Using Artificial Intelligence Algorithms for the Health Examination Scheduling Problems

• Main Authors: Bing-Feng Cheng, 程秉逢
• Other Authors: 謝益智
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/fw28q9

碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 103 === In recent years, along with concept of “prevention is better than cure” for health, more people will take health examination every year. It is an important issue for health examination center to arrange the health examination schedule for all customers such that the makespan (i.e., total time in the health examination center) is minimized. This health examination scheduling problem is an extension of the typical open shop scheduling problem. Since the typical open shop scheduling problem is an NP-hard problem, therefore this considered health examination scheduling problem is also an NP-hard problem. It means that it is time consuming to solve when the traditional methods are used for the larger health examination scheduling problem. Generally speaking, artificial intelligence algorithm is one of the useful methods to solve the complicated problem currently. Although artificial intelligence algorithm cannot promise to obtain the optimal solution of the health examination scheduling problem but it can find near optimal solution effectively. Therefore, in this thesis we attempt to adopt artificial intelligence algorithms to solve the health examination scheduling problem. This research applies three artificial intelligence algorithms, include genetic algorithm, immune algorithm and particle swarm optimization algorithm, to solve the health examination scheduling problem for the minimizing the makespan. Numerical results of test problems show that immune algorithm is more effective than the other two algorithms, and particle swarm optimization algorithm is faster than the other two algorithms.