logistics management case study pdf

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

• Review Article
• Open access
• Published: 15 April 2021
• Volume 8 , pages 344–355, ( 2021 )

Cite this article

You have full access to this open access article

• Bo Feng 1 &
• Qiwen Ye 2  

10k Accesses

51 Citations

Explore all metrics

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.

Article PDF

Download to read the full article text

Similar content being viewed by others

Toward Smart Logistics: Engineering Insights and Emerging Trends

A Literature Review on Smart Technologies and Logistics

Application of Information Platform in Smart Logistics System

Avoid common mistakes on your manuscript.

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

Google Scholar  

Alam K M, El Saddik A (2017). C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access, 1: 2050–2062

Article   Google Scholar  

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

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

Article   MathSciNet   MATH   Google Scholar  

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

Barreto L, Amaral A, Pereira T (2017). Industry 4.0 implications in logistics: An overview. Procedia Manufacturing, 1: 1245–1252

Blümel E (2013). Global challenges and innovative technologies geared toward new markets: Prospects for virtual and augmented reality. Procedia Computer Science, 1: 4–13

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

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

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

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)

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

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

Chapter   Google Scholar  

Cho S, Kim J (2017). Smart logistics model on Internet of Things environment. Advanced Science Letters, 23(3): 1599–1602

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, 1: 291–307

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: https://doi.org/10.1111/jbl.12253

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

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

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

Fu Y, Zhu J (2019). Operation mechanisms for intelligent logistics system: A blockchain perspective. IEEE Access, 1: 144202–144213

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

Gallay O, Hongler M O (2009). Circulation of autonomous agents in production and service networks. International Journal of Production Economics, 120(2): 378–388

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

Gregor T, Krajčovič M, Więcek D (2017). Smart connected logistics. Procedia Engineering, 1: 265–270

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

He L (2017). The development trend of China’s smart logistics. China Business and Market, 31(6): 3–7 (in Chinese)

Hilpert H, Kranz J, Schumann M (2013). Leveraging green is in logistics. Business & Information Systems Engineering, 5(5): 315–325

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

Hopkins J, Hawking P (2018). Big data analytics and IoT in logistics: A case study. International Journal of Logistics Management, 29(2): 575–591

Hu W (2019). An improved flower pollination algorithm for optimization of intelligent logistics distribution center. Advances in Production Engineering & Management, 14(2): 177–188

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

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, 1: 672–679

Jagwani P, Kumar M (2018). IoT powered vehicle tracking system (VTS). In: International Conference on Computational Science and Its Applications. Melbourne: Springer, 488–498

Jiao Y B (2014). Based on the electronic commerce environment of intelligent logistics system construction. Advanced Materials Research, 850–1: 1057–1060

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

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

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

Kirch M, Poenicke O, Richter K (2017). RFID in logistics and production—Applications, research and visions for smart logistics zones. Procedia Engineering, 1: 526–533

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

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, 1: 79–90

Kovalský M, Mičieta B (2017). Support planning and optimization of intelligent logistics systems. Procedia Engineering, 1: 451–456

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

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

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

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

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

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

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

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, 1: 86102–86114

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, 1: 1783–1789

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

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

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

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

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

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

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

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

Ma X, Wang J, Bai Q, Wang S (2020). Optimization of a three-echelon cold chain considering freshness-keeping efforts undercap-and-trade regulation in Industry 4.0. International Journal of Production Economics, 220: 107457

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

Article   MathSciNet   Google Scholar  

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

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

Porter M E, Heppelmann J E (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11): 64–88

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

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, 1: 243–257

Schluse M, Priggemeyer M, Atorf L, Rossmann J (2018). Experimen-table digital twins—Streamlining simulation-based systems engineering for Industry 4.0. IEEE Transactions on Industrial Informatics, 14(4): 1722–1731

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, 1: 462–477

Siror J K, Huanye S, Dong W (2011). RFID based model for an intelligent port. Computers in Industry, 62(8–9): 795–810

Sivamani S, Kwak K, Cho Y (2014). A study on intelligent user-centric logistics service model using ontology. Journal of Applied Mathematics, 162838

Su Y, Fan Q M (2020). The green vehicle routing problem from a smart logistics perspective. IEEE Access, 1: 839–846

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

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

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

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, 1: 290–297

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

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

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, 1: 1–13

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

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, 1: 1–15

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

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

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, 1: 1–12

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

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

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

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

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

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

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)

Zhang G (2015). Large data and intelligent logistics. Journal of Transportation Systems Engineering and Information Technology, 15(1): 2–10, 233 (in Chinese)

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, 1: 166–190

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

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

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

Zhu D (2018). IoT and big data based cooperative logistical delivery scheduling method and cloud robot system. Future Generation Computer Systems, 1: 709–715

Download references

Author information

Authors and affiliations.

School of Business and Research Center for Smarter Supply Chain, Soochow University, Suzhou, 215021, China

School of Economics & Management, South China Normal University, Guangzhou, 510006, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Qiwen Ye .

Additional information

This study is supported by the National Social Science Funds for Major Projects (Grant No. 18ZDA059) and Philosophy and Social Sciences of the Guangdong Province Planning Project (Grant No. GD20YGL03).

Rights and permissions

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/ .

Reprints and permissions

About this article

Feng, B., Ye, Q. Operations management of smart logistics: A literature review and future research. Front. Eng. Manag. 8 , 344–355 (2021). https://doi.org/10.1007/s42524-021-0156-2

Download citation

Received : 16 July 2020

Accepted : 29 January 2021

Published : 15 April 2021

Issue Date : September 2021

DOI : https://doi.org/10.1007/s42524-021-0156-2

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

• smart logistics
• operations management
• optimization
• Internet of Things
• Find a journal
• Publish with us
• Track your research

Library subject guides

Logistics and supply chain management.

• Key Resources
• Journals and Magazines
• Books and eBooks
• Company and Industry Information

Where to find case studies

• Additional information

Case studies generally describe real world practical examples from which others can gain insight for their own application.  They can provide both positive examples and well as raise issues and challenges for others to be aware of.  

To locate case studies in LibrarySearch , enter the search terms "case studies" or "case study*" and the keywords describing your topic. 

Case studies can be located within the RMIT databases and across various subject areas. 

Emerald Emerging Markets Case Studies (EEMCS) 

Cases are written by case writers working in, or closely with, developing economies. They offer local perspectives with global appeal. The collection receives regular content updates throughout the year and all cases benefit from a supportive peer-review process to ensure the highest quality content.

• Emerald Insight A powerful business research tool that provides critical information when it is most needed. Users have instant access to over 35,000 full text articles from Emerald's international management portfolio, complete with full text archives back to 1994. Subject coverage spans a spectrum of management disciplines including: strategy; leadership; library and information management; marketing and human resource management; plus a substantial number of engineering, applied science and technology titles

Datamonitor Case Study Series

Short (10-page) company case studies examining the reasoning behind the success of specific companies.

• Datamonitor Case Study series Short (10-page) company case studies examining the reasoning behind the success of specific companies.

MarketLine Case Studies 

Concise evaluations of the latest innovative company strategies to help you quickly and easily understand exactly what makes them successful.  From social media initiatives to unusual store formats and online retailing, Case Studies present examples of best practice across a wide variety of industries and geographies, bringing innovation into full focus.

• MarketLine Reports & Briefs: Business Insights reports, company profiles,SWOT analyses, industry profiles, Porters Five Forces Scorecards, country profiles. Databooks: Company Financial & Top Ten Industry Players Guide, Financial Deals: tracks 195,000+ deals of over $5 million in value each month globally, News & Opinions: live news feed, Market Data Analytics: data for 150+ consumer products in 55 countries. Country Statistics: data for 215 global countries

ProQuest Case Studies 

• ProQuest Covering over 90,000 authoritative sources ProQuest is one of the world's most comprehensive collections of digital information. Millions of complete articles are available online in various formats.

EbscoHost Case Studies

• Business Source Complete (EBSCO) Business Source Complete is the world's definitive scholarly business database, providing the ultimate collection of bibliographic and full text content. As part of the comprehensive coverage offered by this database, indexing and abstracts for the most important scholarly business journals back to 1886 are included. In addition to the searchable cited references, Business Source Complete contains detailed author profiles for the most-cited authors in the database.

Case studies

Case studies describe real world practical examples, including successes, issues and challenges.  To find case studies using LibrarySearch or databases, add "case study" to your search terms.

• The case study handbook a student's guide This e-book provides tips on how to read, analyse and write about cases.

Finding case studies

The Library's Finding Case Studies guide explains different types of case studies and how to find them.

Writing a case study

See the Learning Lab tutorial on Writing a Case Study to find out how to read, analyse and respond to a case study.

• << Previous: Websites
• Next: Statistics >>
• Last Updated: Feb 13, 2024 3:25 PM
• URL: https://rmit.libguides.com/logistics

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Logistics and transportation cases

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024