A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem. / Greistorfer, Peter; Stanek, Rostislav; Maniezzo, Vittorio.
Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings. Vol. 13838 1. ed. 2023. p. 544-553 (Lecture Notes in Computer Science; Vol. 13838).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Greistorfer, P, Stanek, R & Maniezzo, V 2023, A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem. in Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings. 1 edn, vol. 13838, Lecture Notes in Computer Science, vol. 13838, pp. 544-553.

APA

Greistorfer, P., Stanek, R., & Maniezzo, V. (2023). A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings (1 ed., Vol. 13838, pp. 544-553). (Lecture Notes in Computer Science; Vol. 13838).

Vancouver

Greistorfer P, Stanek R, Maniezzo V. A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem. In Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings. 1 ed. Vol. 13838. 2023. p. 544-553. (Lecture Notes in Computer Science).

Author

Greistorfer, Peter ; Stanek, Rostislav ; Maniezzo, Vittorio. / A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem. Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings. Vol. 13838 1. ed. 2023. pp. 544-553 (Lecture Notes in Computer Science).

Bibtex - Download

@inproceedings{23f2dd12ba274a58939b124d57e421db,
title = "A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem",
abstract = "This work treats the so-called Generalized Quadratic Assignment Problem. Solution methods are based on heuristic and partially LP-optimizing ideas. Base constructive results stem from a simple 1-pass heuristic and a tree-based branch-and-bound type approach. Then we use a combination of Tabu Search and Linear Programming for the improving phase. Hence, the overall approach constitutes a type of mat- and metaheuristic algorithm. We evaluate the different algorithmic designs and report computational results for a number of data sets, instances from literature as well as own ones. The overall algorithmic performance gives rise to the assumption that the existing framework is promising and worth to be examined in greater detail.",
keywords = "Generalized Quadratic Assignment, Matheuristic, Metaheuristic, Linear Programming, Tabu Search",
author = "Peter Greistorfer and Rostislav Stanek and Vittorio Maniezzo",
year = "2023",
month = feb,
day = "23",
language = "English",
isbn = "0302-9743",
volume = "13838",
series = "Lecture Notes in Computer Science",
publisher = "Springer Cham",
pages = "544--553",
booktitle = "Metaheuristics",
edition = "1",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - A Tabu Search Matheuristic for the Generalized Quadratic Assignment Problem

AU - Greistorfer, Peter

AU - Stanek, Rostislav

AU - Maniezzo, Vittorio

PY - 2023/2/23

Y1 - 2023/2/23

N2 - This work treats the so-called Generalized Quadratic Assignment Problem. Solution methods are based on heuristic and partially LP-optimizing ideas. Base constructive results stem from a simple 1-pass heuristic and a tree-based branch-and-bound type approach. Then we use a combination of Tabu Search and Linear Programming for the improving phase. Hence, the overall approach constitutes a type of mat- and metaheuristic algorithm. We evaluate the different algorithmic designs and report computational results for a number of data sets, instances from literature as well as own ones. The overall algorithmic performance gives rise to the assumption that the existing framework is promising and worth to be examined in greater detail.

AB - This work treats the so-called Generalized Quadratic Assignment Problem. Solution methods are based on heuristic and partially LP-optimizing ideas. Base constructive results stem from a simple 1-pass heuristic and a tree-based branch-and-bound type approach. Then we use a combination of Tabu Search and Linear Programming for the improving phase. Hence, the overall approach constitutes a type of mat- and metaheuristic algorithm. We evaluate the different algorithmic designs and report computational results for a number of data sets, instances from literature as well as own ones. The overall algorithmic performance gives rise to the assumption that the existing framework is promising and worth to be examined in greater detail.

KW - Generalized Quadratic Assignment

KW - Matheuristic

KW - Metaheuristic

KW - Linear Programming

KW - Tabu Search

UR - https://link.springer.com/book/10.1007/978-3-031-26504-4

M3 - Conference contribution

SN - 0302-9743

VL - 13838

T3 - Lecture Notes in Computer Science

SP - 544

EP - 553

BT - Metaheuristics

ER -