Matheuristics Hybridizing Metaheuristics and Mathematical Programming
Matheuristics Hybridizing Metaheuristics and Mathematical Programming by Vittorio Maniezzo, published by Springer US in 2009, is a comprehensive exploration of the intersection between metaheuristics and mathematical optimization. This 270-page edition, written in English, delves into how metaheuristics can aid managers in decision-making by providing robust tools for high-quality solutions across various fields, including business, engineering, and economics.
Readers will find a collection of invited reviews and refereed papers from the second Matheuristics workshop held in Bertinoro, Italy, in June 2008. The book discusses mathematical programming techniques within metaheuristic frameworks, emphasizing recent advancements in Mixed Integer Programming and its application to real-world challenges. Topics include dual information, metaheuristics for stochastic problems, and case histories showcasing successful applications, making this work a valuable resource for those interested in optimization and operations research.
Official synopsis Publisher
Metaheuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics, and science in reasonable time frames, but finding exact solutions in these applications still poses a real challenge. However, because of advances in the fields of mathematical optimization and metaheuristics, major efforts have been made on their interface regarding efficient hybridization.
This edited book will provide a survey of the state of the art in this field by providing some invited reviews by well-known specialists as well as refereed papers from the second Matheuristics workshop to be held in Bertinoro, Italy, June 2008. Papers will explore mathematical programming techniques in metaheuristics frameworks, and especially focus on the latest developments in Mixed Integer Programming in solving real-world problems.
Topics to be covered will also include dual information and metaheuristics; metaheuristics for stochastic problems; MIP solvers as search components; decompositions and lower/upper bounds in metaheuristics/MIP codes (MH codes); and real-world case histories of successful MH applications.
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