An adaptive simulated annealing algorithm for job shop scheduling problems

Simulated annealing is a stochastic search method that uses a temperature function to calculate the probability of transition from a low cost point to a high cost solution. In the conventional simulated annealing, the temperature declines constantly, providing the search with a higher transition probability in the beginning of the search and lower probability toward the end of the search. In this paper, we propose a modified simulated annealing method that uses an adaptive cooling schedule that declines based on the number of consecutive downward moves. The performance of this algorithm is favourably compared with those of the literature and the results indicate that the algorithm is capable of finding optimal or near optimal solutions in reasonably short computational time.