Manuel Laguna – författare

This text reflects the need for preserving the marriage between operations research and computing in order to create more efficient and powerful software tools in the years ahead. The 17 papers included in this volume were carefully selected to cover a wide range of topics related to the interface between operations research and computer science. The volume includes the now perennial applications of metaheuristics (such as genetic algorithms, scatter search and tabu search) as well as research on global optimization, knowledge management, software maintainability and object-oriented modelling. These topics reflect the complexity and variety of the problems that current and future software tools must be capable of tackling. The OR/CS interface is frequently at the core of successful applications and the development of new methodologies, making the research in this book a relevant reference, in the future. The editors' goal for this book has been to increase the interest in the interface of computer science and operations research. Both researchers and practitioners should find this book useful.The tutorial papers may spark the interest of practitioners for developing and applying new techniques to complex problems. In addition, the book includes papers that explore new angles of well-established methods for problems in the area pf nonlinear optimization and mixed integer programming, which researchers in these fields may find interesting.

This text contains contributions from various authors on topics related to probabilistic methods used for the design of structures. Several of the papers were initially prepared for advanced courses on structural reliability or on probabilistic methods for structural design. These courses have been held in several countries and have been given by various groups of lecturers. They were aimed at engineers and researchers that had already been exposed to structural reliability methods, and presented overviews of the various topics. The book includes a selection of those contributions, which will be of use for future courses or for engineers and researchers who want an up-to-date overview. It is complementary to the existing textbooks on structural reliability, which normally cover the basic topics but exclude the more specialized aspects. In addition, several papers have been specially prepared for this publication, complementing the others in providing an overall account of recent advances in the field.Among the topics covered are: modelling of uncertainty, prediction of the strength of components, load modelling and combination, assessment of structural systems, stochastic finite elements and design consideration. This volume should prove useful for practitioners as well as for researchers.

This text contains contributions from various authors on topics related to probabilistic methods used for the design of structures. Several of the papers were initially prepared for advanced courses on structural reliability or on probabilistic methods for structural design. These courses have been held in several countries and have been given by various groups of lecturers. They were aimed at engineers and researchers that had already been exposed to structural reliability methods, and presented overviews of the various topics. The book includes a selection of those contributions, which will be of use for future courses or for engineers and researchers who want an up-to-date overview. It is complementary to the existing textbooks on structural reliability, which normally cover the basic topics but exclude the more specialized aspects. In addition, several papers have been specially prepared for this publication, complementing the others in providing an overall account of recent advances in the field.Among the topics covered are: modelling of uncertainty, prediction of the strength of components, load modelling and combination, assessment of structural systems, stochastic finite elements and design consideration. This volume should prove useful for practitioners as well as for researchers.

The fourth edition of this widely used textbook offers a new perspective. Previously titled Business Process Modeling, Simulation and Design, as the new title suggests, this book is about analytical business process modeling and design. However, this new edition introduces analytics to the title and to the presentation.The main objective of this book is to provide students with a comprehensive understanding of the multitude of analytical tools that can be used to model, analyze, understand, and ultimately design business processes.The most flexible and powerful of these tools, although not always the most appropriate, is discrete-event simulation. The wide range of approaches covered in this book include graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, and analytical queuing methods, as well as machine learning.The authors focus on business processes as opposed to just manufacturing processes or general operations management problems and emphasize on simulation modeling using state-of-the-art commercial simulation software.Business Process Analytics: Modeling, Simulation, and Design can be thought of as a hybrid between traditional books on process management, operations management, and simulation. The growing interest in simulation-based tools suggests that an understanding of simulation modeling, its potential as well as its limitations for analyzing and designing processes, is of key importance to students looking for a future career in operations management.Changes from the previous edition include the following:New section on data-driven process improvement (with data visualization)Added a subsection of control charts to the 6-sigma sectionReplaced business process reengineering with business process managementUpdated all text, figures, examples, and exercises to ExtendSim10 (current version)More coverage on design of experimentsMore coverage of machine learning and neural networks

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Contemporary studies demonstrate the practical advantages of this approach for solving a diverse array of optimization problems from both classical and real world settings. Scatter search contrasts with other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions in Euclidean space and by utilizing strategic designs where other approaches resort to randomization. The book's goal is to provide the basic principles and fundamental ideas that will allow the readers to create successful applications of scatter search. The book includes the C source code of the methods introduced in each chapter.

The book Scatter Search by Manuel Laguna and Rafael Martí represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but fromthe use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.

Computing Tools for Modeling, Optimization and Simulation reflects the need for preserving the marriage between operations research and computing in order to create more efficient and powerful software tools in the years ahead. The 17 papers included in this volume were carefully selected to cover a wide range of topics related to the interface between operations research and computer science. The volume includes the now perennial applications of rnetaheuristics (such as genetic algorithms, scatter search, and tabu search) as well as research on global optimization, knowledge management, software rnaintainability and object-oriented modeling. These topics reflect the complexity and variety of the problems that current and future software tools must be capable of tackling. The OR/CS interface is frequently at the core of successful applications and the development of new methodologies, making the research in this book a relevant reference in the future. The editors' goal for this book has been to increase the interest in the interface of computer science and operations research. Both researchers and practitioners will benefit from this book. The tutorial papers may spark the interest of practitioners for developing and applying new techniques to complex problems. In addition, the book includes papers that explore new angles of well-established methods for problems in the area of nonlinear optimization and mixed integer programming, which seasoned researchers in these fields may find fascinating.

This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions. Readers will be introduced to decision-making problems for which metaheuristics offer the most effective solution technique. The book not only shows business problem modelling on a spreadsheet but also how to design and create a Visual Basic for Applications code.Extra Material can be downloaded at http://extras.springer.com/978-3-319-68117-7.

This essential metaheuristics tutorial provides descriptions and practical applications in the area of business analytics. It addresses key problems in predictive and prescriptive analysis, while also illustrating how problems that arise in business analytics can be modelled and how metaheuristics can be used to find high-quality solutions.

Business Process Modeling, Simulation and Design, Third Edition provides students with a comprehensive coverage of a range of analytical tools used to model, analyze, understand, and ultimately design business processes. The new edition of this very successful textbook includes a wide range of approaches such as graphical flowcharting tools, cycle time and capacity analyses, queuing models, discrete-event simulation, simulation-optimization, and data mining for process analytics. While most textbooks on business process management either focus on the intricacies of computer simulation or managerial aspects of business processes, this textbook does both. It presents the tools to design business processes and management techniques on operating them efficiently.The book focuses on the use of discrete event simulation as the main tool for analyzing, modeling, and designing effective business processes. The integration of graphic user-friendly simulation software enables a systematic approach to create optimal designs.