Title
Fuzzy cell genetic algorithm approach for flexible flow-line scheduling model
Document Type
Conference Proceeding
Date of Original Version
2-9-2017
Abstract
This paper focuses on makespan minimization for the flow line scheduling problem using a Fuzzy Cell Genetic Algorithm (FCGA). Real world applications of this problem are commonly found in printing and electronic circuit board manufacturing industries. A generalized integer programming (IP) model for this problem is proposed. The Fuzzy Cell Genetic Algorithm (FCGA) is proposed to solve the IP model, which has been proven to be NP-hard. Sample problems are generated with known good solutions to evaluate the effectiveness of the FCGA approach. The FCGA matches the performance of the IP model for small sized problem instances and it is proven to be effective for larger problem instances.
Publication Title, e.g., Journal
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Citation/Publisher Attribution
Shirazi, Arash N., Meghan Steinhaus, Matthew Agostinelli, and Manbir Sodhi. "Fuzzy cell genetic algorithm approach for flexible flow-line scheduling model." 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2017). doi: 10.1109/SSCI.2016.7850082.