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.
Original languageEnglish
Title of host publicationMetaheuristics
Subtitle of host publication14th International Conference, MIC 2022, Syracuse, Italy, July 11–14, 2022, Proceedings
PublisherSpringer Cham
Number of pages10
ISBN (Electronic)1611-3349
ISBN (Print)0302-9743
Publication statusPublished - 23 Feb 2023

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Generalized Quadratic Assignment
  • Matheuristic
  • Metaheuristic
  • Linear Programming
  • Tabu Search

Cite this