Automatic Task Provisioning and Routing Framework for Carrier Robots in Smart Factory with Edge Computing

Keywords: Dial-a-Ride-Problem, Edge Computing, Particle Swarm Optimization, Pickup and Delivery, Smart Factory

Abstract

This paper proposed a pickup and delivery (P&D) task provisioning and vehicle routing framework for carrier robots in a factory with edge servers assisting as the intermediary between robots and working stations, where the task requests come. The carrier robots must pick up and deliver each assigned load from and to designated locations with minimal traveled distance and without violating designated constraints. Edge servers are utilized to facilitate communication between the main server and delivery robots and assist the main server in deciding the best robot for each incoming task request and the updated route to facilitate execution of tasks by each carrier robot. The problem of pickup and delivery for each robot is modeled based on Dial-a-Ride Problem, and a discrete bio-inspired algorithm is proposed to solve this problem. The tasks are distributed to edge servers and carrier robots by taking their service loads into account. The simulation results show that the proposed framework can provide an effective solution towards optimizing the pickup and delivery process in a smart factory.

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Author Biographies

Philip Tobianto Daely

Department of Information Technology, Telkom University, Surabaya, Indonesia.

Oktavia Ayu Permata

Department of Information Technology, Telkom University, Surabaya, Indonesia.

Bernadus Anggo Seno Aji

Department of Information Technology, Telkom University, Surabaya, Indonesia.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
        Views : 309
2025-03-25
    Downloads : 232
How to Cite
[1]
P. T. Daely, O. A. Permata, and B. A. S. Aji, “Automatic Task Provisioning and Routing Framework for Carrier Robots in Smart Factory with Edge Computing”, International Journal of Recent Technology and Applied Science, vol. 7, no. 1, pp. 1-16, Mar. 2025.
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Articles

References

A. Yildirim, H. Reefke, and E. Aktas, Mobile Robot Systems and Their Evaluation. Cham: Springer International Publishing, 2023, pp. 17–47.

H. Ding, Y. Huang, J. Shi, Q. Shi, and Y. Yang, “A Novel Industrial AGV Control Strategy Based on Dual-Wheel Chassis Model,” Assembly Automation, vol. 42, no. 3, pp. 306–318, 2022.

X. Liang, G. H. de Almeida Correia, K. An, and B. van Arem, “Automated Taxis’ Dial-a-Ride Problem with Ride-Sharing Considering Congestion-Based Dynamic Travel Times,” Transportation Research Part C: Emerging Technologies, vol. 112, pp. 260–281, 2020.

K. Gkiotsalitis, “The Dial-a-Ride Problem Considering the in-Vehicle Crowding Inconvenience due to COVID-19,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 3746–3751.

M. Abedi, R. Chiong, R. Athauda, H. Seidgar, Z. Michalewicz, and A. Sturt, “A Regional Multi-Objective Tabu Search Algorithm for a Green Heterogeneous Dial- a-Ride Problem,” in 2019 IEEE Congress on Evolutionary Computation (CEC), 2019, pp. 2082–2089.

A. Syed, I. Gaponova, and K. Bogenberger, “Neural Network-Based Metaheuristic Parameterization with Application to the Vehicle Matching Problem in Ride-Hailing Services,” Transportation Research Record, vol. 2673, no. 10, pp. 311–320, 2019.

Q. Tang and M. G. Armellini, “An Ant Colony Algorithm with Penalties for the Dial-a- Ride Problem with Time Windows and Capacity Restriction,” in 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2021, pp. 1–6.

M. S. Hossain, C. I. Nwakanma, J. M. Lee, and D.-S. Kim, “Edge Computational Task Offloading Scheme Using Reinforcement Learning for IIoT Scenario,” ICT Express, vol. 6, no. 4, pp. 291–299, 2020. [Online]. Available: https: //doi.org/10.1016/j.icte.2020.06.002. [Accessed: August, 2024].

D. Guo, S. Gu, J. Xie, L. Luo, X. Luo, and Y. Chen, “A Mobile-Assisted Edge Computing Framework for Emerging IoT Applications,” vol. 17, no. 4, Jul 2021.

W. Liang, Y. Ma, W. Xu, Z. Xu, X. Jia, and W. Zhou, “Request Reliability Augmentation with Service Function Chain Requirements in Mobile Edge Computing,” IEEE Transactions on Mobile Computing, vol. 21, no. 12, pp. 4541–4554, 2022.

W. Dai, H. Nishi, V. Vyatkin, V. Huang, Y. Shi, and X. Guan, “Industrial Edge Computing: Enabling Embedded Intelligence,” IEEE Industrial Electronics Magazine, vol. 13, no. 4, pp. 48–56, 2019.

A. Rahman, J. Jin, A. Rahman, A. Cricenti, M. Afrin, and Y. Ning Dong, “Energy-Efficient Optimal Task Offloading in Cloud Networked Multi-Robot Systems,” Computer Networks, vol. 160, pp. 11 – 32, 2019.

L. Hu, Y. Miao, G. Wu, M. M. Hassan, and I. Humar, “iRobot-Factory: An Intelligent Robot Factory Based on Cognitive Manufacturing and Edge Computing,” Future Generation Computer Systems, vol. 90, pp. 569 – 577, 2019.

M. Afrin, J. Jin, A. Rahman, Y.-C. Tian, and A. Kulkarni, “Multi-Objective Resource Allocation for Edge Cloud Based Robotic Workflow in Smart Factory,” Future Generation Computer Systems, vol. 97, pp. 119 – 130, 2019.

Y. Lian, Q. Yang, W. Xie, and L. Zhang, “Cyber- Physical System-Based Heuristic Planning and Scheduling Method for Multiple Automatic Guided Vehicles in Logistics Systems,” IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7882–7893, 2021.

Z. Yan, B. Ouyang, D. Li, H. Liu, and Y. Wang, “Network Intelligence Empowered Industrial Robot Control in the F-RAN Environment,” IEEE Wireless Communications, vol. 27, no. 2, pp. 58–64, 2020.

R. A. de Omena, D. F. Santos, A. Perkusich, and D. C. Valadares, “Two-tier MPC Architecture for AGVs Navigation Assisted by Edge Computing in An Industrial Scenario,” Internet of Things, vol. 21, p. 100666, 2023.

P. T. Daely, A. Putri Anantha, J. M. Lee, and D.-S. Kim, “Distributed Computing Architecture for Logistic Job Allocation in Smart Factory,” in 2020 International Conference on Information and Communication Technology Convergence (ICTC), 2020.