EconPapers    
Economics at your fingertips  
 

A multi-criteria approach to approximate solution of multiple-choice knapsack problem

Ewa M. Bednarczuk (), Janusz Miroforidis () and Przemysław Pyzel ()
Additional contact information
Ewa M. Bednarczuk: Systems Research Institute, Polish Academy of Sciences
Janusz Miroforidis: Systems Research Institute, Polish Academy of Sciences
Przemysław Pyzel: Systems Research Institute, Polish Academy of Sciences

Computational Optimization and Applications, 2018, vol. 70, issue 3, No 10, 889-910

Abstract: Abstract We propose a method for finding approximate solutions to multiple-choice knapsack problems. To this aim we transform the multiple-choice knapsack problem into a bi-objective optimization problem whose solution set contains solutions of the original multiple-choice knapsack problem. The method relies on solving a series of suitably defined linearly scalarized bi-objective problems. The novelty which makes the method attractive from the computational point of view is that we are able to solve explicitly those linearly scalarized bi-objective problems with the help of the closed-form formulae. The method is computationally analyzed on a set of large-scale problem instances (test problems) of two categories: uncorrelated and weakly correlated. Computational results show that after solving, in average 10 scalarized bi-objective problems, the optimal value of the original knapsack problem is approximated with the accuracy comparable to the accuracies obtained by the greedy algorithm and an exact algorithm. More importantly, the respective approximate solution to the original knapsack problem (for which the approximate optimal value is attained) can be found without resorting to the dynamic programming. In the test problems, the number of multiple-choice constraints ranges up to hundreds with hundreds variables in each constraint.

Keywords: Knapsack; Multi-objective optimization; Multiple-choice knapsack; Linear scalarization (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10589-018-9988-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:70:y:2018:i:3:d:10.1007_s10589-018-9988-z

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-018-9988-z

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:coopap:v:70:y:2018:i:3:d:10.1007_s10589-018-9988-z