, methods for ranking the perspectives and indicators of a balance scorecard, robust optimization model applications, integrated production and distribution problem solving, demand functions, supply chain games, probabilistic optimization and profit research, coordinated techniques for order preference, robustness approaches in bank capital optimization, and hybrid methods for tourism demand forecasting. All the papers included contribute to the development of research. |
| optimization techniques in operation research: Optimization Methods in Finance Gerard Cornuejols, Reha Tütüncü, 2006-12-21 Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses. |
| optimization techniques in operation research: Operations Research Richard Johannes Boucherie, H. C. Tijms, Aleida Braaksma, 2021 All essential topics and even more are covered while keeping the size of the book down (competitive textbooks are lengthy at thousand pages, which is overwhelming for beginning students). LP-sensitivity and post-optimality analysis are presented in an easily understandable manner. Much attention is focused on heuristic solution methods and dynamic optimization. Coverage of more advanced operations research topics, such as Markovian control, inventory and queueing approximations, and networks of queues. A carefully designed collection of motivational examples and problems-- |
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The meaning of OPTIMIZATION is an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically : the …
Optimization - Taylor & Francis Online
May 26, 2025 · Reach an engaged target audience and position your brand alongside authoritative peer-reviewed research by advertising in Optimization.
1. WHAT IS OPTIMIZATION? - University of Washington
1. WHAT IS OPTIMIZATION? Optimization problem: Maximizing or minimizing some function relative to some set, often representing a range of choices available in a certain situation. The function …
Calculus I - Optimization - Pauls Online Math Notes
Nov 16, 2022 · In optimization problems we are looking for the largest value or the smallest value that a function can take. We saw how to solve one kind of optimization problem in the Absolute …
OPTIMIZATION | English meaning - Cambridge Dictionary
OPTIMIZATION definition: 1. the act of making something as good as possible: 2. the act of making something as good as…. Learn more.
Introduction to Mathematical Optimization - Stanford University
Mathematical Optimization in the “Real World” Mathematical Optimization is a branch of applied mathematics which is useful in many different fields. Here are a few examples: •Manufacturing …
A Brief Overview of Optimization Problems - MIT …
Why optimization? • In some sense, all engineering design . is optimization: choosing design parameters to improve some objective • Much of . data analysis . is also optimization: extracting …
Optimization for Data Science - GeeksforGeeks
Jul 25, 2024 · What's Optimization? Wikipedia defines optimization as a problem where you maximize or minimize a real function by systematically choosing input values from an allowed set …
Mathematical optimization - Wikipedia
High-level controllers such as model predictive control (MPC) or real-time optimization (RTO) employ mathematical optimization. These algorithms run online and repeatedly determine …
Optimization | Definition, Techniques, & Facts | Britannica
Optimization, collection of mathematical principles and methods used for solving quantitative problems. Optimization problems typically have three fundamental elements: a quantity to be …
OPTIMIZATION Definition & Meaning - Merriam-Webster
The meaning of OPTIMIZATION is an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically : …
Optimization - Taylor & Francis Online
May 26, 2025 · Reach an engaged target audience and position your brand alongside authoritative peer-reviewed research by advertising in Optimization.
1. WHAT IS OPTIMIZATION? - University of Washington
1. WHAT IS OPTIMIZATION? Optimization problem: Maximizing or minimizing some function relative to some set, often representing a range of choices available in a certain situation. The …
Calculus I - Optimization - Pauls Online Math Notes
Nov 16, 2022 · In optimization problems we are looking for the largest value or the smallest value that a function can take. We saw how to solve one kind of optimization problem in the Absolute …
OPTIMIZATION | English meaning - Cambridge Dictionary
OPTIMIZATION definition: 1. the act of making something as good as possible: 2. the act of making something as good as…. Learn more.
Introduction to Mathematical Optimization - Stanford …
Mathematical Optimization in the “Real World” Mathematical Optimization is a branch of applied mathematics which is useful in many different fields. Here are a few examples: •Manufacturing …
A Brief Overview of Optimization Problems - MIT …
Why optimization? • In some sense, all engineering design . is optimization: choosing design parameters to improve some objective • Much of . data analysis . is also optimization: …
Optimization for Data Science - GeeksforGeeks
Jul 25, 2024 · What's Optimization? Wikipedia defines optimization as a problem where you maximize or minimize a real function by systematically choosing input values from an allowed …
