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Title: Job Shop Flow Time Prediction using Neural Networks
Authors: Silva, Cristovão 
Ribeiro, Vera 
Coelho, Pedro 
Magalhães, Vanessa 
Neto, Pedro 
Keywords: Dynamic job shop; Dynamic due date assignment rules; Simulation; Artificial Neural Networks; Dispatching rules
Issue Date: 2017
Project: Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE2020 
Serial title, monograph or event: Procedia Manufacturing
Volume: 11
Abstract: In this paper we investigate the use of Artificial Neural Networks (ANN) for flow time prediction and, consequently, to estimate due dates (DD) in a hypothetical dynamic job-shop. The effectiveness of the proposed ANN based DD assignment model is evaluated comparing it performance with the performance of two dynamic DD assignment rules proposed in the literature: Dynamic Total Work Content, and Dynamic Processing Plus Waiting. Results show that ANN based DD assignment models are more effective than, not only available static DD assignment rules, as concluded by other researchers, but also than the more effective Dynamic DD assignment rules.
ISSN: 23519789
DOI: 10.1016/j.promfg.2017.07.309
Rights: openAccess
Appears in Collections:FCTUC Eng.Mecânica - Artigos em Revistas Internacionais

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