Abstract: Since the collaborative and cooperative manufacturing mode is gaining popularity, which has the merit of utilizing superior resources in collaborative factories to improve production efficiency, thus it becomes important to do the study on scheduling in collaborative manufacturing mode. In a collaborative manufacturing mode, there are multiple processing paths that forming the collaborative manufacturing networks, and thus the corresponding networks are dynamic and changable. Additionally, different type products belong to different type collaborative manufacturing networks and this makes our model more complex. In light of that, four general types of collaborative manufacturing networks G_p are constructed and discussed, including balance type network, bottleneck type network, jump type network, and hybrid type network. Some pramaters of scheduling model are also designed to make problem more reasable, i.e., production cost function, earliest delivery time t_f^k and latest delivery time t_l^k. An objective function composed of processing costs W_c^m, inventory costs W_s^k(Q_k, T'_k), and the two penalty costs of early completion costs W_s^k(Q_k, T″_k) and tardiness costs W_l^k(Q_k, T'″_k) is then constructed. In order to solve our model, an improved ant colony optimization algorithm is presented, into which the Monte Carlo algorithm is incorporated. In particular, the upper confidence bound z_i is used to guide the ant selection. Meanwhile, a moving window award mechanism[min, max] is also designed to improve award criteria. Given that the expectation window moves frequently with the increase of simulation, and thus the credible level of expectation window increases. In view of this, the fixed pheromone concentration should multiply a balance coefficient k(N) as the award value to improve the rewards credibility. The simulation results show that our designed model is reasonable and practical. Meanwhile, our proposed algorithm has fast solving speed, good convergence and stability. Our research work is benefit for enterprise scheduling in collaborative manufacturing mode.

Key words: collaborative manufacturing network, collaborative scheduling optimization, improved ant colony optimization algorithm, Monte Carlo method, supply chain scheduling