281
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

A comprehensive empirical demonstration of the impact of choice constraints on solving generalizations of the 0–1 knapsack problem using the integer programming option of CPLEX®

&
Pages 1632-1644 | Received 19 Nov 2018, Accepted 19 Aug 2019, Published online: 11 Sep 2019
 

Abstract

This paper provides comprehensive empirical and analytical evidence of the impact of choice constraints on two important categories of knapsack problems by using the integer programming option of CPLEX®. Specifically, if choice constraints are added to the 270 multidimensional knapsack problems (MKPs) in Beasley's OR-Library (used widely for comparing MKP heuristics), then the time needed to solve these problems with CPLEX® version 12 is reduced by more than 99.97%. Similar results are demonstrated for the 810 multi-demand multidimensional knapsack problems (MDMKPs) in Beasley's OR-Library. Additionally, using these 270 MKP and 810 MDMKP problem instances, it is shown that even if only some of the variables have choice constraints imposed on them, the CPLEX solution times are drastically reduced. These results provide motivation for operations research practitioners to check whether choice constraints are applicable (even if only on some of the variables) when solving real-world problems involving generalizations of the 0–1 knapsack problem.

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,161.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.