Abstract
This paper presents two methods to solve the production smoothing problem in mixed-model just-in-time (JIT) systems with large setup and processing time variability between different models the systems produce. The problem is motivated by production planning at a leading U.S. automotive pressure hose manufacturer. One method finds all Pareto-optimal solutions that minimize total production rate variation of models and work in process (WIP), and maximize system utilization and responsiveness. These Pareto-optimal solutions are found efficiently in polynomial time with respect to total demand by an algorithm proposed in the paper. The other method relies on Daniel Webster's method of apportionment for production smoothing, which produces periodic, uniform, and reflective production sequences that can improve operations management of the JIT systems. Finally, the paper presents the results of a computational experiment with the two methods.
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.
