An ACO Algorithm with a Job-Sequence-Based Chromosome Representation for Scheduling Distributed Flexible Job Shops

• Main Authors: Tan, Hao, 譚浩
• Other Authors: Wu, Muh-Cherng
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/24375908712350106618

碩士 === 國立交通大學 === 工業工程與管理學系 === 100 === This research examines a distributed and flexible job shops scheduling problem (DFJSP). With NP-hard in complexity, the DFJSP problem involves three sub-decisions: (1) job-to-cell assignment, (2) operation sequencing, and (3) operation-to-machine assignment. Several meta-heuristic algorithms have been proposed to solve the DFJSP problem. This research develops a new solution representation (called Sjob), which is for modeling a particular sequence of jobs. Given such a job sequence, heuristic rules are used to derive the three scheduling sub-decisions. Based on Snew, this research adopts the existing algorithmic architecture of ant colony optimization (ACO) and developed an algorithm (called ACO_Sjob) to solve the DFJSP problem. Experiment results indicate that ACO_Sjob outperforms prior meta-heuristic algorithms in literature.