**Author**: Ronald L. Rardin

**Publisher:** Pearson

**ISBN:**

**Category:** Technology & Engineering

**Page:** 920

**View:** 408

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Developing skills and intuitions through accessible optimization models and analysis. Rardin’s Optimization in Operations Research, Second Edition builds on the critically acclaimed first edition published nearly two decades ago and named Book of the Year in 1999 by the Institute of Industrial Engineers. The goal of the Second Edition is to make the tools of optimization modeling and analysis even more widely accessible to advanced undergraduate and beginning graduate students, as well as to researchers and working practitioners who use it as a reference for self-study. The emphasis lies in developing skills and intuitions that students can apply in real settings or later coursework. LIke the first, the Second Edition covers the full scope of optimization (mathematical programming), spanning linear, integer, nonlinear, network, and dynamic programming models and algorithms, in both single and multiobjective contexts. New material adds large-scale, stochastic and complexity topics, while broadly deepening mathematical rigor without sacrificing the original’s intuitive style. This edition also continues the author’s belief that making optimization materials accessible and exciting to readers of diverse backgrounds requires a continuing discourse on optimization modeling. Every algorithm and analytic principle is developed in the context of a brief story, and computational exercises often begin with a formulation step.

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.

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.

Uniquely blends mathematical theory and algorithm design forunderstanding and modeling real-world problems Optimization modeling and algorithms are key components toproblem-solving across various fields of research, from operationsresearch and mathematics to computer science and engineering.Addressing the importance of the algorithm design process.Deterministic Operations Research focuses on the design ofsolution methods for both continuous and discrete linearoptimization problems. The result is a clear-cut resource forunderstanding three cornerstones of deterministic operationsresearch: modeling real-world problems as linear optimizationproblem; designing the necessary algorithms to solve theseproblems; and using mathematical theory to justify algorithmicdevelopment. Treating real-world examples as mathematical problems, theauthor begins with an introduction to operations research andoptimization modeling that includes applications form sportsscheduling an the airline industry. Subsequent chapters discussalgorithm design for continuous linear optimization problems,covering topics such as convexity. Farkas’ Lemma, and thestudy of polyhedral before culminating in a discussion of theSimplex Method. The book also addresses linear programming dualitytheory and its use in algorithm design as well as the Dual SimplexMethod. Dantzig-Wolfe decomposition, and a primal-dual interiorpoint algorithm. The final chapters present network optimizationand integer programming problems, highlighting various specializedtopics including label-correcting algorithms for the shortest pathproblem, preprocessing and probing in integer programming, liftingof valid inequalities, and branch and cut algorithms. Concepts and approaches are introduced by outlining examplesthat demonstrate and motivate theoretical concepts. The accessiblepresentation of advanced ideas makes core aspects easy tounderstand and encourages readers to understand how to think aboutthe problem, not just what to think. Relevant historical summariescan be found throughout the book, and each chapter is designed asthe continuation of the “story” of how to both modeland solve optimization problems by using the specificproblems-linear and integer programs-as guides. The book’svarious examples are accompanied by the appropriate models andcalculations, and a related Web site features these models alongwith Maple™ and MATLAB® content for the discussedcalculations. Thoroughly class-tested to ensure a straightforward, hands-onapproach, Deterministic Operations Research is an excellentbook for operations research of linear optimization courses at theupper-undergraduate and graduate levels. It also serves as aninsightful reference for individuals working in the fields ofmathematics, engineering, computer science, and operations researchwho use and design algorithms to solve problem in their everydaywork.

This two-volume set of texts explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. They demonstrate the interdependence of three areas of study that usually receive separate treatments: stochastic processes, operating characteristics of stochastic systems, and stochastic optimization. Comprehensive in its scope, they emphasize the practical importance, intellectual stimulation, and mathematical elegance of stochastic models and are intended primarily as graduate-level texts.

This volume of a 2-volume set explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. Explores stochastic processes, operating characteristics of stochastic systems, and stochastic optimization. Comprehensive in its scope, this graduate-level text emphasizes the practical importance, intellectual stimulation, and mathematical elegance of stochastic models.

260 2 Crew Legalities and Crew Pairing Repair 264 3 Model and Mathematical Formulation 266 4 Solution Methodology 271 5 Computational Experiences 277 6 Conclusion 285 REFERENCES 286 10 THE USE OF OPTIMIZATION TO PERFORM AIR TRAFFIC FLOW MANAGEMENT Kenneth Lindsay, E. Andrew Boyd, George Booth, and Charles Harvey 287 1 Introduction 288 2 The Traffic Flow Management (TFM) Problem 289 3 Recent TFM Optimization Models 292 4 The Time Assignment Model (TAM) 302 5 Summary and Conclusions 307 REFERENCES 309 11 THE PROCESSES OF AIRLINE SYSTEM OPERATIONS CONTROL Seth C. Grandeau, Michael D. Clarke, and Dennis F.X. Mathaisel 312 1 Introduction 313 2 The Four Phases of Airline Schedule Development 315 The Airline Operations Control Center (OCC) 3 320 4 Analysis of Operational Problems 331 5 Areas For Improvement 352 6 Case Study: PT Garuda Indonesia Airlines 357 REFERENCES 368 12 THE COMPLEX CONFIGURATION MODEL Bruce W. Patty and Jim Diamond 370 1 Introduction 370 Problem Description 2 371 Problem Formulation 3 375 4 Model Implementation 379 ix Contents 383 5 Summary REFERENCES 383 13 INTEGRATED AIRLINE SCHEDULE PLANNING Cynthia Barnhart, Fang Lu, and Rajesh Shenoi 384 1 Introduction 385 2 Fleet Assignment and Crew Pairing Problems: Existing M- els and Algorithms 388 3 An Integrated Approximate Fleet Assignment and Crew Pa- ing Model 393 4 An Advanced Integrated Solution Approach 395 5 Case Study 396 6 Conclusions and Future Research Directions 399 REFERENCES 401 14 AIRLINE SCHEDULE PERTURBATION PROBLEM: LANDING AND TAKEOFF WITH

A Symposium was held on February 25, 2006 in honor of the 80th birthday of Saul I. Gass and his major contributions to the field of operations research over 50 years. This volume includes articles from each of the Symposium speakers plus 16 other articles from friends, colleagues, and former students. Each contributor offers a forward-looking perspective on the future development of the field.

This comprehensive book provides the students with the basic knowledge of the processes involved in operations research and discusses the techniques of solutions to problems and their applications in daily life. Beginning with an overview of the operations research models and decision-making, the book describes in detail the various optimization techniques such as linear and non-linear programming, integer linear programming, dynamic programming, genetic programming, and network techniques such as PERT (program evaluation review technique) and CPM (critical path method). It also explains the transportation and assignment problems, queuing theory, games theory, sequencing, replacement and capital investment decisions and inventory. Besides, the book discusses the Monte Carlo simulation techniques for solving queuing, demand forecasting, inventory and scheduling problems and elaborates on genetic algorithms. Each mathematical technique is dealt with in two parts. The first part explains the theory underlying the methodology of solution to problems. The second part illustrates how the theory is applied to solve different kinds of problems. This book is designed as a textbook for the undergraduate students of mechanical engineering, electrical engineering, production and industrial engineering, computer science and engineering and information technology. Besides, the book will also be useful to the postgraduate students of production and industrial engineering, computer applications, business administration, commerce, mathematics and statistics. KEY FEATURES : Includes a large number of solved problems to help students comprehend the concepts with ease. Gives step-by-step explanation of algorithms by taking problems. Provides chapter-end exercises to drill the students in self-study.

This book constitutes the proceedings of the 9th International Conference on Discrete Optimization and Operations Research, DOOR 2016, held in Vladivostok, Russia, in September 2016. The 39 full papers presented in this volume were carefully reviewed and selected from 181 submissions. They were organized in topical sections named: discrete optimization; scheduling problems; facility location; mathematical programming; mathematical economics and games; applications of operational research; and short communications.