Computer Methods in Operations Research
Author | : Arne Thesen |
Publisher | : |
Total Pages | : 296 |
Release | : 1978 |
Genre | : Business & Economics |
ISBN | : |
Computer Methods in Operations Research.
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Author | : Arne Thesen |
Publisher | : |
Total Pages | : 296 |
Release | : 1978 |
Genre | : Business & Economics |
ISBN | : |
Computer Methods in Operations Research.
Author | : Arne Thesen |
Publisher | : Academic Press |
Total Pages | : 283 |
Release | : 2014-05-10 |
Genre | : Reference |
ISBN | : 1483260747 |
Computer Methods in Operations Research focuses on the computational methods used in operations research. Topics covered range from list processing to sorting and searching, networks, and critical path methods. Resource-constrained scheduling methods and linear programming methods are also discussed, along with the branch and bound concept. Comprised of 11 chapters, this book begins with a review of some of the basic principles that make a software development effort successful, emphasizing the need to keep things simple and understandable. The reader is then introduced to the basic principles of list processing, searching, and sorting; the concept of networks and several matrix- and list-oriented methods for representing networks in the computer; and the critical path method. Subsequent chapters deal with more complex programs and algorithms to handle scheduling of activities under precedence and resource restrictions; the resource-constrained scheduling problem, formulated both in an exact (using integer programming) and in a heuristic manner; the design of algorithms for the solution of large linear programming problems; and the application of list processing concepts to the development of branch and bound algorithms for solution of combinatorial optimization problems. The book also considers the design of random number generators and discrete event simulation programming before concluding with a description of two programming languages, GPSS and WIDES, for use in simulation modeling. This monograph will be of value to students and practitioners of operations research and industrial engineering.
Author | : Philip M. Morse |
Publisher | : Courier Corporation |
Total Pages | : 188 |
Release | : 2012-12-04 |
Genre | : Technology & Engineering |
ISBN | : 0486154459 |
Operations research originated during World War II with the military's need for a scientific method of providing executives with a quantitative decision-making basis. This text explores strategical kinematics, tactical analysis, gunnery and bombardment problems, more.
Author | : John W. Chinneck |
Publisher | : Springer Science & Business Media |
Total Pages | : 283 |
Release | : 2007-10-25 |
Genre | : Mathematics |
ISBN | : 0387749322 |
Written by a world leader in the field and aimed at researchers in applied and engineering sciences, this brilliant text has as its main goal imparting an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. It includes algorithms on seeking feasibility and analyzing infeasibility, as well as describing new and surprising applications.
Author | : Changhyun Kwon |
Publisher | : Changhyun Kwon |
Total Pages | : 262 |
Release | : 2019-03-03 |
Genre | : Technology & Engineering |
ISBN | : 1798205475 |
Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires "translation" among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation. The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science. For more information, visit: http://www.chkwon.net/julia
Author | : Michael Carter |
Publisher | : CRC Press |
Total Pages | : 448 |
Release | : 2018-08-06 |
Genre | : Business & Economics |
ISBN | : 1498780113 |
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.
Author | : David J. Rader |
Publisher | : John Wiley & Sons |
Total Pages | : 631 |
Release | : 2013-06-07 |
Genre | : Mathematics |
ISBN | : 1118627350 |
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.
Author | : Richard Johannes Boucherie |
Publisher | : World Scientific |
Total Pages | : 512 |
Release | : 2021-10-26 |
Genre | : Mathematics |
ISBN | : 9811239363 |
This attractive textbook with its easy-to-follow presentation provides a down-to-earth introduction to operations research for students in a wide range of fields such as engineering, business analytics, mathematics and statistics, computer science, and econometrics. It is the result of many years of teaching and collective feedback from students.The book covers the basic models in both deterministic and stochastic operations research and is a springboard to more specialized texts, either practical or theoretical. The emphasis is on useful models and interpreting the solutions in the context of concrete applications.The text is divided into several parts. The first three chapters deal exclusively with deterministic models, including linear programming with sensitivity analysis, integer programming and heuristics, and network analysis. The next three chapters primarily cover basic stochastic models and techniques, including decision trees, dynamic programming, optimal stopping, production planning, and inventory control. The final five chapters contain more advanced material, such as discrete-time and continuous-time Markov chains, Markov decision processes, queueing models, and discrete-event simulation.Each chapter contains numerous exercises, and a large selection of exercises includes solutions.
Author | : Abhijit Gosavi |
Publisher | : Springer |
Total Pages | : 530 |
Release | : 2014-10-30 |
Genre | : Business & Economics |
ISBN | : 1489974911 |
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.
Author | : P. L. Hammer |
Publisher | : Springer Science & Business Media |
Total Pages | : 343 |
Release | : 2012-12-06 |
Genre | : Business & Economics |
ISBN | : 3642858236 |
In classical analysis, there is a vast difference between the class of problems that may be handled by means of the methods of calculus and the class of problems requiring combinatorial techniques. With the advent of the digital computer, the distinction begins to blur, and with the increasing emphasis on problems involving optimization over structures, tIlE' distinction vanishes. What is necessary for the analytic and computational treatment of significant questions arising in modern control theory, mathematical economics, scheduling theory, operations research, bioengineering, and so forth is a new and more flexible mathematical theory which subsumes both the cla8sical continuous and discrete t 19orithms. The work by HAMMER (IVANESCU) and RUDEANU on Boolean methods represents an important step in this dnectlOn, and it is thus a great pleasure to welcome it into print. It will certainly stimulate a great deal of additional research in both theory and application. RICHARD BELLMAN University of Southern California FOf(,WOl'