The focus is on workload control, a production planning and control technique that reduces and stabilizes the total throughput time. In these conditions, defining realistic delivery dates should become easier, yet the use of basic techniques often proves to be ineffective. Hence, we propose using statistical and/or neural network techniques to estimate, starting from the current workload of the job shop, the expected lead time of entry jobs, and to use this estimation to define reliable delivering dates. To test the approach, we simulated a 6-machines job-shop and we make predictions using a multi-regressive linear model and a multi-layer neural network. In terms of tardy jobs, both approaches performed very well, with the neural network providing the best results.

Deep learning and WLC: How to set realistic delivery dates in high variety manufacturing systems / Mezzogori, D.; Romagnoli, G.; Zammori, F.. - ELETTRONICO. - 52:(2019), pp. 2092-2097. [10.1016/j.ifacol.2019.11.514]

Deep learning and WLC: How to set realistic delivery dates in high variety manufacturing systems

Mezzogori D.
;
Romagnoli G.;Zammori F.
2019

Abstract

The focus is on workload control, a production planning and control technique that reduces and stabilizes the total throughput time. In these conditions, defining realistic delivery dates should become easier, yet the use of basic techniques often proves to be ineffective. Hence, we propose using statistical and/or neural network techniques to estimate, starting from the current workload of the job shop, the expected lead time of entry jobs, and to use this estimation to define reliable delivering dates. To test the approach, we simulated a 6-machines job-shop and we make predictions using a multi-regressive linear model and a multi-layer neural network. In terms of tardy jobs, both approaches performed very well, with the neural network providing the best results.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2873197
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