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

摘要： 光伏是新能源战略的重要支撑,电池片作为其重要组件,市场需求巨大。由于光伏电池片生产工序及设备较多,设备故障时有发生,从而影响生产效率。当前普遍采用人工规划产能的模式,无法及时应对生产中断及计划调整。同时,相关制造企业纷纷引入自动导引小车(AGV)等自动化设备保障高效生产,但设备高成本使企业面临资金压力。在此背景下,本文基于一家典型的电池片制造企业实际生产问题,研究产能动态规划与AGV配置方案。首先,考虑机台故障与缓存,以最大化产能为目标建立数学模型。其次,基于约束理论,提出三种缓存使用策略下的产能动态规划算法。此外,本文提出AGV配置算法,计算不同生产条件下车间AGV的最优数量。实验结果验证了“优先考虑使用缓存”策略的有效性,并分析了AGV搬运时间对产能和数量的影响。研究成果不仅能够帮助电池片制造车间实时规划产能,决策AGV配置方案,还能拓展应用到其他产能不平衡车间的生产与物流调度场景。

关键词: 缓存策略, 机台故障, 产能规划, 启发式算法