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.
ASJC Scopus subject areas
- Computer Science(all)