Hybrid Harmony Search Combined with Variable Neighborhood Search for the Traveling Tournament Problem

Khelifa, Meriem; Boughaci, Dalila

doi:10.1007/978-3-319-45243-2_48

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…In principle, many aspects are considered 2 of 18 in various applications. As in the case of the travelling tournament problem (TTP), the real-life situation for Major League Baseball aims to optimize the regular-season schedule at the minimum total travelled distance [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Similarly, with the travelling umpire problem (TUP), the primary objective is to minimize umpire travel [21][22][23][24][25][26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Biajoli and Lorena [51] combined the clustering search approach and Variable Neighborhood Search (VNS). Khelifa and Boughaci [15,16] also proposed a VNS and its hybrid form to solve the optimal minimum travelling distance. Their later attempt focused on the mirrored version of the travelling tournament problem with reversed venues and proposed a VNS with Harmony Search (V-HS).…”

Section: Literature Reviewmentioning

confidence: 99%

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Variable Neighborhood Search for Major League Baseball Scheduling Problem

et al. 2021

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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.

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