Abstract
The environment is a manufacturing facility that produces multi-level assemblies in a just-in-time 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 2 phases that alternate until convergence: 1. Phase I is the GA that crosses over solutions for different work-centers. 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.