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  4. Production control under process queue time constraints in systems with a common downstream workstation
 
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Production control under process queue time constraints in systems with a common downstream workstation

Journal
International Journal of Industrial Engineering : Theory Applications and Practice
Journal Volume
23
Journal Issue
5
Pages
349-371
Date Issued
2016
Author(s)
Chen Y.-T
CHENG-HUNG WU  
Tien Y.-J
Yu C.-J.
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016008202&partnerID=40&md5=eaafe3cb0d182ae6c5c42034cd9e4426
https://scholars.lib.ntu.edu.tw/handle/123456789/625532
Abstract
This research develops a dynamic production control method for two-product manufacturing systems under process queue time (PQT) constraints. In which, while two different products are being processed at two upstream workstations, a common downstream workstation is shared by both products. PQT constraints before the downstream common workstation are assumed. Under PQT constraints, waiting time of jobs in the downstream queue is constrained by predefined upper limits. When a work-in-process (WIP) waits in the downstream queue longer than the predefined upper limit, the WIP may be scrapped or the yield quality may deteriorate seriously. When machines are unreliable, random machine failures cause high waiting time variance and significantly increase the risk of violating PQT constraints. Therefore, for production systems with PQT constraints, a robust dynamic scheduling method with real time machine reliability considerations is critical. In this research, a Markov decision process (MDP) model is developed to explicitly consider real time machine reliability status and real time WIP distribution. The objective is to find optimal dynamic admission control policies for the upstream workstations and optimal priority control policies for the downstream common workstation. The MDP model aims to minimize expected long-run average production costs, which are the sum of waiting costs and scrap costs. To minimize total costs, a good control model should balance the need for low scrap rates and low waiting costs. In our numerical study, the robustness of the proposed control method is shown by discrete event simulation. Compared with other control methods in literature, the proposed method reduces production costs by at least 27.6% on average. © International Journal of Industrial Engineerings.
Subjects
Common machines; Dynamic programming; Equipment health; Production control; Queue time constraints
SDGs

[SDGs]SDG9

Other Subjects
Costs; Discrete event simulation; Dynamic programming; Industrial research; Markov processes; Numerical methods; Production control; Queueing theory; Real time systems; Semiconductor device manufacture; Control methods; Dynamic production; Dynamic scheduling methods; Markov Decision Processes; Optimal dynamics; Priority control; Production system; Queue time constraint; Process control
Type
journal article

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