Improving materials flow through autonomous production control
Type
article
Publisher
Identifier
FERNANDES, Nuno; MARTINS, Tiago; SILVA, Sílvio Carmo (2018) - Improving materials flow through autonomous production control. Journal of Industrial and Production Engineering. ISSN: 2168-1023. Vol. 35, nº 5. p, 319-327
2168-1023
10.1080/21681015.2018.1479895
Title
Improving materials flow through autonomous production control
Subject
Autonomous production control
Flexible flow shops
Simulation
Flexible flow shops
Simulation
Relation
info:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F00319%2F2013/PT
Date
2018-11-23T15:10:07Z
2019-06-30T00:30:12Z
2018
2019-06-30T00:30:12Z
2018
Description
“This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Industrial and Production Engineering on 30-05-2018, available online: http://www.tandfonline.com/10.1080/21681015.2018.1479895.”
Autonomous Production Control (APC) aims at improving production systems performance through fast and flexible reaction to changes in dynamic environments. In this paper, a new APC method for job routing decision-making is proposed and its performance compared with that of two other APC methods, namely the Queue Length Estimator and the Pheromones, and with two conventional control strategies – a centralized and a random decision-making strategy. A discrete-event simulation model of a flexible flow shop operating under make-to-order was used to evaluate performance. Results show that the new method outperforms those with which it was compared, under high system workload and high variability of orders’ arrival and operation times. The study gives a contribution for better understanding of the performance behavior of APC methods, having important implications for industrial practice and for future research on autonomous production control.
info:eu-repo/semantics/publishedVersion
Autonomous Production Control (APC) aims at improving production systems performance through fast and flexible reaction to changes in dynamic environments. In this paper, a new APC method for job routing decision-making is proposed and its performance compared with that of two other APC methods, namely the Queue Length Estimator and the Pheromones, and with two conventional control strategies – a centralized and a random decision-making strategy. A discrete-event simulation model of a flexible flow shop operating under make-to-order was used to evaluate performance. Results show that the new method outperforms those with which it was compared, under high system workload and high variability of orders’ arrival and operation times. The study gives a contribution for better understanding of the performance behavior of APC methods, having important implications for industrial practice and for future research on autonomous production control.
info:eu-repo/semantics/publishedVersion
Access restrictions
embargoedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
http://creativecommons.org/licenses/by-nc-sa/4.0/
Language
eng
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