2026-09-19T23:50:46Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1056312022-01-28T17:59:57Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Georgia Perakis and James Kirtley.
Ingram, Christopher Thomas
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
2016-12-05T19:55:43Z
2016-12-05T19:55:43Z
2016
2016
http://hdl.handle.net/1721.1/105631
963211655
Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 55-56).
Storms damage Atlantic Electric's electric distribution network, resulting in power outages and expensive repairs. After severe storms Atlantic Electric hires external contractor crews to perform the majority of the restoration work. This project focuses on increasing the effectiveness of contractor crews by: 1) improving work flow processes that can result in restoration delays; 2) staging contractor crews at operations bases that are close to damage; and 3) optimizing the work allocation to contractor crews so that customers have their power restored sooner. An optimization model used by Atlantic Electric to pre-stage their crews is modified and improved so that it can suggest locations for staging crews throughout a restoration effort. The model compares well to actual storm assignments in previous storms, normally preempting Atlantic Electric's decision by one day. This suggests that Atlantic Electric's experts and storm managers are already operating efficiently, but that the model can help them reach their decisions faster since it incorporates all data instantaneously and objectively. The use of the model will also provide a valuable justification to the state regulators, who monitor storm responses, for crew movements and postings. Next, an optimization model is developed to improve the assignment of individual repair jobs to crews. Currently the process is performed manually and can vary from base to base. Decision makers must balance multiple factors, such as the number of crews available, location of damage points, the severity of the damage, and the number and type of customers without power. Under enormous pressure in a hectic environment, it can be difficult to analyze and weigh all these factors. Additionally, due to the infrequent nature of these large events, some personnel have limited firsthand experience in these situations, while others are extremely experienced and skilled. The model captures and codifies the methodology of Atlantic Electric's storm experts and provides a quick, consistent tool for assigning work efficiently. Finally, we suggest several process improvements to the contractor work flow.
by Christopher Thomas Ingram.
S.M. in Engineering Systems
M.B.A.
56 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Institute for Data, Systems, and Society.
Sloan School of Management.
Engineering Systems Division.
Leaders for Global Operations Program.
Electric utility storm restoration : crew work allocation optimization
Thesis
application/pdf
false
Publication
unknown
unknown
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<Abstract>Storms damage Atlantic Electric's electric distribution network, resulting in power outages and expensive repairs. After severe storms Atlantic Electric hires external contractor crews to perform the majority of the restoration work. This project focuses on increasing the effectiveness of contractor crews by: 1) improving work flow processes that can result in restoration delays; 2) staging contractor crews at operations bases that are close to damage; and 3) optimizing the work allocation to contractor crews so that customers have their power restored sooner. An optimization model used by Atlantic Electric to pre-stage their crews is modified and improved so that it can suggest locations for staging crews throughout a restoration effort. The model compares well to actual storm assignments in previous storms, normally preempting Atlantic Electric's decision by one day. This suggests that Atlantic Electric's experts and storm managers are already operating efficiently, but that the model can help them reach their decisions faster since it incorporates all data instantaneously and objectively. The use of the model will also provide a valuable justification to the state regulators, who monitor storm responses, for crew movements and postings. Next, an optimization model is developed to improve the assignment of individual repair jobs to crews. Currently the process is performed manually and can vary from base to base. Decision makers must balance multiple factors, such as the number of crews available, location of damage points, the severity of the damage, and the number and type of customers without power. Under enormous pressure in a hectic environment, it can be difficult to analyze and weigh all these factors. Additionally, due to the infrequent nature of these large events, some personnel have limited firsthand experience in these situations, while others are extremely experienced and skilled. The model captures and codifies the methodology of Atlantic Electric's storm experts and provides a quick, consistent tool for assigning work efficiently. Finally, we suggest several process improvements to the contractor work flow.</Abstract>
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