关键词: 缓存策略, 机台故障, 产能规划, 启发式算法

Abstract: For the past years, as the market size of the photovoltaic industry has continued to expand, the demand for battery cells as the core component of photovoltaic power generation has increased significantly. However, the production technology of photovoltaic cells is complex, involving multiple processes and precision machinery. Photovoltaic cell manufacturers are generally challenged by high costs and low efficiency. On the one hand, in the photovoltaic cell manufacturing workshop, different processes have different numbers of machines and processing speeds, and the maximum productivity of the workshop is limited by the productivity of the bottleneck process. It is a challenge for the photovoltaic cell production workshop to balance the productivity of machines in different processes and ensure the maximum production efficiency of bottleneck processes. In addition, due to a large number of machines in the workshop, it is difficult to repair and maintain them in a timely manner, and the workshop inevitably encounters unexpected accidents such as machine breakdowns. This may cause the bottleneck process to shift, which in turn causes production reduction, production stoppage and other problems, resulting in reduced workshop productivity and wasted production resources. Therefore, after the machine breakdown, adjusting the production speed of other machines is particularly important. But in fact, most enterprises still rely on manual to plan the workshop productivity. This method lacks science and timeliness, and cannot meet the enterprise cost reduction and efficiency of the real needs. On the other hand, with the promotion and implementation of intelligent manufacturing, automatic guided vehicle (AGV) as an important symbol of factory intelligence, has become the main material handling tool in the production workshop of photovoltaic cells. AGVs have the advantages of high efficiency and accuracy, but their purchase cost is also a major challenge for most companies. Therefore, making a reasonable decision on the number of AGVs to be purchased has become another challenge for photovoltaic cell manufacturing enterprises.
   This paper studies a dynamic productivity planning and AGV configuration problem in a photovoltaic cell manufacturing shop based on the actual production problem of a typical photovoltaic cell manufacturing company. The problem considers the characteristics of fixed capacity caching, random machine failures, and AGV handling materials. In order to solve the problem, firstly, this paper establishes a mixed integer linear programming model with the objective of maximizing the daily productivity of the workshop. Secondly, we propose two cache usage strategies based on the constraint theory, namely, “prioritizing the use of cache and prioritizing the consistency of the productivity of each process”, and design the corresponding productivity dynamic planning algorithms respectively. Meanwhile, this paper proposes a control algorithm to ensure that the productivity of all processes is always consistent with the productivity of the bottleneck process, which is a commonly used productivity planning scheme in current production workshops. Furthermore, since AGVs are a key component of production and cost, this paper introduces an AGV allocation algorithm. This algorithm is employed to calculate the optimal number of AGVs under different production conditions. Finally, in order to compare the performance of the two productivity planning schemes proposed in this paper with the actual schemes adopted by enterprises, we extract the actual workshop data, and set up three schemes, namely, “Prioritizing the use of caching”, “Prioritizing the maintenance of productivity consistency”, and the no-caching strategy, with different breakdown scenarios. The experiments are conducted under different breakdown scenarios to explore the changes in productivity and machine working time. The experimental results show that the strategy of having cache zones and giving priority to the use of caches can help enterprises better cope with unexpected situations such as machine breakdowns. In addition, through the simulation experiments, this paper calculates the optimal number of AGV configurations for the three scenarios when there is no machine breakdown. Moreover, the effect of AGV handling time on the productivity and the number of AGV configurations are further discussed. The research results can not only help the cell manufacturing workshop to plan the production productivity in real time and decide the AGV configuration scheme, but also can be extended to other workshop production and logistics planning scenarios with productivity imbalance.

Key words: cache strategy, machine failure, productivity planning, heuristic algorithm