A single machine learning effect scheduling problem with release times

• Main Authors: Peng-Hsiang Hsu, 徐鵬翔
• Other Authors: Wen-Chiung Lee
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/91317619738545721894

碩士 === 逢甲大學 === 統計與精算所 === 96 === Recently learning effects in scheduling have received considerable attention in the literature. Moreover, most researchers assume that the jobs are available at all times. The learning scheduling problems with release times are then very limited. This paper investigates a single-machine scheduling with a job-position learning model of release times where its objective is to minimize the makespan. The problem has been proven an NP-hard problem. Therefore, we will firstly apply a branch-and-bound algorithm incorporating with developing some powerful dominance properties and lower bounds for the optimal solution for the problem, and we then provide an efficient heuristic algorithm to obtain the near-optimal solution. Finally, we also conduct the computational experiments to further indicate that the branch-and-bound algorithm can solve most instances up to job size 36 within a reasonable amount of time, and the worst case of the heuristic is only with the maximum of the average error percentage of less than 0.11%.