Manufacturers consume about 27% of the total electricity in the U.S. and are among the main contributors in the rising electricity demand. End-user electricity demand response is an effective demand side management tool that can help energy suppliers reduce electricity generation expenditures while providing opportunities for manufacturers to decrease operating costs. Several studies on demand response for manufacturers have been conducted. However, there lacks a unified production model that balances production capability degradation, maintenance requirements, and time-of-use (TOU) electricity prices simultaneously such that the interaction between production, maintenance, and electricity costs is considered. In this paper, a cost-effective production and maintenance scheduling model considering TOU electricity demand response is presented. Additionally, an aggregate cost model is formulated, which considers production, maintenance, and demand response parameters in the same function. The proposed models provide manufacturers with tools for implementing feasible and cost-effective demand response while meeting production targets and efficiently allocating maintenance resources. A case study is performed and illustrates that 19% in cost savings can be achieved when using the proposed model compared to solely minimizing the electricity billing cost. In addition, 14% in cost savings can be achieved when using the proposed model compared to a strategy where only the maintenance cost is minimized. Finally, the benefits of demand response driven production and maintenance scheduling under different cost and parameter settings are investigated; where the rated power, production rate, and initial machine production capability show to have the largest impact on the cost effectiveness of implementing demand response.

References

1.
U.S. Energy Information Administration
,
2013
, “International Energy Outlook,” U.S. Energy Information Administration, Washington, DC, accessed Feb. 13, 2018, https://www.eia.gov/outlooks/ieo/pdf/0484(2013).pdf
2.
U.S. DOE
,
2010
, “Manufacturing Energy and Carbon Footprint for All Manufacturing Sector,” United States Department of Energy, Washington, DC, accessed Feb. 13, 2018, https://energy.gov/sites/prod/files/2015/10/f27/manufacturing_energy_footprint-2010.pdf
3.
Chang
,
Q.
,
Xiao
,
G.
,
Biller
,
S.
, and
Li
,
L.
,
2013
, “
Energy Saving Opportunity Analysis of Automotive Serial Production Systems (March 2012)
,”
IEEE Trans. Autom. Sci. Eng.
,
10
(
2
), pp.
334
342
.
4.
U.S. Energy Information Administration
,
2017
, “Electricity Customers. Energy and the Environment,” U.S. Energy Information Administration, Washington, DC, accessed Feb. 13, 2018, https://www.eia.gov/outlooks/ieo/pdf/industrial.pdf
5.
Rapier
,
R.
,
2011
, “
The Price of Energy
,” Forbes Media LLC, New York, accessed Feb. 13, 2018, www.forbes.com/sites/energysource/2010/01/26/the-price-of-energy/#69e9076367b1
6.
FERC
,
2013
, “A National Assessment & Action Plan on Demand Response Potential,” Federal Energy Regulatory Commission, Washington, DC, accessed Feb. 13, 2018, https://www.ferc.gov/industries/electric/indus-act/demand-response/dr-potential.asp
7.
Kenné
,
J.-P.
,
Gharbi
,
A.
, and
Beit
,
M.
,
2007
, “
Age-Dependent Production Planning and Maintenance Strategies in Unreliable Manufacturing Systems With Lost Sale
,”
Eur. J. Oper. Res.
,
178
(
2
), pp.
408
420
.
8.
Karakas
,
E.
,
Koyuncu
,
M.
,
Erol
,
R.
, and
Kokangul
,
A.
,
2010
, “
Fuzzy Programming for Optimal Product Mix Decisions Based on Expanded ABC Approach
,”
Int. J. Prod. Res.
,
48
(
3
), pp.
729
744
.
9.
Jodlbauer
,
H.
,
2008
, “
Customer Driven Production Planning
,”
Int. J. Prod. Econ.
,
111
(
2
), pp.
793
801
.
10.
Liu
,
X.
, and
Tu
,
Y.
,
2008
, “
Production Planning With Limited Inventory Capacity and Allowed Stockout
,”
Int. J. Prod. Econ.
,
111
(
1
), pp.
180
191
.
11.
Leung
,
S. C. H.
,
Tsang
,
S. O. S.
,
Ng
,
W. L.
, and
Wu
,
Y.
,
2007
, “
A Robust Optimization Model for Multi-Site Production Planning Problem in an Uncertain Environment
,”
Eur. J. Oper. Res.
,
181
(
1
), pp.
224
238
.
12.
Yuan
,
G.
, and
Bernard
,
G.
,
2016
, “Binary Optimization Via Mathematical Programming With Equilibrium Constraints,” preprint
arXiv:1608.04425
.https://arxiv.org/abs/1608.04425
13.
Hemmecke
,
R.
,
Matthias
,
K.
,
Jon
,
L.
, and
Robert
,
W.
,
2010
, “
Nonlinear Integer Programming
,”
50 Years of Integer Programming 1958–2008
, Springer, Berlin, pp.
561
618
.
14.
Gendreau
,
M.
, and
Jean-Yves
,
P.
,
2010
,
Handbook of Metaheuristics
, Vol.
2
,
Springer
,
New York
.
15.
Lee
,
J.-Y.
,
Shin
,
Y.-J.
,
Kim
,
M.-S.
,
Kim
,
E.-S.
,
Yoon
,
H.-S.
,
Kim
,
S.-Y.
,
Yoon
,
Y.-C.
,
Ahn
,
S.-H.
, and
Min
,
S.
,
2015
, “
A Simplified Machine-Tool Power-Consumption Measurement Procedure and Methodology for Estimating Total Energy Consumption
,”
ASME J. Manuf. Sci. Eng.
,
138
(
5
), p.
051004
.
16.
Feng
,
L.
, and
Mears
,
L.
,
2016
, “
Energy Consumption Modeling and Analyses in Automotive Manufacturing Plant
,”
ASME J. Manuf. Sci. Eng.
,
138
(
10
), p.
101005
.
17.
Wang
,
Y.
, and
Li
,
L.
,
2014
, “
Time-of-Use Based Electricity Cost of Manufacturing Systems: Modeling and Monotonicity Analysis
,”
Int. J. Prod. Econ.
,
156
, pp.
246
259
.
18.
Fernandez
,
M.
,
Li
,
L.
, and
Sun
,
Z.
,
2013
, “‘
Just-for-Peak’ Buffer Inventory for Peak Electricity Demand Reduction of Manufacturing Systems
,”
Int. J. Prod. Econ.
,
146
(
1
), pp.
178
184
.
19.
Dababneh
,
F.
,
Li
,
L.
, and
Sun
,
Z.
,
2016
, “
Peak Power Demand Reduction for Combined Manufacturing and HVAC System Considering Heat Transfer Characteristics
,”
Int. J. Prod. Econ.
,
177
, pp.
44
52
.
20.
Sun
,
Z.
, and
Li
,
L.
,
2013
, “
Opportunity Estimation for Real-Time Energy Control of Sustainable Manufacturing Systems
,”
IEEE Trans. Autom. Sci. Eng.
,
10
(
1
), pp.
38
44
.
21.
Feng
,
L.
,
Mears
,
L.
,
Beaufort
,
C.
, and
Schulte
,
J.
,
2016
, “
Energy, Economy, and Environment Analysis and Optimization on Manufacturing Plant Energy Supply System
,”
Energy Convers. Manage.
,
117
, pp.
454
465
.
22.
Li
,
L.
, and
Sun
,
Z.
,
2013
, “
Dynamic Energy Control for Energy Efficiency Improvement of Sustainable Manufacturing Systems Using Markov Decision Process
,”
IEEE Trans. Syst., Man, Cybern. Syst.
,
43
(
5
), pp.
1195
1205
.
23.
Gajic
,
D.
,
Hadera
,
H.
,
Onofri
,
L.
,
Harjunkoski
,
I.
, and
Di Gennaro
,
S.
,
2017
, “
Implementation of an Integrated Production and Electricity Optimization System in Melt Shop
,”
J. Cleaner Prod.
,
155
(Pt. 1), pp.
39
46
.
24.
Beier
,
J.
,
Thiede
,
S.
, and
Herrmann
,
C.
,
2017
, “
Energy Flexibility of Manufacturing Systems for Variable Renewable Energy Supply Integration: Real-Time Control Method and Simulation
,”
J. Cleaner Prod.
,
141
, pp.
648
661
.
25.
Wang
,
Y.
, and
Li
,
L.
,
2013
, “
Time-of-Use Based Electricity Demand Response for Sustainable Manufacturing Systems
,”
Energy
,
63
, pp.
233
244
.
26.
Dababneh
,
F.
,
Mariya
,
A.
,
Zeyi
,
S.
, and
Lin
,
L.
,
2015
, “Simulation-Based Electricity Demand Response for Combined Manufacturing and HVAC System Towards Sustainability,”
ASME
Paper No. MSEC2015-9278.
27.
Gong
,
X.
,
De Pessemier
,
T.
,
Joseph
,
W.
, and
Martens
,
L.
,
2015
, “
An Energy-Cost-Aware Scheduling Methodology for Sustainable Manufacturing
,”
Procedia CIRP
,
29
, pp.
185
190
.
28.
Zhang
,
H.
,
Fu
,
Z.
, and
Sutherland
,
J. W.
,
2016
, “
Scheduling of a Single Flow Shop for Minimal Energy Cost Under Real-Time Electricity Pricing
,”
ASME J. Manuf. Sci. Eng.
,
139
(
1
), p.
014502
.
29.
Jin
,
X.
,
Li
,
L.
, and
Ni
,
J.
,
2009
, “
Option Model for Joint Production and Preventive Maintenance System
,”
Int. J. Prod. Econ.
,
119
(
2
), pp.
347
353
.
30.
Nourelfath
,
M.
,
Nahas
,
N.
, and
Ben-Daya
,
M.
,
2016
, “
Integrated Preventive Maintenance and Production Decisions for Imperfect Processes
,”
Reliab. Eng. Syst. Saf.
,
148
, pp.
21
31
.
31.
Xiao
,
L.
,
Song
,
S.
,
Chen
,
X.
, and
Coit
,
D. W.
,
2016
, “
Joint Optimization of Production Scheduling and Machine Group Preventive Maintenance
,”
Reliab. Eng. Syst. Saf.
,
146
, pp.
68
78
.
32.
Xia
,
T.
,
Jin
,
X.
,
Xi
,
L.
, and
Ni
,
J.
,
2015
, “
Production-Driven Opportunistic Maintenance for Batch Production Based on MAM–APB Scheduling
,”
Eur. J. Oper. Res.
,
240
(
3
), pp.
781
790
.
33.
Beheshti-Fakher
,
H.
,
Nourelfath
,
M.
, and
Gendreau
,
M.
,
2016
, “
Joint Planning of Production and Maintenance in a Single Machine Deteriorating System
,”
IFAC-PapersOnLine
,
49
(
12
), pp.
745
750
.
34.
Fitouhi
,
M.-C.
, and
Nourelfath
,
M.
,
2014
, “
Integrating Noncyclical Preventive Maintenance Scheduling and Production Planning for Multi-State Systems
,”
Reliab. Eng. Syst. Saf.
,
121
, pp.
175
186
.
35.
Li
,
L.
,
Ambani
,
S.
, and
Ni
,
J.
,
2009
, “
Plant-Level Maintenance Decision Support System for Throughput Improvement
,”
Int. J. Prod. Res.
,
47
(
24
), pp.
7047
7061
.
36.
Chang
,
Q.
,
Ni
,
J.
,
Bandyopadhyay
,
P.
,
Biller
,
S.
, and
Xiao
,
G.
,
2007
, “
Maintenance Opportunity Planning System
,”
ASME J. Manuf. Sci. Eng.
,
129
(
3
), pp.
661
668
.
37.
Yao
,
X.
,
Zeyi
,
S.
,
Dong
,
W.
, and
Lingyun
,
W.
,
2016
, “Joint Maintenance and Energy Management in Manufacturing Systems: Prospect Discussion, Challenge Analysis, and a Case Study,”
ASME
Paper No. MSEC2016-8615.
38.
Yao
,
X.
,
Zeyi
,
S.
,
Lin
,
L.
, and
Hua
,
S.
,
2015
, “Joint Maintenance and Energy Management of Sustainable Manufacturing Systems,”
ASME
Paper No. MSEC2015-9345.
39.
Zou
,
J.
,
Arinez
,
J.
,
Chang
,
Q.
, and
Lei
,
Y.
,
2016
, “
Opportunity Window for Energy Saving and Maintenance in Stochastic Production Systems
,”
ASME J. Manuf. Sci. Eng.
,
138
(
12
), p.
121009
.
40.
Montgomery
,
D. C.
,
2004
,
Design and Analysis of Experiments
, 6th ed., John Wiley & Sons, Hoboken, NJ.
41.
Kleijnen
,
J. P. C.
,
1999
, “
Validation of Models: Statistical Techniques and Data Availability
,”
31st Conference on Winter Simulation: Simulation—A Bridge to the Future
(
WSC
), Phoenix, AZ, Dec. 5–8, pp.
647
654
.
42.
Sun, Z., Dababneh, F., and Li, L., 2018, “
Joint Energy, Maintenance and Throughput Modeling for Sustainable Manufacturing Systems
,” IEEE Trans. Syst., Man and Cybern., Syst. (in press).
43.
Sheehy
,
P.
, and
Martz
,
E.
,
2012
, “
Doing Monte Carlo Simulation in Minitab Statistical Software
,”
ASQ Lean Six Sigma Conference
, Phoenix, AZ, Feb. 27–28.https://www.minitab.com/en-us/Published-Articles/Doing-Monte-Carlo-Simulation-in-Minitab-Statistical-Software/
44.
Chen
,
Z.-S.
,
Zhu
,
B.
,
He
,
Y.-L.
, and
Yu
,
L.-A.
,
2017
, “
A PSO Based Virtual Sample Generation Method for Small Sample Sets: Applications to Regression Datasets
,”
Eng. Appl. Artif. Intell.
,
59
, pp.
236
243
.
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