Adaptive mechanism for schedule arrangement and optimization in socially-empowered professional sports games
World financial crisis has caused a great impact to our daily lives. The price reflects the difficulty not only to transportation but finance status. In this paper, an adaptive scheduling algorithm for professional sports games was proposed, which greatly improved the performance of conventional game-match scheduling results by hybridizing the Tabu Search algorithm and Genetics algorithm. The purpose of this work is to reduce the travelling cost of all teams. The information of famous sports league (e.g. NBA and MLB) was adopted as preliminary experiment data. Using the new method proposed, it is efficient to find better results than approaches developed before. In addition to finding a feasible schedule that meets all the timing restrictions, the problem addressed in this paper has the extra complexity of having the objective of minimizing the travel costs and every team has the balancing number of the games in home. We formalize the scheduling problem into an optimization problem and adopt the concept of evolution strategy, with consideration of sequential events in a socially world, to solve the challenging issue.
sports scheduling recommender system schedule optimization social search