So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.

So-called string problems are abundant in bioinformatics and computational biology. New optimization problems dealing with DNA or protein sequences are constantly arising and researchers are highly in need of efficient optimization techniques for solving them. One obstacle for optimization practitioners is the atypical nature of these problems which require an interdisciplinary approach in order to solve them efficiently and accurately.

This book constitutes the refereed proceedings of the 7th International Workshop on Hybrid Metaheuristics, HM 2010, held in Vienna, Austria, in October 2010. The 14 revised full papers presented were carefully reviewed and selected from 29 submissions.

This volume LNCS 12735 constitutes the papers of the 18th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2021, which was held in Vienna, Austria, in 2021. Due to the COVID-19 pandemic the conference was held online. The 30 regular papers presented were carefully reviewed and selected from a total of 75 submissions. The conference program included a Master Class on the topic "Explanation and Verification of Machine Learning Models".

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Learning and Intelligent Optimization, LION 12, held in Kalamata, Greece, in June 2018. The 28 full papers and 12 short papers presented have been carefully reviewed and selected from 62 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

This volume presents a compelling collection of state-of-the-art work in algorithmic computational biology, honoring the legacy of Professor Bernard M.E. Moret in this field. Reflecting the wide-ranging influences of Prof. Moret’s research, the coverage encompasses such areas as phylogenetic tree and network estimation, genome rearrangements, cancer phylogeny, species trees, divide-and-conquer strategies, and integer linear programming. Each self-contained chapter provides an introduction to a cutting-edge problem of particular computational and mathematical interest. Topics and features: addresses the challenges in developing accurate and efficient software for the NP-hard maximum likelihood phylogeny estimation problem; describes the inference of species trees, covering strategies to scale phylogeny estimation methods to large datasets, and the construction of taxonomic supertrees; discusses the inference of ultrametric distances from additive distance matrices, and the inference of ancestral genomes under genome rearrangement events; reviews different techniques for inferring evolutionary histories in cancer, from the use of chromosomal rearrangements to tumor phylogenetics approaches; examines problems in phylogenetic networks, including questions relating to discrete mathematics, and issues of statistical estimation; highlights how evolution can provide a framework within which to understand comparative and functional genomics; provides an introduction to Integer Linear Programming and its use in computational biology, including its use for solving the Traveling Salesman Problem. Offering an invaluable source of insights for computer scientists, applied mathematicians, and statisticians, this illuminating volume will also prove useful for graduate courses on computational biology and bioinformatics.

This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. The chapters that follow present five generally applicable hybridization strategies, with exemplary case studies on selected problems: incomplete solution representations and decoders; problem instance reduction; large neighborhood search; parallel non-independent construction of solutions within metaheuristics; and hybridization based on complete solution archives. The authors are among the leading researchers in the hybridization of metaheuristics with other techniques for optimization, and their work reflects the broad shift to problem-oriented rather than algorithm-oriented approaches, enabling faster and more effective implementation in real-life applications. This hybridization is not restricted to different variants of metaheuristics but includes, for example, the combination of mathematical programming, dynamic programming, or constraint programming with metaheuristics, reflecting cross-fertilization in fields such as optimization, algorithmics, mathematical modeling, operations research, statistics, and simulation. The book is a valuable introduction and reference for researchers and graduate students in these domains.

This edited book addresses the challenges in managing the operations and supply chain of organizations in the era of internet of things and Industry 4.0. It presents cutting edge research on real world operations related problems, in-depth analyses, and relevant managerial implications. Wide variety of solution approaches such as quantitative, quantitative, and simulations are presented in the context of managing the operations and supply chains. Consisting of selected papers from the XXIII Annual International Conference of Society of Operations Management, this volume is part of a two volume series with the other book consisting of chapters on quantitative decision making. This edited book covers various quantitative models on operations and supply chain management such as inventory optimization, machine learning-operations research integrated model for healthcare systems, game-theoretic analysis of review strategies in truthful information sharing, design of contracts in supply chains, supply chain optimization, inventory routing, and shop floor scheduling. In addition to the quantitative models, several innovative heuristics are proposed for different problems. This book explores qualitative models on improving the performance of small and medium enterprises and petroleum industries and a simulation model for staff allocation in the information technology industry. Finally, this book provides review articles on vaccine supply chains and behavioral operations management. The book throws light on the emerging trends in the use of analytics, optimization, and simulation tools and empirical analysis to improve the performance of operations and supply chains of organizations. It will serve as an essential resource for practitioners, students, faculty members and scholars in operations management and related areas to gain knowledge and pursue high quality research on developments in areas such as managing the resource management and the solution methodology---innovative tools employed in addressing the real world problems and the different optimization techniques.