Abstract

Industries are aware of the costs of high electricity consumption and its impact on the environment. Therefore, significant focus is given on how to reduce the consumption of electricity to lower both the carbon footprint and the cost of electricity. There are two ways of reducing manufacturing electricity costs. One consists of reducing the total demand by having more efficient machines, and the other focuses on minimizing costs in response to pricing structures set by energy companies. These pricing structures change depending on several factors including geographic location. In parts of the United States these structures consist of both a consumption charge, which is related to the total amount of energy consumed, and a demand charge, which is a separate charge for the highest average power consumed per a given time window. Consideration for both time-dependent consumption charges and demand charges, when planning a production schedule (e.g. parallel machine scenario), can reduce the total cost of electricity. In this study, a mathematical optimization model is developed wherein the consideration of demand charges in a specific parallel machine scenario allows a 21% reduction in total electricity cost in contrast to the consideration of consumption charges only. Additionally, the model allows for the exploration of the cost implications of changing different parameters, such as the number of machines, time to finish the jobs, and the price of the demand charge. The results provide a better understanding on how demand charges and the inclusion of more than one machine in the model affect the schedule of a flow shop if energy costs are to be minimized.

Library of Congress Subject Headings

Manufacturing industries--Energy consumption; Electric power--Conservation; Electric power consumption--Economic aspects

Publication Date

7-2017

Document Type

Thesis

Student Type

Graduate

Degree Name

Industrial and Systems Engineering (MS)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)

Advisor

Katie McConky

Advisor/Committee Member

Ruben Proano

Advisor/Committee Member

Ruben Proano

Comments

Physical copy available from RIT's Wallace Library at HD9720.5 .B38 2017

Campus

RIT – Main Campus

Plan Codes

ISEE-MS

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