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Framework and Sensitivity Analysis of Joint Energy and Maintenance Planning Considering Production Throughput Requirements

[+] Author Affiliations
Fadwa Dababneh, Rahul Shah, Lin Li

University of Illinois at Chicago, Chicago, IL

Zeyi Sun

Missouri University of Science and Technology, Rolla, MO

Paper No. MSEC2017-2936, pp. V003T04A062; 6 pages
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 3: Manufacturing Equipment and Systems
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5074-9
  • Copyright © 2017 by ASME


As the U.S. manufacturing sector becomes more and more competitive, manufactures are seeking to adopt more cost and resource efficient operation practices. Several research studies that optimize production, maintenance, and energy have been conducted. However, research integrating them all in one model is less developed. In this paper, we present a framework on joint maintenance and energy planning while considering production throughput target and buffer constraints. The problem considers a Time-of-Use (TOU) demand response program such that the cost of production, energy, and maintenance is reduced. A sensitivity analysis considering the effect of production system parameters, such as machine rated power, machine production rate, number of maintenance crew resources, etc., on the cost per part is conducted. In addition, the sensitivity due to varying the individual unit costs incurred from production throughput, power demand, and maintenance is investigated. This study can guide manufacturers and researchers in determining the value of using such joint methods. Furthermore, the sensitivity analysis considering joint energy and maintenance can aid manufacturers and researchers in data acquisition so that the most sensitive parameters are given priority, help identify which controllable parameters have the largest impact on the system performance; and determine which parameters are most impactful.

Copyright © 2017 by ASME



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