Real-time scheduling for holonic manufacturing systems based on estimation of future status

• Published in: International journal of production research Vol. 44; no. 18-19; pp. 3657 - 3675
• Main Authors: Iwamura, K., Okubo, N., Tanimizu, Y., Sugimura, N.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: London Taylor & Francis Group 15.09.2006; Washington, DC Taylor & Francis; Taylor & Francis LLC
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Connectionism. Neural networks; Consortia; Decision making; Decision making models; Estimating techniques; Estimation of future status; Exact sciences and technology; Holonic manufacturing systems; Inventory control, production control. Distribution; Manufacturing; Neural network; Neural networks; Operational research and scientific management; Operational research. Management science; Production scheduling; Real time; Real-time scheduling; Studies; Production system; Coordination; Decision making; Process control; Scheduling; Neural network; Real time system; Holon; Holonic manufacturing systems; Production management; Real-time scheduling; Consortium; Estimation of future status
• DOI: 10.1080/00207540600847129
• ISSN: 0020-7543

This paper deals with a real-time scheduling system of holonic manufacturing systems (HMSs), which were proposed by an international cooperative research consortium called The HMS Consortium for Autonomous Distributed Management and Control of the Manufacturing Systems. The scheduling system generates real-time suitable production schedules, based on decision making by individual constituents named holons and their coordination. The objective of the present research is to improve the decision-making processes of the individual holons through the use of estimation process of the future status of the HMS. Procedures are developed and implemented to the individual holons, in order to estimate the future status of the HMS by applying the simulation model of the HMS. A neural network model is proposed to represent and to simulate the decision-making process of the individual holons.