An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals
| dc.cclicence | CC-BY-NC-ND | en |
| dc.contributor.author | Wang, Zhen | |
| dc.contributor.author | Zhang, Jihui | |
| dc.contributor.author | Yang, Shengxiang | |
| dc.date.acceptance | 2019-10-11 | |
| dc.date.accessioned | 2019-10-25T10:00:39Z | |
| dc.date.available | 2019-10-25T10:00:39Z | |
| dc.date.issued | 2019-10-18 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | Random job arrivals that happen frequently in manufacturing practice may create a need for dynamic scheduling. This paper considers an issue of how to reschedule the randomly arrived new jobs to pursue both performance and stability in a job shop. Firstly, a mixed integer programming model is established to minimize three objectives, including the discontinuity rate of new jobs during the processing, the makespan deviation of initial schedule, and the sequence deviation on machines. Secondly, four match-up strategies from references are modified to determine the rescheduling horizon. Once new jobs arrive, the rescheduling process is immediately triggered with ongoing operations remain. The ongoing operations are treated as machine unavailable constraints (MUC) in the rescheduling horizon. Then, a particle swarm optimization (PSO) algorithm with improvements is proposed to solve the dynamic job shop scheduling problem. Improvement strategies consist of a modified decoding scheme considering MUC, a population initialization approach by designing a new transformation mechanism, and a novel particle movement method by introducing position changes and a random inertia weight. Lastly, extensive experiments are conducted on several instances. The experiments results show that the modified rescheduling strategies are statistically and significantly better than the compared strategies. Moreover, comparative studies with five variants of PSO algorithm and three state-of-the-art meta-heuristics demonstrate the high performance of the improved PSO algorithm. | en |
| dc.funder | Other external funder (please detail below) | en |
| dc.funder.other | National Natural Science Foundation of China | en |
| dc.identifier.citation | Zhang, J., Wang, Z. and Yang, S. (2019) An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals. Swarm and Evolutionary Computation, 51, 100594 | en |
| dc.identifier.doi | https://doi.org/10.1016/j.swevo.2019.100594 | |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/18660 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | 1673228, 61703220 and 61402216 | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | New job arrival | en |
| dc.subject | Dynamic job shop scheduling | en |
| dc.subject | Match-up strategy | en |
| dc.subject | Particle swarm optimization | en |
| dc.subject | Multi-objective problem | en |
| dc.title | An improved particle swarm optimization algorithm for dynamic job shop scheduling problems with random job arrivals | en |
| dc.type | Article | en |