Abstract
Particle Swarm Algorithm for Improved Handling of the Mirrored Traveling Tournament Problem
Highlights
Traveling tournament problems (TTPs) constitute an important class of non-deterministic polynomial-time (NP)-hard problems that have been thoroughly studied [14]
In this study, we used a particle swarm optimization (PSO) algorithm to address a variation of the non-deterministic polynomial-time NP-hard traveling tournament problem, which determines the optimal schedule for a double round-robin tournament, for an even number of teams, to minimize the number of trips taken
The TTP was initially introduced by Easton et al [18] for scheduling Major League Baseball seasons, and they [19] subsequently proposed a hybrid branch-and-price algorithm, based on lower bounds that were set as the sum of minimum travel distances for each team
Summary
Traveling tournament problems (TTPs) constitute an important class of non-deterministic polynomial-time (NP)-hard problems that have been thoroughly studied [14]. The TTP was initially introduced by Easton et al [18] for scheduling Major League Baseball seasons, and they [19] subsequently proposed a hybrid branch-and-price algorithm, based on lower bounds that were set as the sum of minimum travel distances for each team. Another effective heuristic in sports scheduling was constraint programming, implemented by Henz et al [20], who used activated propagator libraries to stabilize and perform the required search strategies. Often used in literature and real-world tournaments alike, is that no team may play more than three consecutive home or away games. A team with an away game followed by a home game travels home: Team A Team B Team C Team D
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