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Joint Production and Energy Modeling for Sustainable Manufacturing Systems

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Title: Joint Production and Energy Modeling for Sustainable Manufacturing Systems
Author(s): Wang, Yong
Advisor(s): Li, Lin
Contributor(s): He, David; Banerjee, Prashant; Darabi, Houshang; Lin, Jie
Department / Program: Mechanical & Industrial Engineering
Graduate Major: Industrial Engineering & Operations Research
Degree Granting Institution: University of Illinois at Chicago
Degree: PhD, Doctor of Philosophy
Genre: Doctoral
Subject(s): Manufacturing Energy Sustainability Production Demand Response
Abstract: This dissertation proposes a framework for addressing challenges of joint production and energy modeling for manufacturing systems. The knowledge generated is used to improve the technological readiness of manufacturing enterprises for the transition towards sustainable manufacturing in the context of smart electric grids. Detailed research tasks of the framework on the modeling of production, energy efficiency, electricity demand, cost, and demand response decision making have been implemented. Specifically, the dynamics and performance measures of general manufacturing systems with multiple machines and buffers have been modeled. Expressions of electricity energy efficiency and cost have been established based on the electricity pricing profile. Production scheduling problem formulations and the solution technique are discussed. New insights are acquired based on the applications of the established model in system parameter monotonicity analysis, rate plan switching decision making, and demand response scheduling. The findings based on case studies show that with appropriate adjustment of production routines, significant improvement in energy efficiency and substantial savings in energy cost can be achieved without sacrificing production. Appropriate implementation of this research outcome may lead to energy-efficient, electricity-demand-responsive, and cost-effective operations and thus improve the sustainability of modern manufacturing systems. The new knowledge generated can be implemented to discrete manufacturing in various industries such as automotive, electronics, appliances, aerospace, etc.
Issue Date: 2015-10-21
Genre: thesis
URI: http://hdl.handle.net/10027/19834
Rights Information: Copyright 2015 Yong Wang
Date Available in INDIGO: 2017-10-22
Date Deposited: 2015-08
 

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