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Books in Operations research management science methods

This comprehensive collection features optimization, simulation, and analytical modelling techniques fundamental to operations research and management science. Supporting researchers, analysts, and managers, it emphasizes innovative algorithms, case studies, and practical frameworks for solving complex organizational problems. Addressing supply chain, scheduling, and resource allocation, these titles promote efficiency, cost reduction, and strategic planning. The portfolio fosters data-driven decision-making that enhances organizational resilience and competitive advantage in a rapidly evolving business landscape.

BooksJournals

    • Smart Infrastructure Management

      • 1st Edition
      • June 17, 2025
      • Shi Qiu + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 0 1 7 8
      • eBook
        9 7 8 0 4 4 3 3 4 0 1 8 5
      People and businesses rely on transportation networks every day, but what happens when critical assets fail unexpectedly or pollute our environment? Smart Infrastructure Management provides an interdisciplinary exploration of this intricate and dynamic landscape, enriching the theoretical and practical understanding of state-of-the-art technologies that can productively support various stakeholders in the decision-making process throughout the entire lifecycle of infrastructure projects.The volume examines the evolutionary trajectory, inherent challenges, and pivotal methodologies of modern infrastructure management, with a narrative that spans several domains to coordinate a fully integrated approach. Key topics include data collection and sensors, spatial modeling and simulation tools, asset management, preventative or predictive maintenance measures, computational techniques, cybersecurity, and decision support systems. The transformative impact of smart cities is also explored, emphasizing their role in enhancing infrastructure capabilities.With real-world case studies systematically featured to illustrate successful implementations and valuable lessons learned, this investigation appeals not only to researchers and students but also to professionals across diverse fields, ensuring that effective strategies are integrated into industry practices, which are essential for improving infrastructure capabilities in line with society’s ever-changing needs.

    • Optimization Tools for Logistics

      • 1st Edition
      • October 20, 2015
      • Jean-Michel Réveillac
      • English
      • Hardback
        9 7 8 1 7 8 5 4 8 0 4 9 2
      • eBook
        9 7 8 0 0 8 1 0 0 4 8 2 1
      Optimization Tools for Logistics covers the theory and practice of the main principles of operational research and the ways it can be applied to logistics and decision support with regards to common software. The book is supported by worked problems and examples from industrial case studies, providing a comprehensive tool for readers from a variety of industries.

    • Handbook of Critical Issues in Goal Programming

      • 1st Edition
      • June 28, 2014
      • C. Romero
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 8 0 4 0
      • eBook
        9 7 8 1 4 8 3 2 9 5 1 1 4
      Goal Programming (GP) is perhaps the oldest and most widely used approach within the Multiple Criteria Decision Making (MCDM) paradigm. GP combines the logic of optimisation in mathematical programming with the decision maker's desire to satisfy several goals. The primary purpose of this book is to identify the critical issues in GP and to demonstrate different procedures capable of avoiding or mitigating the inherent pitfalls associated with these issues. The outcome of a search of the literature shows many instances where GP models produced misleading or even erroneous results simply because of a careless formulation of the problem. Rather than being in itself a textbook, Critical Issues in Goal Programming is designed to complement existing textbooks. It will be useful to students and researchers with a basic knowledge of GP as well as to those interested in building GP models which analyse real decision problems.

    • Markov Processes for Stochastic Modeling

      • 2nd Edition
      • May 22, 2013
      • Oliver Ibe
      • English
      • Hardback
        9 7 8 0 1 2 4 0 7 7 9 5 9
      • Paperback
        9 7 8 0 3 2 3 2 8 2 9 5 6
      • eBook
        9 7 8 0 1 2 4 0 7 8 3 9 0
      Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriente... book that also includes enough theory to provide a solid ground in the subject for the reader.

    • Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology

      • 1st Edition
      • April 9, 2013
      • Paola Lecca + 2 more
      • English
      • Hardback
        9 7 8 1 9 0 7 5 6 8 6 2 6
      • eBook
        9 7 8 1 9 0 8 8 1 8 2 1 8
      Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics.

    • Forecasting Expected Returns in the Financial Markets

      • 1st Edition
      • July 16, 2007
      • Stephen Satchell
      • English
      • Paperback
        9 7 8 0 0 8 0 9 7 6 2 8 0
      • Hardback
        9 7 8 0 7 5 0 6 8 3 2 1 0
      • eBook
        9 7 8 0 0 8 0 5 5 0 6 7 1
      Forecasting returns is as important as forecasting volatility in multiple areas of finance. This topic, essential to practitioners, is also studied by academics. In this new book, Dr Stephen Satchell brings together a collection of leading thinkers and practitioners from around the world who address this complex problem using the latest quantitative techniques.

    • Forecasting Volatility in the Financial Markets

      • 3rd Edition
      • February 19, 2007
      • Stephen Satchell + 1 more
      • English
      • Hardback
        9 7 8 0 7 5 0 6 6 9 4 2 9
      • Paperback
        9 7 8 0 0 8 0 9 7 6 2 1 1
      • eBook
        9 7 8 0 0 8 0 4 7 1 4 2 6
      Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey

    • Handbooks in Operations Research and Management Science: Simulation

      • 1st Edition
      • Volume 13
      • September 2, 2006
      • Shane G. Henderson + 1 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 1 4 2 8 8
      • eBook
        9 7 8 0 0 8 0 4 6 4 7 6 3
      This Handbook is a collection of chapters on key issues in the design and analysis of computer simulation experiments on models of stochastic systems. The chapters are tightly focused and written by experts in each area. For the purpose of this volume “simulation” refers to the analysis of stochastic processes through the generation of sample paths (realization) of the processes. Attention focuses on design and analysis issues and the goal of this volume is to survey the concepts, principles, tools and techniques that underlie the theory and practice of stochastic simulation design and analysis. Emphasis is placed on the ideas and methods that are likely to remain an intrinsic part of the foundation of the field for the foreseeable future. The chapters provide up-to-date references for both the simulation researcher and the advanced simulation user, but they do not constitute an introductory level ‘how to’ guide. Computer scientists, financial analysts, industrial engineers, management scientists, operations researchers and many other professionals use stochastic simulation to design, understand and improve communications, financial, manufacturing, logistics, and service systems. A theme that runs throughout these diverse applications is the need to evaluate system performance in the face of uncertainty, including uncertainty in user load, interest rates, demand for product, availability of goods, cost of transportation and equipment failures.

    • Handbook of Economic Forecasting

      • 1st Edition
      • Volume 1
      • May 30, 2006
      • G. Elliott + 2 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 1 3 9 5 3
      • eBook
        9 7 8 0 0 8 0 4 6 0 6 7 3
      Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.

    • Handbooks in Operations Research and Management Science

      • 1st Edition
      • Volume 12
      • December 8, 2005
      • K. Aardal + 2 more
      • English
      • Hardback
        9 7 8 0 4 4 4 5 1 5 0 7 0
      • eBook
        9 7 8 0 0 8 0 4 5 9 2 1 9
      The chapters of this Handbook volume cover nine main topics that are representative of recenttheoretical and algorithmic developments in the field. In addition to the nine papers that present the state of the art, there is an article on the early history of the field. The handbook will be a useful reference to experts in the field as well as students and others who want to learn about discrete optimization.

Related subjects

Fuzzy sets and systems

Mathematical programming networks

Planning forecasting

Simulation

Stochastic models