Matheuristics

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.

Presents the latest research on using mathematical programming in metaheuristics frameworks Represents a major advance in complex problem-solving methodologies Presents breakthroughs in using Mixed Integer Programming to solve real-world problems Includes supplementary material: sn.pub/extras

Klappentext

• Metaheuristics: Intelligent Problem Solving Marco Caserta and Stefan Voß

• Just MIP it!

  Matteo Fischetti, Andrea Lodi, and Domenico Salvagnin

• MetaBoosting: Enhancing Integer Programming Techniques by Metaheuristics

  Jakob Puchinger, Günther R. Raidl, and Sandro Pirkwieser

• Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms

  Irina Dumitrescu and Thomas Stützle

• Decomposition Techniques as Metaheuristic Frameworks

  Marco Boschetti, Vittorio Maniezzo, and Matteo Roffilli

• Convergence Analysis of Metaheuristics

  Walter J. Gutjahr

• MIP-based GRASP and Genetic Algorithm for Balancing Transfer Lines

  Alexandre Dolgui, Anton Eremeev, and Olga Guschinskaya

• (Meta-)Heuristic Separation of Jump Cuts in a Branch & Cut Approach for the Bounded Diameter Minimum Spanning Tree Problem

  Martin Gruber and Günther R. Raidl

• A Good Recipe for Solving MINLPs

  Leo Liberti, Giacomo Nannicini, and Nenad Mladenovic

• Variable Intensity Local Search

  Sneana Mitrovic-Minic and Abraham P. Punnen

• A Hybrid Tabu Search for the m-Peripatetic Vehicle Routing Problem

  Sandra Ulrich Ngueveu, Christian Prins, and Roberto Wolfer Calvo

  Inhalt
  Metaheuristics: Intelligent Problem Solving.- Just MIP it#x0021;.- MetaBoosting: Enhancing Integer Programming Techniques by Metaheuristics.- Usage of Exact Algorithms to Enhance Stochastic Local Search Algorithms.- Decomposition Techniques as Metaheuristic Frameworks.- Convergence Analysis of Metaheuristics.- MIP-based GRASP and Genetic Algorithm for Balancing Transfer Lines.- (Meta-)Heuristic Separation of Jump Cuts in a Branch#x0026;Cut Approach for the Bounded Diameter Minimum Spanning Tree Problem.- A Good Recipe for Solving MINLPs.- Variable Intensity Local Search.- A Hybrid Tabu Search for the -Peripatetic Vehicle Routing Problem.