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Spatial scheduling strategy for irregular curved blocks based on the modified genetic ant colony algorithm (MGACA) in shipbuilding
- Source :
- International Journal of Production Research. 56:3099-3115
- Publication Year :
- 2017
- Publisher :
- Informa UK Limited, 2017.
-
Abstract
- This paper proposes a scheduling strategy for irregular curved blocks to address the complex spatiotemporal coupling scheduling problem related to the entered time, the entered sequence, the setting positions and the rotated angles for the curved blocks in a shipbuilding yard. The strategy presents a makespan-based curved blocks – classification and selection rule to fulfil the programming time for the entry of the curved blocks into the workplace and realises the suppression on the delay. Useless stepping search of curved blocks in occupied workplace is avoided by combining the lowest centre-of-gravity rule with the calculation method of the remained workplace proposed in this paper. A modified genetic ant colony algorithm was proposed, which apply the ease to premature characteristics of GA and the excellent local optimisation ability of ACO, to let and promote the algorithm falls into local optimum. Then the large-scale and full-range mutation will be implemented to make the algorithm jump out of the original local optimisation to search more local optimal solutions so that the global optimal solution can be achieved. Finally, a software system for algorithm verification was developed which conducts the comparative analysis of the algorithms and verifies the validity of the algorithm proposed.
- Subjects :
- 0209 industrial biotechnology
Engineering
021103 operations research
Job shop scheduling
business.industry
Strategy and Management
Ant colony optimization algorithms
0211 other engineering and technologies
Scheduling (production processes)
02 engineering and technology
Management Science and Operations Research
Industrial and Manufacturing Engineering
020901 industrial engineering & automation
Shipbuilding
Artificial intelligence
business
Computer Science::Operating Systems
Algorithm
Subjects
Details
- ISSN :
- 1366588X and 00207543
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- International Journal of Production Research
- Accession number :
- edsair.doi.dedup.....61ffaf678bc90819a7930544506844ab