Abstract
The environment is a manufacturing facility that produces multi-level assemblies in a Just-In-Time (JIT) fashion. The due-dates and lot-sizes of the end-items are given, and the objective is to determine a lot-for-lot operations schedule that minimizes the cumulative production lead-time. The scheduling problem within such an environment is NP-hard, and therefore, the performance of heuristics may vary depending on the specific problem instance. To address this problem an effective hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm is developed. The GA starts with an initial population generated by well known scheduling heuristics, a critical path heuristic, and randomly generated schedules. The scheduling work is shared by the GA and SA in two phases that alternate until convergence: (1) Phase I is the GA that crosses over solutions for different work-centers, and (2) Phase II is the SA that improves the sequence of operations on individual work-centers. The effectiveness of the proposed heuristic is assessed via numerical studies.
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.
