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Published in:
The Generalized Assignment Problem Read chapter Read first chapter

2021 | OriginalPaper | Chapter

5. Diving Heuristics

Authors : Vittorio Maniezzo, Marco Antonio Boschetti, Thomas Stützle

Published in: Matheuristics

Publisher: Springer International Publishing

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Abstract

Diving heuristics are methods that progressively enlarge a partial solution up to its possible completion, thus ˇjump˘into a solution with no way back. While this is common to all constructive heuristics, diving ones are usually characterized by working on the mathematical formulation of the problem to solve. Some contributions of this type showed remarkably effective, and are included as standard components of general MIP solvers. This chapter reviews two well-known diving heuristics, namely Relaxation Induced Neighborhood Search (RINS) and Local Branching, and outlines a possible specification to the Generalized Assignment Problem.

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previous chapter Population-Based Metaheuristics
next chapter Very Large-Scale Neighborhood Search
Footnotes
1
We remind that in the computational traces the decision variables are indexed from 0.
 
Literature
Bixby RE, Fenelon M, Gu Z, Rothberg E, Wunderling R (2000) MIP: Theory and practice – closing the gap. Kluwer Academic Publishers, pp 19–49
Danna E, Rothberg E, Pape C (2005) Exploring relaxation induced neighborhoods to improve MIP solutions. Math Program 102(1):71–90CrossRef
Fischetti M, Lodi A (2003) Local branching. Math Program B 98(1–3):23–47CrossRef
Fischetti M, Glover F, Lodi A (2005) The feasibility pump. Math Program 104(1):91–104CrossRef
Pigatti A, Poggi de Aragão M, Uchoa E (2005) Stabilized branch-and-cut-and-price for the generalized assignment problem. Electron Notes Discrete Math 19:389–395CrossRef
Metadata
Title
Diving Heuristics
Authors
Vittorio Maniezzo
Marco Antonio Boschetti
Thomas Stützle
Copyright Year
2021
Book
Matheuristics

Print ISBN: 978-3-030-70276-2

Electronic ISBN: 978-3-030-70277-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-70277-9_5