Improved heuristics for finding balanced teams

Daniel Solow, Jie Ning, Jieying Zhu and Yishen Cai

IISE Transactions, 2020, vol. 52, issue 12, 1312-1323

Abstract: This research addresses the problem of dividing a group of people into a collection of teams that need to be “balanced” across a variety of different attributes. This type of problem arises, for example, in an academic setting where it is necessary to partition students into a number of balanced study teams and also in a youth camp in which children need to be formed into sports teams that are competitive with each other. Recent work has resulted in both linear and nonlinear integer programing models for solving this problem. In the research here, improvements to the models are made together with a linear approximation to the nonlinear objective function that significantly reduce the number of integer variables and constraints. Computational experiments are performed on random instances of the problem, as well as on instances for which there are almost perfectly balanced teams, the latter providing a way to determine the quality of the optimal solution obtained by the heuristics. These tests show that the approach developed here almost always obtain better balanced teams than those from prior research.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2020.1732506 (text/html)
Access to full text is restricted to subscribers.

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:taf:uiiexx:v:52:y:2020:i:12:p:1312-1323

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2020.1732506

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().