An improved model and novel simulated annealing for distributed job shop problems

To benefit from globalization, the single-facility scheduling problem is extended to a distributed multi-facility level. This paper tackles both the mathematical modeling and solution techniques for the problem of distributed job shop scheduling. The problem is first mathematically formulated by a mixed integer linear programming model. This model supersedes the best available model in the literature in terms of both size and computational complexities. Moreover, novel simulated annealing algorithms are developed. These algorithms propose an advanced move operators matching the special encoding scheme used. The developed near-optimal search method has been enhanced by combining it with a local search method and adding advanced features to it such as the introduced restart phase. Also, we have hybridized the developed metaheuristic further with a greedy algorithm. Using the Taguchi method, the algorithm is finely tuned. Numerical experiments are conducted to evaluate the performance of the algorithms against an available genetic and a greedy algorithm. The results show that the proposed algorithm outperforms both algorithms.