Show simple item record

dc.contributor.advisorGeorgia Perakis and James Kirtley.en_US
dc.contributor.authorIngram, Christopher Thomasen_US
dc.contributor.otherLeaders for Global Operations Program.en_US
dc.date.accessioned2016-12-05T19:55:43Z
dc.date.available2016-12-05T19:55:43Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/105631
dc.descriptionThesis: 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.en_US
dc.descriptionThesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 55-56).en_US
dc.description.abstractStorms 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.en_US
dc.description.statementofresponsibilityby Christopher Thomas Ingram.en_US
dc.format.extent56 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectSloan School of Management.en_US
dc.subjectEngineering Systems Division.en_US
dc.subjectLeaders for Global Operations Program.en_US
dc.titleElectric utility storm restoration : crew work allocation optimizationen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Engineering Systemsen_US
dc.description.degreeM.B.A.en_US
dc.contributor.departmentLeaders for Global Operations Program at MITen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.contributor.departmentSloan School of Management
dc.identifier.oclc963211655en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record