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|Title:||A study of real-time assembly line balancing in the context of industry 4.0||Authors:||Huo, Jiage||Advisors:||Chan, T. S. Felix (ISE)
Lee, K. M. Carmen (ISE)
Manufacturing industries -- Technological innovations
|Issue Date:||2020||Publisher:||The Hong Kong Polytechnic University||Abstract:||Assembly Line Balancing Problem (ALBP) is important to sustain the efficiency of the assembly process. With the development of complex products, the problem size and the operations' complexity in the assembly process are increasing. Consequently, the development of new approaches to suit the complex assembly environment is urgent. Besides, some researchers explored the task assignment plan for ALBP 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 re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertain factors into the workload balancing process because of the randomness and non-linearity 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 in the context of Industry 4.0, 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 real-time production data in the era of Industry 4.0, but there is little literature considering this new context. To solve ALBP efficiently, a hybrid approach which executes the Ant Colony Optimization in combination with Beam Search (ACO-BS) is proposed. The results of 269 benchmark instances show that for 95.54% of the instances, optimal solutions can be found within 360 CPU time seconds. In addition, order strength and time variability are chosen to indicate the complexity of ALBP instances, and the processing times are generated following a unimodal or a bimodal distribution. Then, 27 instances with a total of 400 tasks are generated randomly. The comparison results show that ACO-BS shows advantages in solving the large-scale random instances.
To monitor and control an assembly line in real time, a fuzzy control system composed of two types of fuzzy controllers is developed. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced, 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. Furthermore, the above fuzzy control system is developed with the assumption that the processing ability of each operative machine is constant. However, the degradation process of a machine can be divided into two or more phases, and the switching of the health state will bring significant changes to the processing times of tasks. Therefore, a new fuzzy control system is developed for the context where each machine's health state is generated randomly following a three-state Markov chain. The workload of each workstation is adjusted to match the production ability of the workstation. Compared with the two assembly lines without the fuzzy control system, the assembly line with the proposed fuzzy control system adjusts the assembly plan in time and achieves higher utilization of machines and lower average buffer level, without the expense of production reduction. In conclusion, the main contributions are concluded in two aspects. To begin with, an efficient algorithm, ACO-BS, is developed to deal with the large-scale ALBP. More importantly, in the context of Industry 4.0, a fuzzy control system is developed to monitor and adjust the assembly process in real time, and the performance of an assembly line with the proposed fuzzy control system is better, in terms of average buffer level and utilization of machines. The research findings shed light on the smart control of the assembly process and provide references for practitioners who are considering the adoption of new technologies involved in Industry 4.0.
|Description:||xvii, 140 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P ISE 2020 Huo
|URI:||http://hdl.handle.net/10397/81944||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
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Citations as of May 6, 2020
Citations as of May 6, 2020
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