IoT-enabled dynamic lean control mechanism for typical production systems

Zhang, Kai; Qu, Ting; Zhou, Dajian; Thürer, Matthias; Liu, Yang; Nie, Duxian; Li, Congdong; Huang, George Q.

doi:10.1007/s12652-018-1012-z

Cited by 44 publications

(18 citation statements)

References 48 publications

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“…Zhang et al [31] emphasized the importance of learn concepts in operational management, especially the importance of the lean approach in Industry 4.0. In this study, process control theory was used for lean methods.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

et al. 2019

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The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule.

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Section: Related Workmentioning

confidence: 99%

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

et al. 2019

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“…Leng J et al [19] proposed an intelligent modeling method based on real-time perception and decision-making of service state of bottom manufacturing resources in CPS in order to meet the requirements of self-organizing allocation and adaptive collaborative control of manufacturing resources in intelligent factories. Ting Qu [20] et al…”

Section: Literature Reviewmentioning

confidence: 99%

A Referenced Cyber Physical System for Compressor Manufacturing

Jin¹,

Wang

Lu³

2020

MATEC Web Conf.

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Compressor is a typical high-end discrete product，with the shortening of product life cycle and the enhancement of the degree of product customization, the traditional compressor manufacturing system architecture cannot meet the requirements of comprehensive digital management of compressor from body scheme design to parts production line, logistics management, operation and maintenance monitoring and evaluation. This paper presents a compressor manufacturing system architecture based on digital twinning, and establishes an Internet platform for compressor industry oriented to remote coordination from three aspects of compressor design, production, operation and maintenance. The platform includes industrial Internet infrastructure layer, physical space entity model layer, virtual space multidimensional model layer, physical space and virtual space multidimensional model correlation and mapping layer, big data intelligent analysis decision-making layer, and digital twin application layer. Through the establishment of the compressor product design and simulation model of digital twin, compressor production process digital twin model, compressor fault diagnosis and remote operations digital twin model, implementation is based on the number of compressor collaboration in manufacturing industrial Internet platform twin system, leading the transformation and upgrading of intelligent manufacturing industry, compressor industry sustainable development ability and international competitiveness.

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“…Lean manufacturing can be considered one of the most significant contributions in the history of operations management [1,2]. However, since its inception, most of the developments in traditional lean manufacturing tools are almost done independently from using information technology [3].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems

et al. 2020

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Lean manufacturing is one of the most popular improvement agents in the pursuit of perfection. However, in today's complex and dynamic manufacturing environments, lean tools are facing an inevitable death. Industry 4.0 can be integrated with lean tools to avoid their end. Therefore, the primary purpose of this paper is to introduce an Industry 4.0-based lean framework called dynamic value stream mapping (DVSM) to digitalize lean manufacturing through the integration of lean tools and Industry 4.0 technologies. DVSM with its powerful features is proposed to be the smart IT platform that can sustain lean tools and keep them alive and effective. This paper specifically tackles the scheduling and dispatching in today's lean manufacturing environments, where the aim of this research is developing a smart lean-based production scheduling and dispatching model to achieve the lean target through optimizing the flow along the VSM and minimizing the manufacturing lead time. The developed model, called the real-time scheduling and dispatching module (RT-SDM), runs on DVSM. The RT-SDM is represented through a mathematical model using mixed integer programming. Part of the testing and verification process, a simplified IT-based software, has been developed and applied on a smart factory lab.Typically, the scheduling level in lean manufacturing uses simple tool called a heijunka-board (i.e., load-leveling box) at the pacemaker workstation to enhance the pull system. Here, "kanban" acts as the nervous system of the pull system through directing materials just-in-time to workstations. Accordingly, continuous flow, i.e., "one-piece flow", as well as the "takt-time", which is used to synchronize the pace of production with the pace of demand, will be achieved. This works well in cases of high-volume and low-mix working environment. However, these lean tools are inefficient in today's highly dynamic and customized manufacturing environment with uncertainty in demand and materials supply, unpredictable material flow, high variety of products that move through different routes and sequences of workstations, different priorities, process times, due dates, etc. Moreover, in a dynamic system, kanban is unable to determine the right time for dispatching and assigning a job into the machines based on changes in production constraints, like customer importance, due date, quantity, sequence of the job, the resource availability, and current workload on the PSF [6]. Therefore, the Dispatching Rules (DRs) play an important role to meet the scheduling level. The importance of DRs lies in decreasing variability, reducing waiting times, increasing utilization of resources, and improving the production smoothness [7]. In the literature, many dispatching priority or sequencing rules, such as first come, first served (FCFS), Shortest Processing Time (SPT), and Earliest Due Date (EDD), have been proposed and investigated [8][9][10][11]. For example, FCFS selects the jobs arriving at a workstation first, which means the first job will be processed fi...

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IoT-enabled dynamic lean control mechanism for typical production systems

Cited by 44 publications

References 48 publications

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

A Referenced Cyber Physical System for Compressor Manufacturing

Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems

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