Hybrid Simulated Annealing in Flow-shop Scheduling: A Diversification and Intensification Approach

In the last few decades, several effective algorithms to solve combinatorial problems have been proposed. However, the challenging nature of these problems restricts the effectiveness of the conventional techniques. This paper presents a generic framework, SAMED, to tackle combinatorial optimisation problems. Based on this framework, a new algorithm tailored for Flow Shop Scheduling, SAMED-FSS, has been developed. The performance of the proposed method has been compared with other techniques including a conventional simulated annealing, a standard genetic algorithm, and a hybrid genetic algorithm. The computational results clearly indicate that the proposed algorithm is much more efficient than the conventional heuristics.

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