Devising a cost effective baseball scheduling by evolutionary algorithms

We discuss the scheduling problems of a sports league and propose a new approach to solve these problems by applying evolution strategy. A schedule in a sports league must satisfy many constraints on timing, such as the number of games played between every pair of teams, the bounds on the number of consecutive home (or away) games for each team, every pair of teams must have played each other in the first half of the season, and so on. In addition to finding a feasible schedule that meets all the timing restrictions, the problem addressed has the additional complexity of having the objective of minimizing travel costs and every team having a balanced number of games at home. We formalize the scheduling problem into an optimization problem and adopt the concept of evolution strategy to solve it. We define the travel cost and distance cost for teams in the sports league by referring to Major League Baseball (MLB) in the United States and focus on the scheduling problem in MLB. Using the new method, it is more efficient at finding better results than previous approaches.