2018Accesses
29Citations
6Altmetric
Abstract
Nowadays, industries have to deal with an increasingly competitive environment. In this sense, the operational process optimization to increase efficiency is crucial. The industrial systems are presently leaning toward every potential means of automation for improved accuracy and superior time management. There are significant challenges in industrial logistics management: streamline operations, customer service, transportation cost, planning, and risk management. Hence in this paper, the IoT-assisted Intelligent Logistics Transportation Management Framework has been proposed in the industrial environment to design an optimized logistics plan, improve customer service, and reduce transportation cost. This paper concentrates on identifying the optimal routes for the directed autonomous vehicle, considering different vehicles’ necessities, renewable generations, logistic requests, and the essential transportation system. The experimental results have been performed, and the proposed method enhances the overall performance ratio of 97.8% and customer satisfaction ratio of 98.9% and reduces the cost function ratio of 15.2%, energy consumption ratio of 11.4%, and computation time of 0.19 s compared to other existing methods.
This is a preview of subscription content,log in via an institution to check access.
Access this article
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
Price includes VAT (Japan)
Instant access to the full article PDF.











Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Ali MS, Gunasekaran N, Zhu Q (2017) State estimation of T–S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control. Fuzzy Sets Syst 306:87–104
Baskar S, Dhulipala VS (2018a) Secure and compact implementation of optimized Montgomery multiplier based elliptic curve cryptography on FPGA with road vehicular traffic collecting protocol for VANET application. Int J Heavy Veh Syst 25(3–4):485–497.https://doi.org/10.1504/IJHVS.2018.094835
Baskar S, Dhulipala VS (2018b) Collaboration of trusted node and QoS based energy multi path routing protocol for vehicular Ad Hoc networks. Wirel Pers Commun 103(4):2833–2842.https://doi.org/10.1007/s11277-018-5965-1
Bhatt M, Sharma S, Luhach AK, Prakash A (2016) Nature inspired route optimization in vehicular adhoc network. In: 2016 5th International conference on reliability, Infocom technologies and optimization (trends and future directions) (ICRITO), pp 447–451. IEEE
Cao Y, Jia F, Manogaran G (2019) Efficient traceability systems of steel products using blockchain-based industrial Internet of Things. IEEE Transactions on Industrial Informatics 16(9), 6004–6012
Daniel A, Subburathinam K, Muthu BA, Rajkumar N, Kadry S, Mahendran RK, Pandian S (2020) Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach. IET Intel Transp Syst 14(11):1410–1417
Ferrell W, Ellis K, Kaminsky P, Rainwater C (2020) Horizontal collaboration: opportunities for improved logistics planning. Int J Prod Res 58(14):4267–4284
Gonzalez-R PL, Canca D, Andrade-Pineda JL, Calle M, Leon-Blanco JM (2020) Truck-drone team logistics: a heuristic approach to multi-drop route planning. Transp Res Part C Emerg Technol 114:657–680
Gunasekaran N, Joo YH (2019) Robust sampled-data fuzzy control for non-linear systems and its applications: free-weight matrix method. IEEE Trans Fuzzy Syst 27(11):2130–2139
Gunasekaran N, Saravanakumar R, Joo YH, Kim HS (2019) Finite-time synchronization of sampled-data T-S fuzzy complex dynamical networks subject to average dwell-time approach. Fuzzy Sets Syst 374:40–59
Gunasekaran N, Zhai G, Yu Q (2020) Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit. Neurocomputing 413:499–511
Gupta M, Awaysheh FM, Benson J, Al Azab M, Patwa F, Sandhu R (2020) An attribute-based access control for cloud-enabled industrial smart vehicles. IEEE Trans Ind Inform
Hussain SA, Iqbal M, Saeed A, Raza I, Raza H, Ali A, Bashir AK, Baig A (2017) An efficient channel access scheme for vehicular ad hoc networks. Mob Inf Syst Hindawi 2017:1–10
Iranmanesh S, Raad R, Raheel MS, Tubbal F, Jan T (2019) Novel DTN mobility-driven routing in autonomous drone Logistics networks. IEEE Access 8:13661–13673
Kumar PM, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162
Liu J, Mirchandani P, Zhou X (2020) Integrated vehicle assignment and routing for system-optimal shared mobility planning with endogenous road congestion. Transp Res Part C Emerg Technol 117:102675
Lou K, Yang Y, Wang E, Liu Z, Baker T, Bashir AK (2020) Reinforcement learning based advertising strategy using crowdsensing vehicular data. IEEE Trans Intell Transp Syst
Luhach AK, Kumar SV, Poonia RC (2019) Speed of Things (SoT): evolution of Isolation-to-Intermingle (I2I) technology transition towards IoT. Recent Pat Comput Sci 12(4):354–360
Lv Z, Yang HAN, Singh AK, Manogaran G, Lv H (2020) Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Trans Ind Inform 17(2):1496–1504
Malik KR, Ahmad M, Khalid S, Ahmad H, Al-Turjman F, Jabbar S (2020) Image and command hybrid model for vehicle control using Internet of Vehicles. Trans Emerg Telecommun Technol 31(5):e3774
Manogaran G, Shakeel PM, Baskar S, Hsu CH, Kadry SN, Sundarasekar R et al (2020) FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics. IEEE Trans Fuzzy Syst 29(1):177–185
Manogaran G, Balasubramanian V, Rawal BS, Saravanan V, Montenegro-Marin CE, Ramachandran V, Kumar PM (2020) Multi-variate data fusion technique for reducing sensor errors in intelligent transportation systems. IEEE Sens J
Masłowski D, Kulińska E, Kulińska K (2019) Application of routing methods in city logistics for sustainable road traffic. Transp Res Procedia 39:309–319
Molano JIR, Lovelle JMC, Montenegro CE, Granados JJR, Crespo RG (2018) Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Hum Comput 9(3):709–723
Netra K, Manjunath KG (2019) An effective vehicular adhoc network using cloud computing: a review. In: 2019 9th International conference on cloud computing, data science & engineering (confluence), pp 69–74. IEEE
Qiao F, Wu J, Li J, Bashir AK, Mumtaz S, Tariq U (2020) Trustworthy edge storage orchestration in intelligent transportation systems using reinforcement learning. IEEE Transactions on Intelligent Transportation Systems
Raja G, Anbalagan S, Vijayaraghavan G, Dhanasekaran P, Al-Otaibi YD, Bashir AK (2020) Energy-efficient end-to-end security for software defined vehicular networks. IEEE Transactions on Industrial Informatics
Shi S, Xiong Y, Chen J, Xiong C (2019) A bilevel optimal motion planning (BOMP) model with application to autonomous parking. Int J Intell Robot Appl 3(4):370–382
Srinivas J, Das AK, Wazid M, Kumar N (2018) Anonymous lightweight chaotic map-based authenticated key agreement protocol for industrial Internet of Things. IEEE Trans Dependable Secure Comput 17(6):1133–1146
Thompson F, Galeazzi R (2020) Robust mission planning for Autonomous Marine Vehicle fleets. Robot Auton Syst 124:103404
Yu B, Xie N, Zheng B, Chen D (2019) Methodology and decentralised control of modularised changeable conveyor logistics system. Int J Comput Integr Manuf 32(8):739–749
Zambrano-Martinez JL, Calafate CT, Soler D, Lemus-Zúñiga LG, Cano JC, Manzoni P, Gayraud T (2019) A centralized route-management solution for autonomous vehicles in urban areas. Electronics 8(7):722
Zhou D, Ma Z, Sun J (2019) Autonomous vehicles’ turning motion planning for conflict areas at mixed-flow intersections. IEEE Trans Intell Veh 5(2):204–216
Acknowledgements
This work was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (DF-198-980-1441). The authors, therefore, gratefully acknowledge DSR technical and financial support.
Author information
Authors and Affiliations
Department of Port and Maritime Transportation, Faculty of Maritime Studies, King Abdulaziz University, Jeddah, 21588, Saudi Arabia
Shougi Suliman Abosuliman
Department of Information Systems, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
Alaa Omran Almagrabi
- Shougi Suliman Abosuliman
You can also search for this author inPubMed Google Scholar
- Alaa Omran Almagrabi
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toAlaa Omran Almagrabi.
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Communicated by Vicente Garcia Diaz.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abosuliman, S.S., Almagrabi, A.O. Routing and scheduling of intelligent autonomous vehicles in industrial logistics systems.Soft Comput25, 11975–11988 (2021). https://doi.org/10.1007/s00500-021-05633-4
Accepted:
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative