Research Papers

Demand Response-Driven Production and Maintenance Decision-Making for Cost-Effective Manufacturing

[+] Author and Article Information
Fadwa Dababneh

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: fdabab2@uic.edu

Lin Li

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: linli@uic.edu

Rahul Shah

Department of Mechanical and
Industrial Engineering,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: rshah204@uic.edu

Cliff Haefke

Energy Resources Center,
University of Illinois at Chicago,
Chicago, IL 60607
e-mail: chaefk1@uic.edu

1Corresponding author.

Manuscript received September 4, 2017; final manuscript received January 26, 2018; published online March 13, 2018. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 140(6), 061008 (Mar 13, 2018) (11 pages) Paper No: MANU-17-1552; doi: 10.1115/1.4039197 History: Received September 04, 2017; Revised January 26, 2018

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.

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Fig. 2

Serial N machine N–1 buffer production system

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Fig. 1

Energy unit price comparison

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Fig. 4

Energy and maintenance cost comparison

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Fig. 3

Production capability degradation dynamics

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Fig. 6

Cpart for PSO generated samples

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Fig. 5

Cpart for: (a) percent of machine rated power; (b) percent of maintenance rated power; (c) machine production rate; (d) initial machine production capability; (e) buffer fill percentage; and (f) number of maintenance crew resources

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Fig. 8

Normal probability plot and histogram of residuals for ANOVA analysis

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Fig. 9

Response optimizer



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