A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints

Xiong, Hegen; Fan, Huali; Jiang, Guozhang; Li, Gongfa

doi:10.1016/j.ejor.2016.07.030

Cited by 77 publications

(29 citation statements)

References 64 publications

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“…Xiong et al [23] proposed a simulation-based model for determining dispatching rules in a dynamic scheduling problem where job release times and extended technical priority constraints are included. The proposed algorithm reduced the total tardiness and the number of tardy jobs.…”

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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“…Unfortunately, there is a lack of authority on how to determine and select the two, which is mainly dependent on the result of the actual noise reduction effect of the researchers according to their experimental results [41][42][43]. The current actual noise reduction effect is mainly evaluated from the angle of the signal itself and the noise, and this paper is based on the two values of the mean square root and the signal to noise ratio gain in the noise reduction effect to determine the appropriateness of the number of decomposition layers; the noise reduction effect is negatively correlated with the mean square root error and is positively related to the signal to noise ratio gain [44][45][46].…”

Section: Wavelet Threshold Noise Reduction Of Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

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107

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In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

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“…To deal with the dynamic flexible job shop scheduling problem considering machine failure, urgent job arrival, and job damage as disruptions, Wang et al [39] adopted a modified GA to construct rescheduling strategy. Xiong et al [40] simulated and analyzed dispatching rules for dynamic job shop scheduling. To achieve the real-time data-driven optimization decision, Zhang et al [41] proposed a dynamic optimization model for flexible job shop scheduling based on game theory.…”

Section: A Adaptive Schedulingmentioning

confidence: 99%

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

2020

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A simulation-based study of dispatching rules in a dynamic job shop scheduling problem with batch release and extended technical precedence constraints

Cited by 77 publications

References 64 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

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN

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