Abstract
In this paper, we consider the permutation flow shop scheduling problem with a general exponential learning effect. The objective is to minimize the maximum lateness. A special case that can be solved to optimality by EDD algorithm is provided. To form a hybrid solution framework, several heuristics, a branch-and-bound algorithm and a new Nested-Partition-based solution approach are proposed. Composite bounds and dominance rules are developed to reduce the searching region and to provide guidance on the lower bound. Finally, computational experiments are conducted to evaluate the performance of the algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
