Research On Ant Colony Optimization Algorithm For Collaborative Manufacturing Scheduling

As one of the core issue for manufacturing system optimization, scheduling problem has received great attentions. Under the trend of globalization and networked in manufacturing, how to solve the problem of collaborative scheduling on the multi-machine, multi-workshop, and even the multi-factory is become a new issues of production scheduling research which needed to be solved urgently.Ant colony optimization (ACO) algorithm is a metaheuristic algorithm, inspired by the food-seeking behavior of real ants, which can be applied to the solution of combinatorial optimization problems. In this paper, the problem of collaborated scheduling in jobshops is considered and a new improved ant colony optimization (IACO) algorithm is proposed.According to the characteristics of scheduling problems and the shortcoming of ant colony optimization is often fall into local optimization solution, the improved ant colony optimization algorithm which integrated the simulated annealing processes to the ant colony optimization algorithm, is proposed to solve the complex scheduling problems. In particular, we give the specific design idea of operation selection rules, pheromone updating rules and new solution generating rules. Here, the ant colony optimization is used to provide the initial value to the simulated annealing that to improved the search efficiency, and the simulated annealing is used to help the intermediate result jumping out of local optimum; besides, the bi-directional convergence strategy is used to accelerate the convergence speed.To test and confirm the optimal facilities of the proposed improved ant colony optimization algorithm, it is applied to solve the job shop scheduling problem and the flexible job shop scheduling problem. The new solution generating rules and machine selection rules are elaborated under the objective of minimize the makespan. It was found after extensive computational investigation that the proposed improved ant colony optimization algorithm gives better results, faster convergence rate and higher stability of the solution, as compared to those solutions given by the existing algorithm for the scheduling problem under study.For the collaborative manufacturing scheduling problem under the distributed multi-shop manufacturing environment, both the job shop and machine selection rules are exquisitely designed. Under these rules, jobshops could choose the best composition of jobs, as well as the jobs could select the best combination of machines to achieve a collaborative and optimal schedule. Two existing modified genetic algorithm and the proposed improved ant colony optimization algorithm were compared. The results show that the proposed algorithm works well when solving the complex collaborated scheduling problem.