Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0 Abstract   Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task reassignment solutions for the assembly line rebalancing problem with the assumption that the rebalancing decision has been made already. There is limited literature exploring online adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because realtime monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and nonlinearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of realtime production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the realtime information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be rebalanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.

Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time A general assembly shop in an aerospace industry has extremely unbalanced workstation time and several reentry processes. The capacity allocation of bufferlike workstations has been identified as a crucial factor of production improvement. Experience based whatif analyses based on simulation of feasible alternatives are timeconsuming and cannot get the optimization solutions. In this study, a data driven simulationbased optimization framework is presented and used to redesign the capacity of bufferlike workstations in the line. The assembly processes with distinct characteristics are introduced and a data driven method is adopted to build the different structures of the simulation model. An optimization problem with constraints is formulated and solved based on genetic algorithm, which uses selfadoptive refusal and penalty function strategy to improve the performance. The case study of the real system is carried out and the results show that the data driven simulationbased optimization is efficient to the problem.