Akademik Veri Yönetim Sistemi
COMPUTERS & INDUSTRIAL ENGINEERING, cilt.50, ss.185-194, 2006 (SCI-Expanded)
When there is a production system With excess capacity, i.e. more capacity than the demand for the foreseeable. future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relations hip between the design and control parameters of a manufacturing system and the resulting performance targets. (c) 2006 Elsevier Ltd. All rights reserved.