Operations management of smart logistics: A literature review and future research

Bo FENG, Qiwen YE

PDF(505 KB)
PDF(505 KB)
Front. Eng ›› 2021, Vol. 8 ›› Issue (3) : 344-355. DOI: 10.1007/s42524-021-0156-2
REVIEW ARTICLE
REVIEW ARTICLE

Operations management of smart logistics: A literature review and future research

Author information +
History +

Abstract

The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.

Graphical abstract

Keywords

smart logistics / operations management / optimization / Internet of Things

Cite this article

Download citation ▾
Bo FENG, Qiwen YE. Operations management of smart logistics: A literature review and future research. Front. Eng, 2021, 8(3): 344‒355 https://doi.org/10.1007/s42524-021-0156-2

References

[1]
Al-Turjman F, Hasan M Z, Al-Rizzo H (2018). Task scheduling in cloud-based survivability applications using swarm optimization in IoT. Transactions on Emerging Telecommunications Technologies, 30(8): e3539
[2]
Alam K M, El Saddik A (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 5: 2050–2062
CrossRef Google scholar
[3]
Anandhi S, Anitha R, Sureshkumar V (2019). IoT enabled RFID authentication and secure object tracking system for smart logistics. Wireless Personal Communications, 104(2): 543–560
CrossRef Google scholar
[4]
Anderluh A, Nolz P C, Hemmelmayr V C, Crainic T G (2021). Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and “grey zone” customers arising in urban logistics. European Journal of Operational Research, 289(3): 940–958
[5]
Andersson J, Jonsson P (2018). Big data in spare parts supply chains: The potential of using product-in-use data in aftermarket demand planning. International Journal of Physical Distribution & Logistics Management, 48(5): 524–544
CrossRef Google scholar
[6]
Barreto L, Amaral A, Pereira T (2017). Industry 4.0 implications in logistics: An overview. Procedia Manufacturing, 13: 1245–1252
CrossRef Google scholar
[7]
Blümel E (2013). Global challenges and innovative technologies geared toward new markets: Prospects for virtual and augmented reality. Procedia Computer Science, 25: 4–13
CrossRef Google scholar
[8]
Borstell H, Pathan S, Cao L, Richter K, Nykolaychuk M (2013). Vehicle positioning system based on passive planar image markers. In: International Conference on Indoor Positioning and Indoor Navigation. Montbeliard: IEEE, 1–9
[9]
Breivold H P, Sandström K (2015). Internet of Things for industrial automation—Challenges and technical solutions. In: International Conference on Data Science and Data Intensive Systems. Sydney: IEEE, 532–539
[10]
Caballero-Gil C, Molina-Gil J, Caballero-Gil P, Quesada-Arencibia A (2013). IoT application in the supply chain logistics. In: International Conference on Computer Aided Systems Theory. Berlin: Springer, 55–62
[11]
Chen Q Y, Lin Y H, Qiu R Z (2016). Optimization of the multi-object recognition algorithm based on RFID for woodwork logistics. Journal of Fujian Agriculture and Forestry University (Natural Science Edition), 45(4): 476–480 (in Chinese)
[12]
Chen X (2019). The development trend and practical innovation of smart cities under the integration of new technologies. Frontiers of Engineering Management, 6(4): 485–502
CrossRef Google scholar
[13]
Chen Y (2020). Novel smart logistics pipeline based on cloud scheduling and intelligent interactive data center. In: International Conference on Inventive Computation Technologies (ICICT). Coimbatore: IEEE, 467–470
[14]
Cho S, Kim J (2017). Smart logistics model on Internet of Things environment. Advanced Science Letters, 23(3): 1599–1602
CrossRef Google scholar
[15]
Chu Z, Feng B, Lai F (2018). Logistics service innovation by third party logistics providers in China: Aligning guanxi and organizational structure. Transportation Research Part E: Logistics and Transportation Review, 118: 291–307
CrossRef Google scholar
[16]
Dong C, Franklin R (2020). From the digital Internet to the physical Internet: A conceptual framework with a stylized network model. Journal of Business Logistics, in press, doi: 10.1111/jbl.12253
CrossRef Google scholar
[17]
Eitzen H, Lopez-Pires F, Baran B, Sandoya F, Chicaiza J L (2017). A multi-objective two-echelon vehicle routing problem. An urban goods movement approach for smart city logistics. In: XLIII Latin American Computing Conference. Córdoba: IEEE, 1–10
[18]
Feng B, Ye Q W, Collins B J (2019). A dynamic model of electric vehicle adoption: The role of social commerce in new transportation. Information & Management, 56(2): 196–212
CrossRef Google scholar
[19]
Fraile F, Tagawa T, Poler R, Ortiz A (2018). Trustworthy industrial IoT gateways for interoperability platforms and ecosystems. IEEE Internet of Things Journal, 5(6): 4506–4514
CrossRef Google scholar
[20]
Fu Y, Zhu J (2019). Operation mechanisms for intelligent logistics system: A blockchain perspective. IEEE Access, 7: 144202–144213
CrossRef Google scholar
[21]
Fukui T (2016). A systems approach to big data technology applied to supply chain. In: International Conference on Big Data. Washington DC: IEEE, 3732–3736
[22]
Gallay O, Hongler M O (2009). Circulation of autonomous agents in production and service networks. International Journal of Production Economics, 120(2): 378–388
CrossRef Google scholar
[23]
Gan M, Yang S, Li D, Wang M, Chen S, Xie R, Liu J (2018). A novel intensive distribution logistics network design and profit allocation problem considering sharing economy. Complexity, 4678358
CrossRef Google scholar
[24]
Gregor T, Krajčovič M, Więcek D (2017). Smart connected logistics. Procedia Engineering, 192: 265–270
CrossRef Google scholar
[25]
Hasan M Z, Al-Rizzo H (2020). Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization. Concurrency and Computation: Practice and Experience, 32(2): e5442
CrossRef Google scholar
[26]
He L (2017). The development trend of China’s smart logistics. China Business and Market, 31(6): 3–7 (in Chinese)
[27]
Hilpert H, Kranz J, Schumann M (2013). Leveraging green is in logistics. Business & Information Systems Engineering, 5(5): 315–325
CrossRef Google scholar
[28]
Hongler M O, Gallay O, Hülsmann M, Cordes P, Colmorn R (2010). Centralized versus decentralized control—A solvable stylized model in transportation. Physica A: Statal Mechanics & Its Applications, 389(19): 4162–4171
[29]
Hopkins J, Hawking P (2018). Big data analytics and IoT in logistics: A case study. International Journal of Logistics Management, 29(2): 575–591
CrossRef Google scholar
[30]
Hu W (2019). An improved flower pollination algorithm for optimization of intelligent logistics distribution center. Advances in Production Engineering & Management, 14(2): 177–188
CrossRef Google scholar
[31]
Huang S, Guo Y, Zha S, Wang Y (2019). An Internet-of-Things-based production logistics optimisation method for discrete manufacturing. International Journal of Computer Integrated Manufacturing, 32(1): 13–26
CrossRef Google scholar
[32]
Jabeur N, Al-Belushi T, Mbarki M, Gharrad H (2017). Toward leveraging smart logistics collaboration with a multi-agent system based solution. Procedia Computer Science, 109: 672–679
CrossRef Google scholar
[33]
Jagwani P, Kumar M (2018). IoT powered vehicle tracking system (VTS). In: International Conference on Computational Science and Its Applications. Melbourne: Springer, 488–498
[34]
Jiao Y B (2014). Based on the electronic commerce environment of intelligent logistics system construction. Advanced Materials Research, 850–851: 1057–1060
[35]
Katsuma R, Yoshida S (2018). Dynamic routing for emergency vehicle by collecting real-time road conditions. International Journal of Communications, Network & System Sciences, 11(2): 27–44
CrossRef Google scholar
[36]
Kim S H, Cohen M A, Netessine S (2017). Reliability or inventory? An analysis of performance-based contracts for product support services. In: Ha A, Tang C, eds. Handbook of Information Exchange in Supply Chain Management. Cham: Springer, 65–68
[37]
Kim T Y, Dekker R, Heij C (2018). Improving warehouse labour efficiency by intentional forecast bias. International Journal of Physical Distribution & Logistics Management, 48(1): 93–110
CrossRef Google scholar
[38]
Kirch M, Poenicke O, Richter K (2017). RFID in logistics and production—Applications, research and visions for smart logistics zones. Procedia Engineering, 178: 526–533
CrossRef Google scholar
[39]
Klumpp M (2018). Economic and social advances for geospatial data use in vehicle routing. In: International Conference on Dynamics in Logistics. Bremen: Springer, 368–377
[40]
Kong X T, Fang J, Luo H, Huang G Q (2015). Cloud-enabled real-time platform for adaptive planning and control in auction logistics center. Computers & Industrial Engineering, 84: 79–90
CrossRef Google scholar
[41]
Kovalský M, Mičieta B (2017). Support planning and optimization of intelligent logistics systems. Procedia Engineering, 192: 451–456
CrossRef Google scholar
[42]
Kwak K H, Bae N J, Cho Y Y (2014). Smart logistics service model based on context information. In: Park J, Zomaya A, Jeong H Y, Obaidat M, eds. Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol. 301. Dordrecht: Springer, 669–676
CrossRef Google scholar
[43]
Lee C K M, Lv Y, Ng K K H, Ho W, Choy K L (2018). Design and application of Internet of Things-based warehouse management system for smart logistics. International Journal of Production Research, 56(8): 2753–2768
CrossRef Google scholar
[44]
Lee S, Kang Y, Prabhu V V (2016). Smart logistics: Distributed control of green crowdsourced parcel services. International Journal of Production Research, 54(23): 6956–6968
CrossRef Google scholar
[45]
Lei L (2015). Research on the key technology of RFID and its application in modern logistics. In: AASRI International Conference on Industrial Electronics and Applications. Paris: Atlantis Press, 328–331
[46]
Levina A I, Dubgorn A S, Iliashenko O Y (2017). Internet of Things within the service architecture of intelligent transport systems. In: European Conference on Electrical Engineering and Computer Science (EECS). Bern: IEEE, 351–355
[47]
Li S, Sun Q, Wu W (2019a). Benefit distribution method of coastal port intelligent logistics supply chain under cloud computing. Journal of Coastal Research, 93(SI): 1041–1046
[48]
Li Y, Chu F, Feng C, Chu C, Zhou M (2019b). Integrated production inventory routing planning for intelligent food logistics systems. IEEE Transactions on Intelligent Transportation Systems, 20(3): 867–878
CrossRef Google scholar
[49]
Lin N, Shi Y, Zhang T, Wang X (2019). An effective order-aware hybrid genetic algorithm for capacitated vehicle routing problems in Internet of Things. IEEE Access, 7: 86102–86114
CrossRef Google scholar
[50]
Liu B W, Liu X F, Li J T (2014). Research on heterogeneous information integration for intelligent logistics information system based on Internet of Things. WIT Transactions on Information and Communication Technologies, 46: 1783–1789
[51]
Liu C, Feng Y, Lin D, Wu L, Guo M (2020). IoT based laundry services: An application of big data analytics, intelligent logistics management, and machine learning techniques. International Journal of Production Research, 58(17): 5113–5131
CrossRef Google scholar
[52]
Liu P, Yang L, Gao Z, Huang Y, Li S, Gao Y (2018). Energy-efficient train timetable optimization in the subway system with energy storage devices. IEEE Transactions on Intelligent Transportation Systems, 19(12): 3947–3963
CrossRef Google scholar
[53]
Liu T, Yue Q, Wu X (2015). Design and implementation of cloud-based port logistics public service platform. In: International Conference on Computer & Communications. Chengdu: IEEE, 234–239
[54]
Liu Y Q, Wang H (2016a). Optimization for logistics network based on the demand analysis of customer. In: Chinese Control and Decision Conference (CCDC). Yinchuan: IEEE, 4547–4552
[55]
Liu Y Q, Wang H (2016b). Optimization for service supply network based on the user’s delivery time under the background of big data. In: Chinese Control and Decision Conference (CCDC). Yinchuan: IEEE, 4564–4569
[56]
Lo C C, Hsieh W C, Huang L T (2004). The implementation of an intelligent logistics tracking system utilizing RFID. In: The 4th International Conference on Electronic Business. Beijing, 199–204
[57]
Luo H, Chen J, Huang G Q (2016a). IoT enabled production-logistic synchronization in make-to-order industry. In: Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management. Paris: Atlantis Press, 527–538
[58]
Luo H, Zhu M, Ye S, Hou H, Chen Y, Bulysheva L (2016b). An intelligent tracking system based on Internet of Things for the cold chain. Internet Research, 26(2): 435–445
CrossRef Google scholar
[59]
Ma X, Wang J, Bai Q, Wang S (2020). Optimization of a three-echelon cold chain considering freshness-keeping efforts under cap-and-trade regulation in Industry 4.0. International Journal of Production Economics, 220: 107457
CrossRef Google scholar
[60]
Moradi B (2020). The new optimization algorithm for the vehicle routing problem with time windows using multi-objective discrete learnable evolution model. Soft Computing, 24(9): 6741–6769
CrossRef Google scholar
[61]
Murguzur A, de Carlos X, Trujillo S, Sagardui G (2014). Context-aware staged configuration of process variants@runtime. In: International Conference on Advanced Information Systems Engineering. Thessaloniki: Springer, 241–255
[62]
Nguyen J, Wu Y, Zhang J, Yu W, Lu C (2019). Real-time data transport scheduling for edge/cloud-based Internet of Things. In: International Conference on Computing, Networking and Communications (ICNC). Honolulu, HI: IEEE, 642–646
[63]
Porter M E, Heppelmann J E (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11): 64–88
[64]
Rjoub G, Bentahar J, Wahab O A, Bataineh A (2019). Deep smart scheduling: A deep learning approach for automated big data scheduling over the cloud. In: 7th International Conference on Future Internet of Things and Cloud. Istanbul: IEEE, 189–196
[65]
Sarkar B, Guchhait R, Sarkar M, Cárdenas-Barrón L E (2019). How does an industry manage the optimum cash flow within a smart production system with the carbon footprint and carbon emission under logistics framework? International Journal of Production Economics, 213: 243–257
CrossRef Google scholar
[66]
Schluse M, Priggemeyer M, Atorf L, Rossmann J (2018). Experimentable digital twins—Streamlining simulation-based systems engineering for Industry 4.0. IEEE Transactions on Industrial Informatics, 14(4): 1722–1731
CrossRef Google scholar
[67]
Shen Z M, Feng B, Mao C, Ran L (2019). Optimization models for electric vehicle service operations: A literature review. Transportation Research Part B: Methodological, 128: 462–477
CrossRef Google scholar
[68]
Siror J K, Huanye S, Dong W (2011). RFID based model for an intelligent port. Computers in Industry, 62(8–9): 795–810
CrossRef Google scholar
[69]
Sivamani S, Kwak K, Cho Y (2014). A study on intelligent user-centric logistics service model using ontology. Journal of Applied Mathematics, 162838
CrossRef Google scholar
[70]
Su Y, Fan Q M (2020). The green vehicle routing problem from a smart logistics perspective. IEEE Access, 8: 839–846
CrossRef Google scholar
[71]
Sun R, Liu M, Zhao L (2019). Research on logistics distribution path optimization based on PSO and IoT. International Journal of Wavelets, Multiresolution and Information Processing, 17(6): 1950051
[72]
Tang H, Yang X, Xiong S (2013). Modified particle swarm algorithm for vehicle routing optimization of smart logistics. In: Proceedings of the 2nd International Conference on Measurement, Information and Control. Harbin: IEEE, 783–787
[73]
Tao F, Zhang H, Liu A, Nee A Y C (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4): 2405–2415
CrossRef Google scholar
[74]
Trab S, Bajic E, Zouinkhi A, Abdelkrim M N, Chekir H, Ltaief R H (2015). Product allocation planning with safety compatibility constraints in IoT-based warehouse. Procedia Computer Science, 73: 290–297
CrossRef Google scholar
[75]
Trab S, Bajic E, Zouinkhi A, Thomas A, Abdelkrim M N, Chekir H, Ltaief R H (2017). A communicating object’s approach for smart logistics and safety issues in warehouses. Concurrent Engineering, 25(1): 53–67
CrossRef Google scholar
[76]
Trappey A J C, Trappey C V, Fan C Y, Hsu A P T, Li X K, Lee I J Y (2017). IoT patent roadmap for smart logistic service provision in the context of Industry 4.0. Journal of the Chinese Institute of Engineers, 40(7): 593–602
CrossRef Google scholar
[77]
Tsang Y P, Choy K L, Wu C H, Ho G T S, Lam H Y, Koo P S (2017). An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment. International Journal of Engineering Business Management, 9: 1–13
CrossRef Google scholar
[78]
Tu M, Lim M K, Yang M F (2018). IoT-based production logistics and supply chain system—Part 2. IoT-based cyber-physical system: A framework and evaluation. Industrial Management & Data Systems, 118(1): 96–125
CrossRef Google scholar
[79]
Tuli S, Ilager S, Ramamohanarao K, Buyya R (2020). Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks. IEEE Transactions on Mobile Computing, 99: 1–15
CrossRef Google scholar
[80]
Verdouw C N, Robbemond R M, Verwaart T, Wolfert J, Beulens A J (2018). A reference architecture for IoT-based logistic information systems in agri-food supply chains. Enterprise Information Systems, 12(7): 755–779
CrossRef Google scholar
[81]
Wang C L, Li S W (2018). Hybrid fruit fly optimization algorithm for solving multi-compartment vehicle routing problem in intelligent logistics. Advances in Production Engineering & Management, 13(4): 466–478
CrossRef Google scholar
[82]
Wang D, Zhu J, Wei X, Cheng T C E, Yin Y, Wang Y (2019). Integrated production and multiple trips vehicle routing with time windows and uncertain travel times. Computers & Operations Research, 103: 1–12
CrossRef Google scholar
[83]
Wang J, Lim M K, Zhan Y, Wang X (2020). An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transportation Research Part E: Logistics and Transportation Review, 135: 101886
CrossRef Google scholar
[84]
Wang K, Liang Y, Zhao L (2017a). Multi-stage emergency medicine logistics system optimization based on survival probability. Frontiers of Engineering Management, 4(2): 221–228
CrossRef Google scholar
[85]
Wang Y, Bai X, Ou H (2017b). Design and development of intelligent logistics system based on semantic web and data mining technology. In: International Conference on Computer Network, Electronic and Automation (ICCNEA). Xi’an: IEEE, 231–235
[86]
Weyer S, Meyer T, Ohmer M, Gorecky D, Zühlke D (2016). Future modeling and simulation of CPS-based factories: An example from the automotive industry. IFAC-PapersOnLine, 49(31): 97–102
CrossRef Google scholar
[87]
Xu W, Guo S, Li X, Guo C, Wu R, Peng Z (2019). A dynamic scheduling method for logistics tasks oriented to intelligent manufacturing workshop. Mathematical Problems in Engineering, 7237459
CrossRef Google scholar
[88]
Yang S, Wang J, Shi L, Tan Y, Qiao F (2018). Engineering management for high-end equipment intelligent manufacturing. Frontiers of Engineering Management, 5(4): 420–450
CrossRef Google scholar
[89]
Yao K, Yang B, Zhu X L (2019). Low-carbon vehicle routing problem based on real-time traffic conditions. Computer Engineering and Applications, 55(3): 231–237 (in Chinese)
[90]
Zhang G (2015). Large data and intelligent logistics. Journal of Transportation Systems Engineering and Information Technology, 15(1): 2–10, 233 (in Chinese)
[91]
Zhang H, Zhang Q, Ma L, Zhang Z, Liu Y (2019a). A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows. Information Sciences, 490: 166–190
CrossRef Google scholar
[92]
Zhang J, Liu Y, Zhao Y, Deng T (2020). Emergency evacuation problem for a multi-source and multi-destination transportation network: Mathematical model and case study. Annals of Operations Research, 291(1–2): 1153–1181
CrossRef Google scholar
[93]
Zhang L (2016). Application of IoT in the supply chain of the fresh agricultural products. In: International Conference on Communications, Information Management and Network Security. Shanghai: Atlantis Press, 201–204
[94]
Zhang M, Fu Y, Zhao Z, Pratap S, Huang G Q (2019b). Game theoretic analysis of horizontal carrier coordination with revenue sharing in E-commerce logistics. International Journal of Production Research, 57(5): 1524–1551
CrossRef Google scholar
[95]
Zhu D (2018). IoT and big data based cooperative logistical delivery scheduling method and cloud robot system. Future Generation Computer Systems, 86: 709–715
CrossRef Google scholar

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2021 The Author(s) 2021. This article is published with open access at link.springer.com and journal.hep.com.cn
AI Summary AI Mindmap
PDF(505 KB)

Accesses

Citations

Detail

Sections
Recommended

/