Paper Info
Title
A Stochastic Programming Model For Resequencing Buffer Content Optimisation In Mixed-Model Assembly Lines
Abstract
In mixed-model assembly lines, smooth operation of the assembly line depends on adherence to the scheduled sequence. However, during production process, this sequence is altered both intentionally and uninstentionally. A major source of unintentional sequence alteration in automobile plants is the paint defects. A post-paint resequencing buffer, located before the final assembly is used to restore the altered sequence. Restoring the altered sequence back to the scheduled sequence requires three distinct operations in this buffer: Changing the positions (i.e. resequencing) of vehicles, inserting spare vehicles in between difficult models and replacing spare vehicles with paint defective vehicles. We develop a two-stage stochastic model to determine the optimal number of spare vehicles from each model-colour type to be placed into the Automated Storage and Retrieval System resequencing buffer that maximises the scheduled sequence achievement ratio (SSAR). The model contributes to the literature by explicitly considering above three distinct operations and random nature of paint defect occurrences. We use sample average approximation algorithm to solve the model. We provide managerial insights on how paint entrance sequence, defect rate and buffer size affect the SSAR. A value of stochastic solution shows that the model significantly outperforms its deterministic counterpart.
Year
DOI
Venue
2017
10.1080/00207543.2016.1227101
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Keywords
DocType
Volume
mixed-model sequencing, car resequencing, sample average approximation, stochastic programming, value of stochastic solution
Journal
55
Issue
ISSN
Citations 
10
0020-7543
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Elif Elcin Gunay111.36
Ufuk Kula2193.76