A mixed-integer linear programming approach to constructing sporting fixtures with minimal inequity

Ideally, sporting fixtures should be equitable to all teams. This typically means each team plays all other opponents an equal number of times, and other inherent inequities such as a home-field advantage are shared equally across the fixture. However, in some instances this is not possible. Time constraints may prevent teams from playing all other teams an equal number of times, while venue constraints may only permit a subset of the teams to play in each round. Challenges such as these are considered, and a mixed-integer linear programming formulation which takes such issues into account is proposed. Two examples are given which demonstrate that the proposed formulation can be solved to provable optimality in tractable time on modern machines, while heuristic approaches could be used to rapidly obtain high-quality solutions if needed.

Authors

Institutions

Publication Details

Published
2024-12-03
DOI
https://doi.org/10.5149/ms.1292
Primary Topic
Assembly Line Balancing Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A mixed-integer linear programming approach to constructing sporting fixtures with minimal inequity

Michael Haythorpe
Assembly Line Balancing Optimization
article

A mixed-integer linear programming approach to constructing sporting fixtures with minimal inequity

Michael Haythorpe
article en

Abstract

Ideally, sporting fixtures should be equitable to all teams. This typically means each team plays all other opponents an equal number of times, and other inherent inequities such as a home-field advantage are shared equally across the fixture. However, in some instances this is not possible. Time constraints may prevent teams from playing all other teams an equal number of times, while venue constraints may only permit a subset of the teams to play in each round. Challenges such as these are considered, and a mixed-integer linear programming formulation which takes such issues into account is proposed. Two examples are given which demonstrate that the proposed formulation can be solved to provable optimality in tractable time on modern machines, while heuristic approaches could be used to rapidly obtain high-quality solutions if needed.

Vol. 6(1)
Flinders University (AU)
Openalex Percentile: Top 56%
Assembly Line Balancing Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.