Real-Time Bottleneck in Serial Production Lines With Bernoulli Machines: Theory and Case Study

2020 ◽  
pp. 1-13
Author(s):  
Jiachen Tu ◽  
Yishu Bai ◽  
Mengzhuo Yang ◽  
Liang Zhang ◽  
Peter Denno
Keyword(s):  
Real Time ◽  
Production Lines ◽  
Serial Production ◽  
Serial Production Lines

scholarly journals c-Bottlenecks in serial production lines: Identification and application

2001 ◽  
Vol 7 (6) ◽  
pp. 543-578 ◽  
Author(s):  
S.-Y. Chiang ◽  
C.-T. Kuo ◽  
S. M. Meerkov
Keyword(s):  
Cycle Time ◽  
Production Line ◽  
Markovian Model ◽  
Production Lines ◽  
Serial Production ◽  
Automotive Engine ◽  
Machine Reliability ◽  
Bottleneck Identification ◽  
Serial Production Lines

The bottleneck of a production line is a machine that impedes the system performance in the strongest manner. In production lines with the so-called Markovian model of machine reliability, bottlenecks with respect to the downtime, uptime, and the cycle time of the machines can be introduced. The two former have been addressed in recent publications [1] and [2]. The latter is investigated in this paper. Specifically, using a novel aggregation procedure for performance analysis of production lines with Markovian machines having different cycle time, we develop a method for c-bottleneck identification and apply it in a case study to a camshaft production line at an automotive engine plant.

Gantry Scheduling for Two-Machine One-Buffer Composite Work Cell by Reinforcement Learning

2017 ◽  
Author(s):  
Jorge Arinez ◽  
Xinyan Ou ◽  
Qing Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Production Lines ◽  
Serial Production ◽  
Q Learning ◽  
Proper Actions ◽  
Work Cell ◽  
Scheduling Policy ◽  
Serial Production Lines

In this paper, a manufacturing work cell with a gantry that is in charge of moving materials/parts between machines and buffers is considered. With the effect of the gantry movement, the system performance becomes quite different from traditional serial production lines. In this paper, reinforcement learning is used to develop a gantry scheduling policy in order to improve system production. The gantry learns to take proper actions under different situations to reduce system production loss by using Q-Learning algorithm and finds the optimal moving policy. A two-machine one-buffer work cell with a gantry is used for case study, by which reinforcement learning is applied. Compare with the FCFS policy, the fidelity and effectiveness of the reinforcement learning method are also demonstrated.

scholarly journals Bernoulli serial production lines with quality control devices: Theory and application

2006 ◽  
Vol 2006 ◽  
pp. 1-30 ◽  
Author(s):  
Shu-Yin Chiang
Keyword(s):  
Quality Control ◽  
Performance Measures ◽  
System Performance ◽  
Production Systems ◽  
Production Plant ◽  
Production Lines ◽  
Serial Production ◽  
Control Devices ◽  
Serial Production Lines

This paper develops the procedure for the analysis of the production systems with quality control devices. The evaluation of the production system requires an expression for the system performance measures as functions of the machine and buffer parameters. This paper presents a method for evaluating these functions and illustrates their practical utility using a case study at a production plant.

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