Towards an adaptive Kanban system
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A control methodology for flow shops that is decentralized and adaptive in nature, and has low data handling and computational requirements is presented and relationship of the control model to computational models such as neural computing is discussed.
Abstract
SUMMARY This paper presents a control methodology for flow shops that is decentralized and adaptive in nature, and has low data handling and computational requirements. The methodology is based on stochastic automata methods for modelling learning behaviour. It is proposed that such a methodology can be used with Kanban type control technique to make flow shop systems more flexible and adaptive in nature, Relationship of the control model to computational models such as neural computing is discussed.
