Abstract
Actual manufacturing enterprises usually solve the production blockage problem by increasing the public buffer. However, the increase of the public buffer makes the flexible flow shop scheduling rather challenging. In order to solve the flexible flow shop scheduling problem with public buffer (FFSP–PB), this study proposes a novel method combining the simulated annealing algorithm-based Hopfield neural network algorithm (SAA–HNN) and local scheduling rules. The SAA–HNN algorithm is used as the global optimization method, and constructs the energy function of FFSP–PB to apply its asymptotically stable characteristic. Due to the limitations, such as small search range and high probability of falling into local extremum, this algorithm introduces the simulated annealing algorithm idea such that the algorithm is able to accept poor fitness solution and further expand its search scope during asymptotic convergence. In the process of local scheduling, considering the transferring time of workpieces moving into and out of public buffer and the manufacturing state of workpieces in the production process, this study designed serval local scheduling rules to control the moving process of the workpieces between the public buffer and the limited buffer between the stages. These local scheduling rules can also be used to reduce the production blockage and improve the efficiency of the workpiece transfer. Evaluated by the groups of simulation schemes with the actual production data of one bus manufacturing enterprise, the proposed method outperforms other methods in terms of searching efficiency and optimization target.
Highlights
With rapid development of information, advanced manufacturing, and artificial intelligence technology, Germany proposed “Industry 4.0” national strategy, which promotes progress in manufacturing industry and provides producing solution schemes for many complex industrial systems [1,2,3]
The simulated annealing algorithm-based Hopfield neural network algorithm (SAA–Hopfield neural network (HNN)) algorithm and other global optimization algorithms were combined with the local scheduling rules, respectively, to solve the FFSP–PB problem under different data scales, which verified the optimization performance of the SAA–HNN algorithm with respect to the complex scheduling problems
The above analysis indicates that the public buffer adds in the flexible flow shop and relevant local scheduling rules can be established in order to relieve the production blockage effectively
Summary
With rapid development of information, advanced manufacturing, and artificial intelligence technology, Germany proposed “Industry 4.0” national strategy, which promotes progress in manufacturing industry and provides producing solution schemes for many complex industrial systems [1,2,3]. It is necessary to explore an effective scheduling method for the flexible flow shop scheduling problem with public buffer (FFSP–PB) due to its role in reducing the production blockage and improving the utilization of production resources [16,17] It is of great theoretical and engineering value to solve this problem. Only a few investigators have addressed the issue of solving the production blockage led by limited buffer stresses on adjusting the production plan and buffer space via alleviating the production blockage by setting the public buffer in the workshop They have not explored the impact of transit time on the production process caused by the movement of the workpiece between public buffer and limited buffer among the stages. The relevant scheduling optimization technology for automatic production lines with public buffer has an extensive application prospect, which would improve the intellectualization of the manufacturing automation technology
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