Abstract

In production systems, the buffer capacities have usually been assumed to be fixed during normal operations. Inspired by the observations from the real industrial operations, a novel concept of Adaptive Buffer Space (ABS) is proposed in this paper. The ABS is a type of equipment, such as movable racks or mobile robots with racks, which can be used to provide extra storage space for a production line to temporarily increase certain buffers’ capacities in a real-time fashion. A good strategy to assign and reassign the ABS can significantly improve real-time production throughput. In order to model the production systems with changing buffer capacities, a data-driven model is developed to incorporate the impact of buffer capacity variation in system dynamics. Based on the model, a real-time ABS assignment strategy is developed by analyzing real-time buffer levels and machine status. The strategy is demonstrated to be effective in improving the system throughput. An approximate dynamic programming algorithm, referred to as ABS-ADP, is developed to obtain the optimal ABS assignment policy based on the strategy. Traditional ADP algorithms often initialize the state values with zeros or random numbers. In this paper, a knowledge-guided value function initialization method is proposed in ABS-ADP algorithm to expedite the convergence, which saves up to 80% computation time in the case study.

References

References
1.
Li
,
J.
, and
Meerkov
,
S. M.
,
2008
,
Production Systems Engineering
,
Springer Science & Business Media
,
New York, NY
.
2.
Becker
,
C.
, and
Scholl
,
A.
,
2006
, “
A Survey on Problems and Methods in Generalized Assembly Line Balancing
,”
Eur. J. Oper. Res.
,
168
(
3
), pp.
694
715
. 10.1016/j.ejor.2004.07.023
3.
Huang
,
J.
,
Chang
,
Q.
,
Zou
,
J.
, and
Arinez
,
J.
,
2018
, “
A Real-Time Maintenance Policy for Multi-stage Manufacturing Systems Considering Imperfect Maintenance Effects
,”
IEEE Access
,
6
, pp.
62174
62183
. 10.1109/ACCESS.2018.2876024
4.
Djurdjanovic
,
D.
,
Mears
,
L.
,
Niaki
,
F. A.
,
Haq
,
A. U.
, and
Li
,
L.
,
2018
, “
State of the Art Review on Process, System, and Operations Control in Modern Manufacturing
,”
ASME J. Manuf. Sci. Eng.
,
140
(
6
), p.
061010
. 10.1115/1.4038074
5.
Buckholtz
,
B.
,
Ragai
,
I.
, and
Wang
,
L.
,
2015
, “
Cloud Manufacturing: Current Trends and Future Implementations
,”
ASME J. Manuf. Sci. Eng.
,
137
(
4
), p.
040902
. 10.1115/1.4030009
6.
Liu
,
Y.
, and
Xu
,
X.
,
2017
, “
Industry 4.0 and Cloud Manufacturing: A Comparative Analysis
,”
ASME J. Manuf. Sci. Eng.
,
139
(
3
), p.
034701
. 10.1115/1.4034667
7.
Huang
,
J.
,
Chang
,
Q.
, and
Arinez
,
J.
,
2020
, “
Deep Reinforcement Learning Based Preventive Maintenance Policy for Serial Production Lines
,”
Expert Syst. Appl.
,
160
, p.
113701
. 10.1016/j.eswa.2020.113701
8.
Demir
,
L.
,
Tunali
,
S.
, and
Eliiyi
,
D. T.
,
2014
, “
The State of the Art on Buffer Allocation Problem: A Comprehensive Survey
,”
J. Intell. Manuf.
,
25
(
3
), pp.
371
392
. 10.1007/s10845-012-0687-9
9.
Weiss
,
S.
,
Schwarz
,
J. A.
, and
Stolletz
,
R.
,
2019
, “
The Buffer Allocation Problem in Production Lines: Formulations, Solution Methods, and Instances
,”
IISE Trans.
,
51
(
5
), pp.
456
485
. 10.1080/24725854.2018.1442031
10.
Zou
,
J.
,
Chang
,
Q.
,
Arinez
,
J.
,
Xiao
,
G.
, and
Lei
,
Y.
,
2017
, “
Dynamic Production System Diagnosis and Prognosis Using Model-Based Data-Driven Method
,”
Expert Syst. Appl.
,
80
, pp.
200
209
. 10.1016/j.eswa.2017.03.025
11.
Demir
,
L.
,
Tunal
,
S.
, and
Eliiyi
,
D. T.
,
2012
, “
An Adaptive Tabu Search Approach for Buffer Allocation Problem in Unreliable non-Homogenous Production Lines
,”
Comput. Oper. Res.
,
39
(
7
), pp.
1477
1486
. 10.1016/j.cor.2011.08.019
12.
Diamantidis
,
A. C.
, and
Papadopoulos
,
C. T.
,
2004
, “
A Dynamic Programming Algorithm for the Buffer Allocation Problem in Homogeneous Asymptotically Reliable Serial Production Lines
,”
Math. Probl. Eng.
,
2004
(
3
), pp.
209
223
. 10.1155/S1024123X04402014
13.
Tiacci
,
L.
,
2015
, “
Simultaneous Balancing and Buffer Allocation Decisions for the Design of Mixed-Model Assembly Lines with Parallel Workstations and Stochastic Task Times
,”
Int. J. Prod. Econ.
,
162
, pp.
201
215
. 10.1016/j.ijpe.2015.01.022
14.
Koren
,
Y.
, and
Shpitalni
,
M.
,
2010
, “
Design of Reconfigurable Manufacturing Systems
,”
J. Manuf. Syst.
,
29
(
4
), pp.
130
141
. 10.1016/j.jmsy.2011.01.001
15.
Jiang
,
Z.
,
Wang
,
H.
,
Dulebenets
,
M. A.
, and
Pasha
,
J.
,
2019
, “
Assembly System Configuration Design for Reconfigurability Under Uncertain Production Evolution
,”
ASME J. Manuf. Sci. Eng.
,
141
(
7
), p.
071001
. 10.1115/1.4043581
16.
Zhang
,
Y.
,
Zhao
,
M.
,
Zhang
,
Y.
,
Pan
,
R.
, and
Cai
,
J.
,
2019
, “
Dynamic and Steady-State Performance Analysis for Multi-state Repairable Reconfigurable Manufacturing Systems with Buffers
,”
Eur. J. Oper. Res.
,
283
(
2
), pp.
491
510
. 10.1016/j.ejor.2019.11.013
17.
Yang
,
D.
, and
Seo
,
D. W.
,
2017
, “
Closed-Form Formulae for Moment, Tail Probability, and Blocking Probability of Waiting Time in a Buffer-Sharing Deterministic System
,”
Oper. Res. Lett.
,
45
(
5
), pp.
403
408
. 10.1016/j.orl.2017.06.004
18.
Li
,
Z. W.
,
Zhou
,
M. C.
, and
Wu
,
N. Q.
,
2008
, “
A Survey and Comparison of Petri Net-Based Deadlock Prevention Policies for Flexible Manufacturing Systems
,”
IEEE Trans. Syst. Man Cybern. Part C Appl. Rev.
,
38
(
2
), pp.
173
188
. 10.1109/TSMCC.2007.913920
19.
Yue
,
H.
,
Xing
,
K.
,
Hu
,
H.
,
Wu
,
W.
, and
Su
,
H.
,
2018
, “
Resource Failure and Buffer Space Allocation Control for Automated Manufacturing Systems
,”
Inf. Sci.
,
450
, pp.
392
408
. 10.1016/j.ins.2018.02.043
20.
Luo
,
J. C.
,
Liu
,
Z.
,
Zhou
,
M.
,
Xing
,
K.
,
Wang
,
X.
,
Li
,
X.
, and
Liu
,
H.
,
2019
, “
Robust Deadlock Control of Automated Manufacturing Systems with Multiple Unreliable Resources
,”
Inf. Sci.
,
479
, pp.
401
415
. 10.1016/j.ins.2018.11.051
21.
Wang
,
F.
, and
Ju
,
F.
,
2020
, “
Transient and Steady-State Analysis of Multistage Production Lines With Residence Time Limits
,”
IEEE Trans. Autom. Sci. Eng.
, pp.
1
13
. 10.1109/tase.2020.2979179
22.
Mhada
,
F. Z.
,
Malhamé
,
R. P.
, and
Pellerin
,
R.
,
2013
, “
Joint Assignment of Buffer Sizes and Inspection Points in Unreliable Transfer Lines With Scrapping of Defective Parts
,”
Prod. Manuf. Res.
,
1
(
1
), pp.
79
101
. 10.1080/21693277.2013.857618
23.
Colledani
,
M.
,
Tolio
,
T.
,
Fischer
,
A.
,
Iung
,
B.
,
Lanza
,
G.
,
Schmitt
,
R.
, and
Váncza
,
J.
,
2014
, “
Design and Management of Manufacturing Systems for Production Quality
,”
CIRP Ann.
,
63
(
2
), pp.
773
796
. 10.1016/j.cirp.2014.05.002
24.
Huang
,
J.
,
Chang
,
Q.
, and
Arinez
,
J.
,
2019
, “
Modeling and Analysis of Multi-Product Manufacturing Systems with two Machines and one Buffer
,”
ASME 2019 14th International Manufacturing Science and Engineering Conference (MSEC 2019)
,
Erie, PA
,
June 10
, MSEC 2019, Vol.
1
.
25.
Liu
,
J.
,
Chang
,
Q.
,
Xiao
,
G.
, and
Biller
,
S.
,
2012
, “
The Costs of Downtime Incidents in Serial Multistage Manufacturing Systems
,”
ASME J. Manuf. Sci. Eng.
,
134
(
2
), p.
021016
. 10.1115/1.4005789
26.
Li
,
Y.
,
Chang
,
Q.
,
Biller
,
S.
, and
Xiao
,
G.
,
2014
, “
Event-Based Modelling of Distributed Sensor Networks in Battery Manufacturing
,”
Int. J. Prod. Res.
,
52
(
14
), pp.
4239
4252
. 10.1080/00207543.2013.874606
27.
Ou
,
X.
,
Chang
,
Q.
, and
Chakraborty
,
N.
,
2019
, “
Simulation Study on Reward Function of Reinforcement Learning in Gantry Work Cell Scheduling
,”
J. Manuf. Syst.
,
50
, pp.
1
8
. 10.1016/j.jmsy.2018.11.005
28.
Powell
,
W. B.
,
2007
,
Approximate Dynamic Programming: Solving the Curses of Dimensionality
, 2nd ed.,
John Wiley & Sons
,
Hoboken, NJ
.
29.
Sutton
,
R. S.
, and
Barto
,
A. G.
,
2018
,
Reinforcement Learning: An Introduction
,
MIT Press, Cambridge, MA
.
30.
Zhao
,
R.
,
Yan
,
R.
,
Chen
,
Z.
,
Mao
,
K.
,
Wang
,
P.
, and
Gao
,
R. X.
,
2019
, “
Deep Learning and Its Applications to Machine Health Monitoring
,”
Mech. Syst. Signal Process.
,
115
, pp.
213
237
. 10.1016/j.ymssp.2018.05.050
31.
He
,
A.
, and
Jin
,
X.
,
2019
, “
Failure Detection and Remaining Life Estimation for Ion Mill Etching Process Through Deep-Learning Based Multimodal Data Fusion
,”
ASME J. Manuf. Sci. Eng.
,
141
(
10
), p.
101008
. 10.1115/1.4044248
32.
Caridi
,
M.
, and
Cavalieri
,
S.
,
2004
, “
Multi-agent Systems in Production Planning and Control: An Overview
,”
Prod. Plan. Control
,
15
(
2
), pp.
106
118
. 10.1080/09537280410001662556
You do not currently have access to this content.