Genetic algorithms and simulated annealing for scheduling in agile manufacturing

In this paper, genetic algorithms and simulated annealing are applied to scheduling in agile manufacturing. The system addressed consists of a single flexible machine followed by multiple identical assembly stations, and the scheduling objective is to minimize the makespan. Both genetic algorithms and simulated annealing are investigated based on random starting solutions and based on starting solutions obtained from existing heuristics in the literature. Overall, four new algorithms are developed and their performance is compared to the existing heuristics. A 2³ factorial experiment, replicated twice, is used to compare the performance of the various approaches, and identify the significant factors that affect the frequency of resulting in the best solution and the average percentage deviation from a lower bound. The results show that both genetic algorithms and simulated annealing outperform the existing heuristics in many instances. In addition, simulated annealing outperforms genetic algorithms with a more robust performance. In some instances, existing heuristics provide comparable results to those of genetic algorithms with simulated annealing with the added advantage of being simpler. Significant factors and interactions affecting the performance of the various approaches are also investigated.