Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network

2022 ◽  
Vol 74 ◽  
pp. 102283
Author(s):  
Yuxin Li ◽  
Wenbin Gu ◽  
Minghai Yuan ◽  
Yaming Tang
Keyword(s):  
Real Time ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Data Driven ◽  
Flexible Job Shop ◽  
Time Data ◽  
Real Time Data

Heart Watch: Dynamical Systems Based Real Time Data Driven ECG Synthesis

2021 ◽  
Author(s):  
Ashutosh Simha ◽  
Suryansh Sharma ◽  
Sujay Narayana ◽  
R. Venkatesh Prasad
Keyword(s):  
Dynamical Systems ◽  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

A REAL-TIME DATA-DRIVEN CONTROL SYSTEM FOR MULTI-MOTOR-DRIVEN MECHANISMS

2017 ◽  
Vol 32 (6) ◽  
Author(s):  
Huashan Liu ◽  
Lingbin Zeng ◽  
Wuneng Zhou ◽  
Shiqiang Zhu
Keyword(s):  
Control System ◽  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

scholarly journals Real-time data-driven interactive rough sketch inking

2018 ◽  
Vol 37 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Edgar Simo-Serra ◽  
Satoshi Iizuka ◽  
Hiroshi Ishikawa
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

scholarly journals Real-time data-driven average active period method for bottleneck detection

2016 ◽  
Vol 11 (3) ◽  
pp. 428-437 ◽  
Author(s):  
M. Subramaniyan ◽  
A. Skoogh ◽  
M. Gopalakrishnan ◽  
A. Hanna
Keyword(s):  
Real Time ◽  
Data Driven ◽  
Active Period ◽  
Time Data ◽  
Bottleneck Detection ◽  
Real Time Data

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

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 278 ◽  
Author(s):  
Ahmet Turker ◽  
Adnan Aktepe ◽  
Ali Inal ◽  
Olcay Ersoz ◽  
Gulesin Das ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Job Shop ◽  
Performance Criteria ◽  
Dispatching Rules ◽  
Dispatching Rule ◽  
Time Data ◽  
Real Time Data

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.

Sign in / Sign up

Export Citation Format

Share Document