Metaheuristics for the 2-stage 3-machine Assembly Scheduling Problem with Sum of Processing Times of Already Processed-jobs Learning Effect

• Main Author: 陳家揚
• Other Authors: Chin-Chia Wu
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/dm2ap5

碩士 === 逢甲大學 === 統計學系統計與精算碩士班 === 106 === Researchers claim that most of productive items in manufacturing systems may be formulated a two-stage assembly scheduling model. Meanwhile, the literature releases that the cumulated learning experience is neglect in solving a two-stage assembly scheduling problem. In fact, the sum-of-processing-times-based learning model is pertinent to process manufacturing in which an initial setup is often followed by a lengthy uninterrupted production process. Motivated by this observation, this study introduces the 2-stage 3-machine assembly problem with sum of processing times of already processed jobs learning to minimize the makespan criterion. This model assumes that there are two machines at the first stage and an assembly machine at the second stage. Due to the fact that this problem is an NP-hard one, this study first provides some dominances and a lower bound applied in the branch-and-bound method for an optimal solution. Three heuristics modified from Johnson’s rule with and without improving by an interchange pairwise method then are separately applied in three versions of genetic algorithm and three versions of cloud theory-based simulated annealing algorithm for find near-optimal solutions. Finally, the statistical results of all proposed algorithms are evaluated and reported.