Metaheuristics for Big Data

This book describes the main classical combinatorial problems that can be encountered when designing a logistics network or driving a supply chain. It shows how these problems can be tackled by metaheuristics, both separately and using an integrated approach. A huge number of techniques, from the simplest to the most advanced ones, are given for helping the reader to implement efficient solutions that meet its needs. A lot of books have been written about metaheuristics (methods for solving hard optimization problems) and supply chain management (the field in which we find a huge number of combinatorial optimization problems) in the last decades. So, the main reason of this book is to describe how these methods can be implemented for this class of problems.

This book is dedicated to the application of metaheuristic optimization in trajectory generation and control issues in robotics. In this area, as in other fields of application, the algorithmic tools addressed do not require a comprehensive list of eligible solutions to effectively solve an optimization problem. This book investigates how, by reformulating the problems to be solved, it is possible to obtain results by means of metaheuristics. Through concrete examples and case studies – particularly related to robotics – this book outlines the essentials of what is needed to reformulate control laws into concrete optimization data. The resolution approaches implemented – as well as the results obtained – are described in detail, in order to give, as much as possible, an idea of metaheuristics and their performance within the context of their application to robotics.

This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields. For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The second part is itself split into two, the first section being devoted to five multi-objective problems to which metaheuristics are adapted, while the second tackles various transportation problems related to the organization of production systems. Many real-world applications are presented by the authors, making this an invaluable resource for researchers and students in engineering, economics, mathematics and computer science. Contents 1. An Estimation of Distribution Algorithm for Solving Flow Shop Scheduling Problems with Sequence-dependent Family Setup Times, Mansour Eddaly, Bassem Jarboui, Radhouan Bouabda, Patrick Siarry and Abdelwaheb Reba?. 2. Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems, Imed Kacem. 3. A Hybrid GRASP-Differential Evolution Algorithm for Solving Flow Shop Scheduling Problems with No-Wait Constraints, Hanen Akrout, Bassem Jarboui, Patrick Siarry and Abdelwaheb Reba?. 4. A Comparison of Local Search Metaheuristics for a Hierarchical Flow Shop Optimization Problem with Time Lags, Emna Dhouib, Jacques Teghem, Daniel Tuyttens and Ta?cir Loukil. 5. Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search, Marie-El?onore Marmion. 6. Evolutionary Metaheuristic Based on Genetic Algorithm: Application to Hybrid Flow Shop Problem with Availability Constraints, Nadia Chaaben, Racem Mellouli and Faouzi Masmoudi. 7. Models and Methods in Graph Coloration for Various Production Problems, Nicolas Zufferey. 8. Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties, Mustapha Ratli, Rachid Benmansour, Rita Macedo, Sa?d Hanafi, Christophe Wilbaut. 9. Metaheuristics for Biobjective Flow Shop Scheduling, Matthieu Basseur and Arnaud Liefooghe. 10. Pareto Solution Strategies for the Industrial Car Sequencing Problem, Caroline Gagn?, Arnaud Zinflou and Marc Gravel. 11. Multi-Objective Metaheuristics for the Joint Scheduling of Production and Maintenance, Ali Berrichi and Farouk Yalaoui. 12. Optimization via a Genetic Algorithm Parametrizing the AHP Method for Multicriteria Workshop Scheduling, Fouzia Ounnar, Patrick Pujo and Afef Denguir. 13. A Multicriteria Genetic Algorithm for the Resource-constrained Task Scheduling Problem, Olfa Dridi, Saoussen Krichen and Adel Guitouni. 14. Metaheuristics for the Solution of Vehicle Routing Problems in a Dynamic Context, Tient? Hsu, Gilles Gon?alves and R?my Dupas. 15. Combination of a Metaheuristic and a Simulation Model for the Scheduling of Resource-constrained Transport Activities, Virginie Andr?, Nathalie Grangeon and Sylvie Norre. 16. Vehicle Routing Problems with Scheduling Constraints, Rahma Lahyani, Fr?d?ric Semet and Beno?t Trouillet. 17. Metaheuristics for Job Shop Scheduling with Transportation, Qiao Zhang, Herv? Manier, Marie-Ange Manier. About the Authors Bassem Jarboui is Professor at the University of Sfax, Tunisia. Patrick Siarry is Professor at the Laboratoire Images, Signaux et Syst?mes Intelligents (LISSI), University of Paris-Est Cr?teil, France. Jacques Teghem is Professor at the Universit

The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

This book will be a “must” for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.

Intelligence is defined by the ability to optimize, manage and reconcile the currents of physical, economic and even social flows. The strong constraint of immediacy proves to be an opportunity to imagine, propose and deliver solutions on the common basis of optimization techniques. Metaheuristics for Intelligent Electrical Networks analyzes the use of metaheuristics through independent applications but united by the same methodology.

Metaheuristic Algorithms in Maritime Operations Optimization focuses on the seaside and port side problems regarding the maritime transportation. The book reviews and introduces the most important problems regarding the shipping network design, long-term and short-term scheduling and planning problems in both bulk and container shipping as well as liquid maritime transportation. Application of meta heuristic algorithm is important for these problems, as most of them are hard and time-consuming to be solved optimally.

Air Traffic Management involves many different services such as Airspace Management, Air Traffic Flow Management and Air Traffic Control. Many optimization problems arise from these topics and they generally involve different kinds of variables, constraints, uncertainties. Metaheuristics are often good candidates to solve these problems. The book models various complex Air Traffic Management problems such as airport taxiing, departure slot allocation, en route conflict resolution, airspace and route design. The authors detail the operational context and state of art for each problem. They introduce different approaches using metaheuristics to solve these problems and when possible, compare their performances to existing approaches

This book is dedicated to metaheuristics as applied to vehicle routing problems. Several implementations are given as illustrative examples, along with applications to several typical vehicle routing problems. As a first step, a general presentation intends to make the reader more familiar with the related field of logistics and combinatorial optimization. This preamble is completed with a description of significant heuristic methods classically used to provide feasible solutions quickly, and local improvement moves widely used to search for enhanced solutions. The overview of these fundamentals allows appreciating the core of the work devoted to an analysis of metaheuristic methods for vehicle routing problems. Those methods are exposed according to their feature of working either on a sequence of single solutions, or on a set of solutions, or even by hybridizing metaheuristic approaches with others kind of methods.

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.