A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation
19 Mar 2019-Vol. 7, Iss: 3, pp 278
TL;DR: 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 is proposed.
Abstract: 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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TL;DR: In this paper, a five-dimensional digital twin (DT) is proposed to fuse both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed.
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TL;DR: It can be concluded that the establishment of intelligent DSS for enterprises in manufacturing industry and the utilization of data mining technology as the key technology to achieve the system can make the decision-making of the manufacturing enterprises more effective and scientific.
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TL;DR: The results show that the Business Process Model and Notation, Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing.
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References
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TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.
937 citations
TL;DR: This paper explores the future research direction in SDS and discusses the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Abstract: Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
308 citations
TL;DR: This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs and generates managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.
Abstract: Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, wh...
297 citations
TL;DR: An intelligent agent based dynamic scheduling system that selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent.
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"A Decision Support System for Dynam..." refers background in this paper
...Aydin and Oztemel [10] developed an approach for the solution of the dynamic job-shop type scheduling problem by using the agent and the simulated environment....
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TL;DR: Five new dispatching rules for scheduling in a job shop are presented and it has been observed that the proposed rules are not only simple in structure, but also quite efficient in minimizing several measures of performance.
228 citations