Application of Meta-Heuristics Accompanied with Reinforcement Learning Mechanism to Optimization of Rolling Sequence

It is known that ants and bees are working cooperatively as a group by detecting the information transmission material called pheromone. In this paper, this idea is combined with meta-heuristics search, and applied to the problem of finding an optimal operational sequence. In modeling, one job is treated as an agent such as ant or bee. Each agent scatters its peculiar pheromone on each processing order corresponding to decision variable, and exchanges the orders statistically among agents according to the distribution of pheromone. The results of the exchanges are evaluated as a whole and the update value of each pheromone is determined. We propose a method that generates a processing sequence converging to the optimal processing order by regulating the related parameters through such iteration. We have developed a computer program based on the algorithm. The usefulness of the proposed method is confirmed by numerical experiments for scheduling of the hot rolling process.