scholarly journals A Dynamic Route-Planning System Based on Industry 4.0 Technology

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 308
Author(s):  
Duy Nguyen Duc ◽  
Thong Tran Huu ◽  
Narameth Nananukul
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Industry 4.0 ◽  
Big Data Analytics ◽  
Route Planning ◽  
Research Area ◽  
Automated Guided Vehicles ◽  
Planning System ◽  
Routing Problem ◽  
Dynamic Route

Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.

scholarly journals Machine Learning Using Big Data Link Stability Based Node Observation for IoT Security

2021 ◽  
Author(s):  
R. Ganesh Babu ◽  
S. Yuvaraj ◽  
A. VedanthSrivatson ◽  
T. Ramachandran ◽  
G. Vikram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Big Data Analytics ◽  
Research Problem ◽  
Research Literature ◽  
Handheld Devices ◽  
Data Link ◽  
Iot Security ◽  
Primary Driver

IoT systems create a multi-hop organizational structure among mobile devices in required to send on data groups. The remarkable properties of gadgets frameworks cause communications to interconnect among competing handheld devices. Most physiological directing displays don’t believe secure associations all through bundle communication to organize high communicate ability and genetic blocks that also prompts increased delay as well as bundle decreasing in mastermind. Only with continued growth and transformation of IoT networks, attacks on such IoT systems are increasing at an alarming rate. Our purpose will provide researchers with a research resource on latest research patterns in IoT security. As the primary driver of with us research problem concerning IoT security as well as machine learning. This analysis of the literature among the most research literature in IoT security recognized some very key current research which will generate organizational investigations. Only with fast emergence of different IoT threats, it is essential to develop frameworks that could integrate cutting-edge big data analytics and machine learning advanced technologies. Effectiveness are critical quality variables in shaping the best methods and algorithms for detecting IoT threats in real-time or close to real time.

Dynamic route planning with real-time traffic predictions

2017 ◽  
Vol 64 ◽  
pp. 258-265 ◽  
Author(s):  
Thomas Liebig ◽  
Nico Piatkowski ◽  
Christian Bockermann ◽  
Katharina Morik
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Real Time Traffic ◽  
Dynamic Route

scholarly journals Novel Route Planning System for Machinery Selection. Case: Slurry Application

2020 ◽  
Vol 2 (3) ◽  
pp. 408-429 ◽  
Author(s):  
Mahdi Vahdanjoo ◽  
Christian Toft Madsen ◽  
Claus Grøn Sørensen
Keyword(s):  
Vehicle Routing Problem ◽  
Route Planning ◽  
Optimal Solution ◽  
Application Rate ◽  
Planning System ◽  
Application Process ◽  
Optimal Sequence ◽  
Routing Problem ◽  
Optimization And Simulation ◽  
Working Distance

The problem of finding an optimal solution for the slurry application process is casted as a capacitated vehicle routing problem (CVRP) in which by considering the vehicle’s capacity, it is required to visit all the tracks only once to fully cover the field, as well as complying with a specified targeted application rate. A key objective in this study was to determine an optimized coverage plan in order to minimize the driving distance in the field, while at the same time allowing for varying the application rate. The coverage plan includes the optimal sequence of tracks with a specified application rate for each track. Two algorithms were developed for optimization and simulation of the slurry application cast as capacitated operations. In order to validate the proposed algorithms, a slurry application operation was recorded, and the results of the optimization algorithm were compared with the conventional non-optimized method. The comparison showed that applying the proposed new method reduces the non-working distance by 18.6% and the non-working time by 28.1%.

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