Skip to contents

R package qap - Heuristics for the Quadratic Assignment Problem (QAP)

Package on CRAN CRAN RStudio mirror downloads Licenser-universe status

Maintainer: Michael Hahsler

Introduction

This package implements heuristics for the Quadratic Assignment Problem (QAP). The QAP was introduced as a facility location problem in operations research (Koopmans and Beckmann, 1957). It also has applications in data analysis, including cluster analysis and seriation (Hubert and Schultz, 1976).

The problem is NP-hard, and the package implements the simulated annealing heuristic described in Burkard and Rendl (1984).

The following R packages use qap: seriation

To cite package ‘qap’ in publications use:

Hahsler M (2022). qap: Heuristics for the Quadratic Assignment Problem (QAP). doi:10.32614/CRAN.package.qap https://doi.org/10.32614/CRAN.package.qap. R package version 0.1-2, https://CRAN.R-project.org/package=qap.

@Manual{,
  title = {qap: Heuristics for the Quadratic Assignment Problem (QAP)},
  author = {Michael Hahsler},
  year = {2022},
  note = {R package version 0.1-2},
  url = {https://CRAN.R-project.org/package=qap},
  doi = {10.32614/CRAN.package.qap},
}

Installation

Stable CRAN version: Install from within R with

Current development version: Install from r-universe.

install.packages("qap",
    repos = c("https://mhahsler.r-universe.dev",
              "https://cloud.r-project.org/"))

Usage

The package contains a copy of the problem instances and solutions from QAPLIB. We load the had20 QAPLIB problem. It contains flow and distance matrices, a known optimal solution, and its objective value.

library(qap)
set.seed(1000)

p <- read_qaplib(system.file("qaplib", "had20.dat", package = "qap"))
p$solution
##  [1]  8 15 16 14 19  6  7 17  1 12 10 11  5 20  2  3  4  9 18 13
p$opt
## [1] 6922

We run the simulated annealing heuristic 10 times and use the best solution.

a <- qap(p$A, p$B, rep = 10)
a
##  [1]  8 15 16 14 19  6  7 12  1 11 10  5  3 20  2 17  4  9 18 13
## attr(,"obj")
## [1] 6926

Compare the solution with the known optimum (percentage above optimum).

(attr(a, "obj") - p$opt)/p$opt * 100
## [1] 0.058

References