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 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.

Automated summary

This research used the 2016 MLB season as a case study and proposed the Variable Neighborhood Search algorithm with different coding structures to optimize sports scheduling. The algorithm successfully reduced the total travelling distances of all teams in the league, showing promising performance in real-world cases.