The assembly of personalized products with high fluctuations in demand is imposed by the production paradigm of personalized products. This opens the problem of managing the assembly system. Decision-making should be based on available information from the assembly process. To contribute to better management of the assembly of personalized products, the APPINT (APPlicable and INTeractive) algorithm and the workstation complexity indicator (WCI) are developed in this research. Besides, the information integration framework based on the developed algorithm and indicator is proposed, to track real information from the assembly process and to use it for decision-making. The APPINT algorithm describes an assembly system considering several crucial aspects that affect productivity such as material flows, the precedence and duration of assembly tasks, workstation layout, worker schedules and their available time. Also, it enables the optimization of mentioned aspects. The workstation complexity indicator is used to show the distribution of complexity loads on workstations and its impact on assembly productivity. A specially designed APPINT algorithm based on the theory of graphs and linear programming, together with a complexity indicator, gives the result of optimization problems within the given constraints of the observed system. To validate the effectiveness of the proposed framework for information integration, the case study was made in the real environment.

需求波动较大的个性化产品的组装是由个性化产品的生产范式强加的。这就提出了管理装配系统的问题。决策应基于装配过程中的可用信息。为了更好地管理个性化产品的装配，本研究开发了APPINT（APPlicable and INteractive）算法和工作站复杂度指标（WCI）。此外，还提出了基于所开发的算法和指标的信息集成框架，以跟踪装配过程中的真实信息并将其用于决策。 APPINT 算法描述了一个装配系统，考虑了影响生产力的几个关键方面，例如物料流、装配任务的优先级和持续时间、工作站布局、工人时间表及其可用时间。此外，它还可以优化上述方面。工作站复杂性指标用于显示工作站上复杂性负载的分布及其对装配生产率的影响。基于图和线性规划理论专门设计的 APPINT 算法与复杂性指标一起给出了观察系统给定约束内优化问题的结果。为了验证所提出的信息集成框架的有效性，在真实环境中进行了案例研究。