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Presentation
Risk-Averse Stochastic Integer Programs for Mixed-Model Assembly Line Sequencing Problems
Proceedings of the 2017 Industrial and Systems Engineering Conference
  • Ge Guo, Iowa State University
  • Sarah M. Ryan, Iowa State University
Document Type
Conference Proceeding
Publication Version
Published Version
Publication Date
1-1-2017
Conference Title
Industrial and Systems Engineering Conference
Conference Date
May 20-23, 2017
Geolocation
(40.44062479999999, -79.99588640000002)
Abstract
A variety of optimization formulations have been proposed for mixed-model assembly sequencing problems with stochastic demand and task times. In the real world, however, mixed-model assembly lines are faced with more challenging uncertainties including timely part delivery, material quality, upstream sub-assembly completion and availability of other resources. In addition, sub-assembly lines must meet deadlines imposed by downstream stations. The inevitable disruptions require resequencing. We present a risk-averse stochastic mixed-integer model for mixed-model assembly line resequencing problems to increase on-time performance.
Comments

This is a proceeding from Proceedings of the 2017 Industrial and Systems Engineering Conference (2017). Posted with permission.

Copyright Owner
The Authors
Language
en
Citation Information
Ge Guo and Sarah M. Ryan. "Risk-Averse Stochastic Integer Programs for Mixed-Model Assembly Line Sequencing Problems" Pittsburgh, PAProceedings of the 2017 Industrial and Systems Engineering Conference (2017)
Available at: http://works.bepress.com/sarah_m_ryan/91/