TY - JOUR
T1 - A queue-based aggregation approach for performance evaluation of a production system with an AMHS
AU - Mohammadi, M.
AU - Dauzère-pérès, S.
AU - Yugma, C.
AU - Karimi-Mamaghan, M.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - © 2019Production planning optimization remains a major challenge in almost all industries, particularly in high-tech manufacturing. A critical task to support such optimization is performance evaluation, wherein an accurate estimation of the cycle time as a function of the throughput rate plays a key role. This paper develops a novel aggregation model based on a queueing network approach, so-called queue-based aggregation (QAG) model, to estimate the cycle time of a job-shop production system that consists of several processing workstations, and in which products are transferred via an Automated Material Handling System (AMHS). The proposed model aggregates both production and automated material handling systems and provides an accurate and fast estimation of the overall cycle time. The performance and superiority of the proposed model is validated by comparing its results with those of a detailed simulation model. Numerous sensitivity analyses are performed to provide valuable managerial insights on both the production and automated material handling systems.
AB - © 2019Production planning optimization remains a major challenge in almost all industries, particularly in high-tech manufacturing. A critical task to support such optimization is performance evaluation, wherein an accurate estimation of the cycle time as a function of the throughput rate plays a key role. This paper develops a novel aggregation model based on a queueing network approach, so-called queue-based aggregation (QAG) model, to estimate the cycle time of a job-shop production system that consists of several processing workstations, and in which products are transferred via an Automated Material Handling System (AMHS). The proposed model aggregates both production and automated material handling systems and provides an accurate and fast estimation of the overall cycle time. The performance and superiority of the proposed model is validated by comparing its results with those of a detailed simulation model. Numerous sensitivity analyses are performed to provide valuable managerial insights on both the production and automated material handling systems.
UR - http://www.scopus.com/inward/record.url?scp=85076515657&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2019.104838
DO - 10.1016/j.cor.2019.104838
M3 - Article
SN - 0305-0548
VL - 115
JO - Computers and Operations Research
JF - Computers and Operations Research
M1 - 104838
ER -
