Combining Constraint Programming with Ant Colony Optimization to Solve Sports Scheduling Problem.

• Main Authors: Cheng-yu Tsai, 蔡政諭
• Other Authors: Huey-Kuo Chen
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/78517759144826432132

碩士 === 國立中央大學 === 土木工程研究所 === 99 === The arrangement of sports scheduling for each sport scheduling planner is a time consuming work. In this research, a constraint satisfaction problem (CSP) of Major League Baseball (MLB) scheduling with real world considerations is represented. The objective value of the proposed formulation is to minimize total travel distance of all teams in MLB. Since the sport scheduling problem is NP-complete, the CSP of MLB is hard to solve with consideration of rules of regulations of MLB. A combined algorithm which composed by constraint programming (CP) and ant colony system is proposed to solve the CSP of sport scheduling. Three different types of sports scheduling problem are tested by using the combined algorithm. The first one is MLB Scheduling problem, the second is Chinese Professional Baseball League Problem and the third is Belgian Soccer League scheduling. The test results show that the combined algorithm is advantageous over the current status of MLB with respect to the objective value.