A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems

Kück, Mirko; Ehm, Jens; Freitag, Michael; Frazzon, Enzo Morosini; Pimentel, Ricardo

doi:10.4028/www.scientific.net/amr.1140.449

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…Kuck et al [19] proposed a data-driven simulation-based optimization algorithm for the control of dynamic production systems. In the present study, it was emphasized that flexibility in production is very important.…”

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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“…In the environment of industrial Internet of things (IIoT), real-time manufacturing resources allocation (RTMRA) can be further developed to make full use of the interconnection among manufacturing resources to achieve intelligent cooperation [ 17 ]. Therefore, it is timely and crucial to consider adaptive scheduling and control (i.e., RTMRA) for dynamic manufacturing environments as crucial research issues in smart production management [ 18 ]. The timely feedback shop floor information during the manufacturing execution stage leads to a significant improvement in achieving real-time production scheduling [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory

Yang

Cao

et al. 2020

Sensors

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In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in the factory, such as the industrial Internet of things (IIoT) and cloud manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with a random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

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“…In the environment of Industrial Internet of Things (IIoT), real-time manufacturing resources allocation (RTMRA) can be further developed to make full use of the interconnection among manufacturing resources to achieve intelligent cooperations [17]. Therefore, it is timely and crucial to consider adaptive scheduling and control (i.e., RTMRA) for dynamic manufacturing environments as crucial research issues in smart production management [18]. The timely feedback shop floor information during the manufacturing execution stage leads to significant improvement in achieving real-time production scheduling [19].…”

Section: Rlated Workmentioning

confidence: 99%

“…The tasks in taskpool can be described as , where ∈ [1, ]. Normalized urgency of a task in task-pool at time ，denoted by (18).…”

Section: Real-time Information Model Of Tasks For Multi-customermentioning

confidence: 99%

Real-time Production and Logistics Self-adaption Scheduling based on Information Entropy Theory

Yang

Cao

et al. 2020

Preprint

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In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

show abstract

A Data-Driven Simulation-Based Optimisation Approach for Adaptive Scheduling and Control of Dynamic Manufacturing Systems

Cited by 20 publications

References 22 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

Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory

Real-time Production and Logistics Self-adaption Scheduling based on Information Entropy Theory

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