Operations Research Series – serie

Industrial engineering has expanded from its origins in manufacturing to transportation, health care, logistics, services, and more. A common denominator among all these industries, and one of the biggest challenges facing decision-makers, is the unpredictability of systems. Probability Models in Operations Research provides a comprehensive overview of the probabilistic and stochastic modeling approaches commonly used to capture the randomness in industrial and systems engineering.

Stochastic modeling is a set of quantitative techniques for analyzing practical systems with random factors. This area is highly technical and mainly developed by mathematicians. Most existing books are for those with extensive mathematical training; this book minimizes that need and makes the topics easily understandable.Fundamentals of Stochastic Models offers many practical examples and applications and bridges the gap between elementary stochastics process theory and advanced process theory. It addresses both performance evaluation and optimization of stochastic systems and covers different modern analysis techniques such as matrix analytical methods and diffusion and fluid limit methods. It goes on to explore the linkage between stochastic models, machine learning, and artificial intelligence, and discusses how to make use of intuitive approaches instead of traditional theoretical approaches.The goal is to minimize the mathematical background of readers that is required to understand the topics covered in this book. Thus, the book is appropriate for professionals and students in industrial engineering, business and economics, computer science, and applied mathematics.

Stochastic modeling is a set of quantitative techniques for analyzing practical systems with random factors. This area is highly technical and mainly developed by mathematicians. Most existing books are for those with extensive mathematical training; this book minimizes that need and makes the topics easily understandable.Fundamentals of Stochastic Models offers many practical examples and applications and bridges the gap between elementary stochastics process theory and advanced process theory. It addresses both performance evaluation and optimization of stochastic systems and covers different modern analysis techniques such as matrix analytical methods and diffusion and fluid limit methods. It goes on to explore the linkage between stochastic models, machine learning, and artificial intelligence, and discusses how to make use of intuitive approaches instead of traditional theoretical approaches.The goal is to minimize the mathematical background of readers that is required to understand the topics covered in this book. Thus, the book is appropriate for professionals and students in industrial engineering, business and economics, computer science, and applied mathematics.

Recipient of the 2019 IISE Institute of Industrial and Systems Engineers Joint Publishers Book-of-the-Year Award This is a comprehensive textbook on service systems engineering and management. It emphasizes the use of engineering principles to the design and operation of service enterprises. Service systems engineering relies on mathematical models and methods to solve problems in the service industries. This textbook covers state-of-the-art concepts, models and solution methods important in the design, control, operations and management of service enterprises. Service Systems Engineering and Management begins with a basic overview of service industries and their importance in today’s economy. Special challenges in managing services, namely, perishability, intangibility, proximity and simultaneity are discussed. Quality of service metrics and methods for measuring them are then discussed. Evaluating the design and operation of service systems frequently involves the conflicting criteria of cost and customer service. This textbook presents two approaches to evaluate the performance of service systems – Multiple Criteria Decision Making and Data Envelopment Analysis. The textbook then discusses several topics in service systems engineering and management – supply chain optimization, warehousing and distribution, modern portfolio theory, revenue management, retail engineering, health systems engineering and financial services.Features: Stresses quantitative models and methods in service systems engineering and managementIncludes chapters on design and evaluation of service systems, supply chain engineering, warehousing and distribution, financial engineering, healthcare systems, retail engineering and revenue managementBridges theory and practiceContains end-of-chapter problems, case studies, illustrative examples, and real-world applications Service Systems Engineering and Management is primarily addressed to those who are interested in learning how to apply operations research models and methods for managing service enterprises. This textbook is well suited for industrial engineering students interested in service systems applications and MBA students in elective courses in operations management, logistics and supply chain management that emphasize quantitative analysis.

Supply chain management decisions are made under the conflicting criteria of maximizing profit and customer responsiveness while minimizing supply chain risk. Multiple Criteria Decision Making in Supply Chain Management provides a comprehensive overview of multi-criteria optimization models and methods that can be used in supply chain decision making.Presenting the contributions of internationally known authors, researchers, educators, and practitioners, this new book in the Operations Research Series provides readers with a single source guide to recent developments in this area. The focus of the book is on the design and operation of the supply chain system, which involves connecting many production and distribution systems, often across wide geographic distances, in such a way that the businesses involved can ultimately satisfy the consumer demand as efficiently as possible, resulting in maximum financial returns to those businesses connected to that supply chain system. The book includes several case studies on the design and operation of supply chain networks in manufacturing and healthcare.

Students with diverse backgrounds will face a multitude of decisions in a variety of engineering, scientific, industrial, and financial settings. They will need to know how to identify problems that the methods of operations research (OR) can solve, how to structure the problems into standard mathematical models, and finally how to apply or develop computational tools to solve the problems.Perfect for any one-semester course in OR, Operations Research: A Practical Introduction answers all of these needs. In addition to providing a practical introduction and guide to using OR techniques, it includes a timely examination of innovative methods and practical issues related to the development and use of computer implementations. It provides a sound introduction to the mathematical models relevant to OR and illustrates the effective use of OR techniques with examples drawn from industrial, computing, engineering, and business applications.Many students will take only one course in the techniques of Operations Research. Operations Research: A Practical Introduction offers them the greatest benefit from that course through a broad survey of the techniques and tools available for quantitative decision making. It will also encourage other students to pursue more advanced studies and provides you a concise, well-structured, vehicle for delivering the best possible overview of the discipline.

Operations Research (OR) began as an interdisciplinary activity to solve complex military problems during World War II. Utilizing principles from mathematics, engineering, business, computer science, economics, and statistics, OR has developed into a full fledged academic discipline with practical application in business, industry, government and military. Currently regarded as a body of established mathematical models and methods essential to solving complicated management issues, OR provides quantitative analysis of problems from which managers can make objective decisions. Operations Research and Management Science (OR/MS) methodologies continue to flourish in numerous decision making fields.Featuring a mix of international authors, Operations Research and Management Science Handbook combines OR/MS models, methods, and applications into one comprehensive, yet concise volume. The first resource to reach for when confronting OR/MS difficulties, this text – Provides a single source guide in OR/MSBridges theory and practice Covers all topics relevant to OR/MS Offers a quick reference guide for students, researchers and practitionersContains unified and up-to-date coverage designed and edited with non-experts in mindDiscusses software availability for all OR/MS techniquesIncludes contributions from a mix of domestic and international expertsThe 26 chapters in the handbook are divided into two parts. Part I contains 14 chapters that cover the fundamental OR/MS models and methods. Each chapter gives an overview of a particular OR/MS model, its solution methods and illustrates successful applications. Part II of the handbook contains 11 chapters discussing the OR/MS applications in specific areas. They include airlines, e-commerce, energy systems, finance, military, production systems, project management, quality control, reliability, supply chain management and water resources. Part II ends with a chapter on the future of OR/MS applications.

This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out. The textbook targets engineering students and emphasizes the use of operations research models and solution methods important in the design, control, operation, and management of global supply chains.Completely updated, Supply Chain Engineering: Models and Applications, Second Edition stresses quantitative models and methods, highlights global supplier selection and vendor risk management techniques, and discusses the use of multiple criteria decision-making models in supply chain management. The new edition includes chapters on health and humanitarian supply chains, including disaster management and logistics modeling, and on warehousing and distribution. Disruptions to global supply chains due to the COVID-19 pandemic are discussed throughout the book. Industry and government strategies to make the global supply chains resilient are also presented. Thirty four case studies have been included to illustrate various supply chain models and methods. Exercises are included at the end of each chapter, and a solutions manual and PowerPoint slides are available for qualified textbook adoptions.The new edition continues to target upper-level undergraduate and graduate students in engineering, as well as MBA students in operations management, logistics, and supply chain management programs that emphasize quantitative analysis. It is also useful as a reference for technical professionals and researchers in industrial engineering, supply chain management, procurement, logistics and health administration.

This new edition textbook continues down the path that the first edition, winner of the 2013 IISE/Joint Publishers Book-of-the-Year Award, successfully carved out. The textbook targets engineering students and emphasizes the use of operations research models and solution methods important in the design, control, operation, and management of global supply chains.Completely updated, Supply Chain Engineering: Models and Applications, Second Edition stresses quantitative models and methods, highlights global supplier selection and vendor risk management techniques, and discusses the use of multiple criteria decision-making models in supply chain management. The new edition includes chapters on health and humanitarian supply chains, including disaster management and logistics modeling, and on warehousing and distribution. Disruptions to global supply chains due to the COVID-19 pandemic are discussed throughout the book. Industry and government strategies to make the global supply chains resilient are also presented. Thirty four case studies have been included to illustrate various supply chain models and methods. Exercises are included at the end of each chapter, and a solutions manual and PowerPoint slides are available for qualified textbook adoptions.The new edition continues to target upper-level undergraduate and graduate students in engineering, as well as MBA students in operations management, logistics, and supply chain management programs that emphasize quantitative analysis. It is also useful as a reference for technical professionals and researchers in industrial engineering, supply chain management, procurement, logistics and health administration.

This book takes an approach that leverages methods using time series analysis, machine learning, and stochastic models to effectively forecast solar power. The goal of this book is not only to produce an accurate forecast but also to make it conducive to being used for decision-making.Solar Power Forecasting: Using Time Series and Machine Learning combines traditional forecasting with recent advances in machine learning and data science. It uses a decision-making-oriented approach and provides probabilistic forecasts and methods as well as explains the analytical underpinnings of accuracy metrics in detail. As it illustrates through examples of how forecasting can be used in planning and operations, the book also delivers a systems-level approach.This comprehensive resource covers various aspects of solar forecasting, including data science methods, computational techniques, and mathematical foundations. It serves as a valuable tool for practitioners, students, and experienced researchers, both in the solar power industry and in the broader field of forecasting.Color figures can be found on Routledge.com/9781032515328 and in the eBook version.

This book takes an approach that leverages methods using time series analysis, machine learning, and stochastic models to effectively forecast solar power. The goal of this book is not only to produce an accurate forecast but also to make it conducive to being used for decision-making.Solar Power Forecasting: Using Time Series and Machine Learning combines traditional forecasting with recent advances in machine learning and data science. It uses a decision-making-oriented approach and provides probabilistic forecasts and methods as well as explains the analytical underpinnings of accuracy metrics in detail. As it illustrates through examples of how forecasting can be used in planning and operations, the book also delivers a systems-level approach.This comprehensive resource covers various aspects of solar forecasting, including data science methods, computational techniques, and mathematical foundations. It serves as a valuable tool for practitioners, students, and experienced researchers, both in the solar power industry and in the broader field of forecasting.Color figures can be found on Routledge.com/9781032515328 and in the eBook version.

Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current l

Written with students and professors in mind, Analysis of Queues: Methods and Applications combines coverage of classical queueing theory with recent advances in studying stochastic networks. Exploring a broad range of applications, the book contains plenty of solved problems, exercises, case studies, paradoxes, and numerical examples.In addition to the standard single-station and single class discrete queues, the book discusses models for multi-class queues and queueing networks as well as methods based on fluid scaling, stochastic fluid flows, continuous parameter Markov processes, and quasi-birth-and-death processes, to name a few. It describes a variety of applications including computer-communication networks, information systems, production operations, transportation, and service systems such as healthcare, call centers and restaurants.

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 discussion of each of these classical models begins with the motivation for studying the problem as well as examples of the problem’s application in operations planning contexts. The book explores special structural results and properties of optimal solutions that have led to effective algorithmic solution approaches for each problem class.Each of the models and solution methods presented is the result of high-impact research that has been published in the scholarly literature, with appropriate references cited throughout the book. The author highlights the close relationships among the models, examining those situations in which a particular model results as a special case of other related models or how one model generalizes another. Understanding these relationships allows you to more easily characterize new models being developed through their relationships to classical models. The models and methods presented in the book have widespread application in operations planning. It enables you to recognize the structural similarities between models and to recognize these structural elements within other contexts. It also gives you an understanding of various critical operations research techniques and classical operations planning models, without the need to consult numerous sources.

A handbook in the truest sense of the word, the first edition of the Operations Research Calculations Handbook quickly became an indispensible resource. While other books available tend to give detailed information about specific topics, this one contains comprehensive information and results useful for real-world problem solving. Reflecting the breadth and depth of growth in the field, the scope of the second edition has been expanded to cover several additional topics. And as with the first edition, it focuses on presenting analytical results and formulas that allow quick calculations and provide understanding of system models.See what‘s in the Second Edition:New chapters include Order Statistics, Traffic Flow and Delay, and Heuristic Search Methods New sections include Distance Norms, Hyper-Exponential and Hypo-Exponential Distributions Newly derived formulas and an expanded reference listLike its predecessor, the new edition of this handbook presents the analytical results and formulas needed in the scientific applications of operations research and management. It continues to provide quick calculations and insight into system performance. Presenting practical results and formulas without derivations, the material is organized by topic and offered in a concise format that allows ready-access to a wide range of results in a single volume. The field of operations research encompasses a growing number of technical areas, and uses analyses and techniques from a variety of branches of mathematics, statistics, and other scientific disciplines. And as the field continues to grow, there is an even greater need for key results to be summarized and easily accessible in one reference volume. Yet many of the important results and formulas are widely scattered among different textbooks and journals and are often hard to find in t

Multiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.

A handbook in the truest sense of the word, the first edition of the Operations Research Calculations Handbook quickly became an indispensible resource. While other books available tend to give detailed information about specific topics, this one contains comprehensive information and results useful for real-world problem solving. Reflecting the breadth and depth of growth in the field, the scope of the second edition has been expanded to cover several additional topics. And as with the first edition, it focuses on presenting analytical results and formulas that allow quick calculations and provide understanding of system models.See what’s in the Second Edition: New chapters include Order Statistics, Traffic Flow and Delay, and Heuristic Search Methods New sections include Distance Norms, Hyper-Exponential and Hypo-Exponential Distributions Newly derived formulas and an expanded reference listLike its predecessor, the new edition of this handbook presents the analytical results and formulas needed in the scientific applications of operations research and management. It continues to provide quick calculations and insight into system performance. Presenting practical results and formulas without derivations, the material is organized by topic and offered in a concise format that allows ready-access to a wide range of results in a single volume. The field of operations research encompasses a growing number of technical areas, and uses analyses and techniques from a variety of branches of mathematics, statistics, and other scientific disciplines. And as the field continues to grow, there is an even greater need for key results to be summarized and easily accessible in one reference volume. Yet many of the important results and formulas are widely scattered among different textbooks and journals and are often hard to find in the midst of mathematical derivations. This book provides a one-stop resource for many important results and formulas needed in operations research and management science applications.

Industrial engineering has expanded from its origins in manufacturing to transportation, health care, logistics, services, and more. A common denominator among all these industries, and one of the biggest challenges facing decision-makers, is the unpredictability of systems. Probability Models in Operations Research provides a comprehensive overview of the probabilistic and stochastic modeling approaches commonly used to capture the randomness in industrial and systems engineering.

Written with students and professors in mind, Analysis of Queues: Methods and Applications combines coverage of classical queueing theory with recent advances in studying stochastic networks. Exploring a broad range of applications, the book contains plenty of solved problems, exercises, case studies, paradoxes, and numerical examples.In addition to the standard single-station and single class discrete queues, the book discusses models for multi-class queues and queueing networks as well as methods based on fluid scaling, stochastic fluid flows, continuous parameter Markov processes, and quasi-birth-and-death processes, to name a few. It describes a variety of applications including computer-communication networks, information systems, production operations, transportation, and service systems such as healthcare, call centers and restaurants.

Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB® provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current linear optimization technologies such as predictor-path following interior point methods for both linear and quadratic optimization as well as the inclusion of linear optimization of uncertainty i.e. stochastic programming with recourse and robust optimization. The author introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. The author’s unusual approach—developing these topics in an introductory book—highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in real world environments. The author also includes applications and case studies from finance and supply chain management that involve the use of MATLAB.Even though there are several LP texts in the marketplace, most do not cover data uncertainty using stochastic programming and robust optimization techniques. Most emphasize the use of MS Excel, while this book uses MATLAB which is the primary tool of many engineers, including financial engineers. The book focuses on state-of-the-art methods for dealing with parameter uncertainty in linear programming, rigorously developing theory and methods. But more importantly, the author’s meticulous attention to developing intuition before presenting theory makes the material come alive.

Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current l

Recipient of the 2019 IISE Institute of Industrial and Systems Engineers Joint Publishers Book-of-the-Year Award This is a comprehensive textbook on service systems engineering and management. It emphasizes the use of engineering principles to the design and operation of service enterprises. Service systems engineering relies on mathematical models and methods to solve problems in the service industries. This textbook covers state-of-the-art concepts, models and solution methods important in the design, control, operations and management of service enterprises. Service Systems Engineering and Management begins with a basic overview of service industries and their importance in today’s economy. Special challenges in managing services, namely, perishability, intangibility, proximity and simultaneity are discussed. Quality of service metrics and methods for measuring them are then discussed. Evaluating the design and operation of service systems frequently involves the conflicting criteria of cost and customer service. This textbook presents two approaches to evaluate the performance of service systems – Multiple Criteria Decision Making and Data Envelopment Analysis. The textbook then discusses several topics in service systems engineering and management – supply chain optimization, warehousing and distribution, modern portfolio theory, revenue management, retail engineering, health systems engineering and financial services.Features: Stresses quantitative models and methods in service systems engineering and managementIncludes chapters on design and evaluation of service systems, supply chain engineering, warehousing and distribution, financial engineering, healthcare systems, retail engineering and revenue managementBridges theory and practiceContains end-of-chapter problems, case studies, illustrative examples, and real-world applicationsService Systems Engineering and Management is primarily addressed to those who are interested in learning how to apply operations research models and methods for managing service enterprises. This textbook is well suited for industrial engineering students interested in service systems applications and MBA students in elective courses in operations management, logistics and supply chain management that emphasize quantitative analysis.

This textbook presents methodologies and applications associated with multiple criteria decision analysis (MCDA), especially for those students with an interest in industrial engineering. With respect to methodology, the book covers (1) problem structuring methods; (2) methods for ranking multi-dimensional deterministic outcomes including multiattribute value theory, the analytic hierarchy process, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and outranking techniques; (3) goal programming,; (4) methods for describing preference structures over single and multi-dimensional probabilistic outcomes (e.g., utility functions); (5) decision trees and influence diagrams; (6) methods for determining input probability distributions for decision trees, influence diagrams, and general simulation models; and (7) the use of simulation modeling for decision analysis.This textbook also offers:· Easy to follow descriptions of how to apply a wide variety of MCDA techniques· Specific examples involving multiple objectives and/or uncertainty/risk of interest to industrial engineers· A section on outranking techniques ; this group of techniques, which is popular in Europe, is very rarely mentioned as a methodology for MCDA in the United States· A chapter on simulation as a useful tool for MCDA, including ranking & selection procedures. Such material is rarely covered in courses in decision analysis· Both material review questions and problems at the end of each chapter . Solutions to the exercises are found in the Solutions Manual which will be provided along with PowerPoint slides for each chapter.The methodologies are demonstrated through the use of applications of interest to industrial engineers, including those involving product mix optimization, supplier selection, distribution center location and transportation planning, resource allocation and scheduling of a medical clinic, staffing of a call center, quality control, project management, production and inventory control,and so on. Specifically, industrial engineering problems are structured as classical problems in multiple criteria decision analysis, and the relevant methodologies are demonstrated.

Multiple Criteria Decision Making (MCDM) is a subfield of Operations Research, dealing with decision making problems. A decision-making problem is characterized by the need to choose one or a few among a number of alternatives. The field of MCDM assumes special importance in this era of Big Data and Business Analytics. In this volume, the focus will be on modelling-based tools for Business Analytics (BA), with exclusive focus on the sub-field of MCDM within the domain of operations research. The book will include an Introduction to Big Data and Business Analytics, and challenges and opportunities for developing MCDM models in the era of Big Data.

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 discussion of each of these classical models begins with the motivation for studying the problem as well as examples of the problem’s application in operations planning contexts. The book explores special structural results and properties of optimal solutions that have led to effective algorithmic solution approaches for each problem class.Each of the models and solution methods presented is the result of high-impact research that has been published in the scholarly literature, with appropriate references cited throughout the book. The author highlights the close relationships among the models, examining those situations in which a particular model results as a special case of other related models or how one model generalizes another. Understanding these relationships allows you to more easily characterize new models being developed through their relationships to classical models. The models and methods presented in the book have widespread application in operations planning. It enables you to recognize the structural similarities between models and to recognize these structural elements within other contexts. It also gives you an understanding of various critical operations research techniques and classical operations planning models, without the need to consult numerous sources.