The Research of Flexible Scheduling of Workshop Based on Artificial Fish Swarm Algorithm and Knowledge Mining
| Autor: | Jieyang Peng, Andreas Kimmig, Dongkun Wang, Jivka Ovtcharova, Jiahai Wang |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Knowledge representation and reasoning Job shop scheduling Computer science Decision tree Scheduling (production processes) Initialization Swarm behaviour 02 engineering and technology Data-driven 020901 industrial engineering & automation Production planning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030539559 ICSI |
| DOI: | 10.1007/978-3-030-53956-6_10 |
| Popis: | The Job Shop Scheduling problem is critical in the manufacturing industry. At present, the decision tree reasoning technique and data mining are often used in multi-objective optimization research to solve flexible job shop scheduling issues. Unfortunately, when job shop scheduling problems involve complex logic, it becomes difficult to implement data-driven automatic scheduling without human intervention. Based on the analysis of mass data and specialized knowledge in the scheduling domain, an ontology-based scheduling knowledge model and a method of knowledge representation can be established. Considering the relationship between data mining and knowledge, this paper illustrates the acquisition process of scheduling rules. These scheduling rules were applied to improve the initialization process of the artificial fish algorithm. Then, a scheduling experiment was designed, the results of which show that the efficiency and accuracy of the algorithm has been improved. The desired uncertain information analysis, decision-making support for production planning and scheduling on the shop floor are provided and an adaptive scheduling algorithm for complex manufacturing systems is established by building a knowledge-based system. |
| Databáze: | OpenAIRE |
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