About: 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.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • 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.
subject
  • Decision-making
  • Optimization algorithms and methods
  • Production and manufacturing
  • Scheduling (computing)
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software