Development of an intelligent shopfloor scheduler using neural networks and simulation

• Main Author: Tan, Hock Soon.
• Other Authors: School of Computer Engineering
• Format: Theses and Dissertations
• Language: English
• Published: 2010
• Subjects: DRNTU::Engineering::Systems engineering
• Online Access: http://hdl.handle.net/10356/41539
• Institution: Nanyang Technological University

In recent years, many firms have rediscovered the importance of scheduling in the shop floor. Within the manufacturing functions, scheduling remains among the most important and challenging tasks that must be performed routinely. Developing a schedule involves designating the resources needed to execute each operation of the process routing plan and assigning the times at which each operation in the routing will start and finish execution. It is apparent that three common scheduling approaches: OR-based, Simulation-based and Al-based alone cannot fully solve the scheduling problem satisfactorily. The trend is towards a combination of the three approaches. A hybrid approach using a neural network and simulation-based scheduling to solve the detailed scheduling problem of the shop floor is presented. The neural network is developed to analyze the complex information as well as orders coming into the shopfloor and suggests candidate scheduling rules to the simulation model. The simulation model then uses the rules to schedule the orders on hand. The scheduling performance is analyzed, and strengths and limitations of the approach are discussed. The work is set against a backdrop of a currently operating flexible manufacturing cell.