Variable Neighborhood Search for Major League Baseball Scheduling Problem
Modern society pays more and more attention to leisure activities, and watching sports is one of the most popular activities for people. In professional leagues, sports scheduling plays a very critical role. To efficiently arrange a schedule while complying with the relevant rules in a sports league...
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Online Access: | https://www.mdpi.com/2071-1050/13/7/4000 |
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doaj-88681ca369984f5b8e6c4f0309c7a4142021-04-03T23:01:37ZengMDPI AGSustainability2071-10502021-04-01134000400010.3390/su13074000Variable Neighborhood Search for Major League Baseball Scheduling ProblemYun-Chia Liang0Yen-Yu Lin1Angela Hsiang-Ling Chen2Wei-Sheng Chen3Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, TaiwanDepartment of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, TaiwanDepartment of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, TaiwanDepartment of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, TaiwanModern society pays more and more attention to leisure activities, and watching sports is one of the most popular activities for people. In professional leagues, sports scheduling plays a very critical role. To efficiently arrange a schedule while complying with the relevant rules in a sports league has become a challenge for schedule planners. This research uses Major League Baseball (MLB) of the year 2016 as a case study. The study proposed the Variable Neighborhood Search (VNS) algorithm with different coding structures to optimize the objective function—minimize the total travelling distance of all teams in the league. We have compared the algorithmic schedules with the 2016 and 2019 MLB regular-season schedules in the real-world case for its performance evaluation. The results have confirmed success in reducing the total travelling distances by 2.48% for 2016 and 6.02% in 2019 while lowering the standard deviation of total travelling distances by 7.06% for 2016.https://www.mdpi.com/2071-1050/13/7/4000sports schedulingmetaheuristicsoptimizationMajor League Baseball |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yun-Chia Liang Yen-Yu Lin Angela Hsiang-Ling Chen Wei-Sheng Chen |
spellingShingle |
Yun-Chia Liang Yen-Yu Lin Angela Hsiang-Ling Chen Wei-Sheng Chen Variable Neighborhood Search for Major League Baseball Scheduling Problem Sustainability sports scheduling metaheuristics optimization Major League Baseball |
author_facet |
Yun-Chia Liang Yen-Yu Lin Angela Hsiang-Ling Chen Wei-Sheng Chen |
author_sort |
Yun-Chia Liang |
title |
Variable Neighborhood Search for Major League Baseball Scheduling Problem |
title_short |
Variable Neighborhood Search for Major League Baseball Scheduling Problem |
title_full |
Variable Neighborhood Search for Major League Baseball Scheduling Problem |
title_fullStr |
Variable Neighborhood Search for Major League Baseball Scheduling Problem |
title_full_unstemmed |
Variable Neighborhood Search for Major League Baseball Scheduling Problem |
title_sort |
variable neighborhood search for major league baseball scheduling problem |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-04-01 |
description |
Modern society pays more and more attention to leisure activities, and watching sports is one of the most popular activities for people. In professional leagues, sports scheduling plays a very critical role. To efficiently arrange a schedule while complying with the relevant rules in a sports league has become a challenge for schedule planners. This research uses Major League Baseball (MLB) of the year 2016 as a case study. The study proposed the Variable Neighborhood Search (VNS) algorithm with different coding structures to optimize the objective function—minimize the total travelling distance of all teams in the league. We have compared the algorithmic schedules with the 2016 and 2019 MLB regular-season schedules in the real-world case for its performance evaluation. The results have confirmed success in reducing the total travelling distances by 2.48% for 2016 and 6.02% in 2019 while lowering the standard deviation of total travelling distances by 7.06% for 2016. |
topic |
sports scheduling metaheuristics optimization Major League Baseball |
url |
https://www.mdpi.com/2071-1050/13/7/4000 |
work_keys_str_mv |
AT yunchialiang variableneighborhoodsearchformajorleaguebaseballschedulingproblem AT yenyulin variableneighborhoodsearchformajorleaguebaseballschedulingproblem AT angelahsianglingchen variableneighborhoodsearchformajorleaguebaseballschedulingproblem AT weishengchen variableneighborhoodsearchformajorleaguebaseballschedulingproblem |
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1721543461539151872 |