Artificial neural network case study: The control of work-in-process inventory in a manufacturing line
| dc.contributor.author | Col Muhterem | |
| dc.contributor.author | Karlik Bekir | |
| dc.date.accessioned | 2025-04-10T11:18:33Z | |
| dc.date.available | 2025-04-10T11:18:33Z | |
| dc.date.issued | 1997 | |
| dc.description.abstract | The work-in-process in a middle-scale shoe factory is investigated. At the manufacturing center, there are five work departments composed of cutting, sewing, hand-leather working, assembling and quality control-packing. In these departments, there are work-in-process because of different processing time and insufficient number of labor-machines. Artificial neural network (ANN) with backpropagation is used for solving this work-in-process problem. The optimum number of labor-machines at work stations are obtained for work-in-process without hindering the production by considering processing times and flow at these work stations. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/53573 | |
| dc.publisher | IEEE | |
| dc.title | Artificial neural network case study: The control of work-in-process inventory in a manufacturing line | |
| dc.type | Conference paper |