1 Introduction

Managing large complex projects at the execution stage presents various challenges that need to be addressed, including increased project scope, resource problems, and communication issues (Merrow, 2024). For instance, Smith, Li and Rafferty (2020) stated that to effectively manage projects in such environments.

Project managers need to utilise project management tools, processes, and approaches to monitor and control project activities to ensure they are completed on time, within budget, and meet quality standards. Further, executive project management practices are crucial in addressing these challenges while executing large projects (Abualdenien et al., 2020). Therefore, this report examines how these practices can be applied to handle the difficulties of managing complex projects effectively.

2 Understanding Large Complex Projects

2.1 Definition and Characteristics

Large complex projects involve the utilisation of many resources and the participation of many stakeholders, and typically large projects cross-cut definite disciplines (Royce, 2021). For instance, Lu et al. (2024) stated that they usually run for several years, and the management of each is complex to make sure they are completed on time.

Furthermore, some of the large complex projects are building projects like cross rail in London, city developments such as Masdar city in Abu Dhabi as well as technological projects such as national healthcare project implementation (Koh and Askell‐Williams, 2021).

Moreover, complexity is one commonly observed feature of such projects due to the incorporation of various systems and technologies, multi-disciplinary teams and compliance with industry standards. Therefore, Nye et al. (2021) described that complex mega projects involve huge capital expenditure with many projects costing billions of dollars and they need to deliver both performance efficiencies and sustainability goals. 

2.2 Challenges in the Execution Stage

The problems that are encountered during the execution stage of large complex projects can have negative impacts not if they can be well handled. For instance, Al-Saqqa, Sawalha and AbdelNabi (2020) stated that the inability to maintain scope control due to changes that may be a result of the needs of the stakeholders or some technical issues compounds the problems of project duration by increasing costs.

Further, Skivington et al. (2021) described that resource organisations involving the distribution of labor, consumables, and tools must make prior arrangements to avoid complications that may cause delays to the project. In addition, staff management and workload should always be a focus to avoid seeing skilled personnel being overwhelmed and demotivated.

Moreover, Pan and Zhang (2021) mentioned that the stakeholders may also prove rather tricky to engage due to rivalry among government agencies, contractors, suppliers, and the general population. Therefore, Sjödin et al. (2020) a distinction has to be made to understand how goals fit with their objectives and expectations to avoid confusion.

Besides, risk management is critical due to the risks including technical ones like system crashes, supply chain risks, and regulatory changes customary in big projects. Similarly, McGilvray (2021) reported that risk management is all about having a plan and various tools to be able to methodically recognise, evaluate, and manage risks.

Thus, this means that to overcome these challenges the appropriate project management tools, methodologies and frameworks must be applied to achieve project success in terms of time, cost and quality. 

3 Project Management Tools and Processes

3.1 Overview of Project Management Tools

The roles of Project management tools are crucial in defining and measuring the different components of large systems projects (Harold, 2021). For instance, Shen et al. (2024) stated that it is used for scheduling and displaying the timeline of a project so that managers can assess the performance and any signs of a problem.

Further, Gupta and Kembhavi (2023) mentioned that the Critical Path Method (CPM) is a project management tool used to determine the sequence of project activities and recognise the essential tasks to finish the project, which can assist managers in planning resources and assigning critical activities.

Similarly, Yuliansyah and Ayu (2021) reported that another critically effective technique is Earned Value Management (EVM) which may be used as an effective tool that captures scope, time, and cost data at the same time. Therefore, together these tools help in increasing the awareness levels of the project to the stakeholders, keeping track of time and managing the resources effectively. 

3.2 Application of Tools in Complex Projects

Different tools are crucial when it comes to organisation and handling large and complicated projects (Pan and Zhang, 2021). Therefore, there can be no project plan without Gantt charts being an essential tool in planning a project and controlling the timeline and progress.

Further, Hong et al. (2023) stated that the Critical Path Method (CPM) in the construction sense is useful in establishing the longest task sequences in projects to guarantee the right completion of critical activities. Moreover, Perera et al. (2020) described that Earned Value Management (EVM) is especially useful for tracking government and defense projects to identify the plan and actual conditions to fix the issues.

For instance, EVM is applied in aerospace industries to enhance the proper management of systems development schedules and costs (Al Baroudi et al., 2021). Additionally, the Heathrow Terminal 5 project can also be used to demonstrate how advanced scheduling tools as well as risk management strategies can be used to address challenges and deliver the project (Gomes, Queiroz and Ferreira, 2020).

Thus, this demonstrates the great importance of using project management tools in big projects. 

3.3 Making Informed Decisions

The decision-making step is a vital component of project management as it impacts the overall success of the undertaking (Hunhevicz and Hall, 2020). For instance, it means that correct data as well as accurate assumptions are crucial in making sound decisions on the side of managers.

Further, Singh et al. (2023) stated that EVM and other project management tools provide a quantitative evaluation of the project performance and allow managers to allocate resources, control the risks, and plan more detailed schedules. Therefore, these are stochastic simulations of the risks and returns involving projects and enable managers to play actual ‘games’ to be able to determine the risks accruing from each move they make.

In addition, Akhtar, Bakhtawar and Akhtar (2022) mentioned that the Sydney Opera House project demonstrates this issue where the initial plans neglected project uncertainty leading to delays and cost explosions. Thus, this illustrates how project management practices, particularly in making sound decisions, supported the effort to return the project to the right track (Baduge et al., 2022).

Lastly, the use of project management tools is vital in addressing challenges, decision making and the overall success of a project. 

4 Project Management Methodologies

4.1 Overview of Methodologies

General management frameworks that are suitable for projects include Waterfall, Agile, PRINCE2, and Lean strategies for planning, implementation, and phase endings (Karalis, 2020). For instance, Popkin and Ng (2022) stated that Waterfall is a sequential model where one phase cannot commence until the preceding phase is complete, appropriate for projects with concrete specifications.

While, the term Agile is quite the opposite as it is repeated and fluid, which is perfect for projects that are still in the process of development (Raji et al., 2020). Besides, the elements of control in PRINCE2 include defined roles, product-based planning, and the continual review of plans and progress (Wang et al., 2023).

Moreover, Liang et al. (2023) mentioned that lean is a process improvement strategy primarily targeting the reduction of waste, rendering the most value to the customer through a streamlined approach. Hence, both methodologies have their advantages depending on the project type and the desired outcome.

4.2 Evaluating Methodologies for Large Projects

It is imperative to choose the appropriate methodology as a way of addressing challenges that are often encountered in large complex projects (Imperial, 2021). For instance, Benitez, Ayala and Frank (2020) reported that the Waterfall is most effective in settings where phases and products are well-defined as in infrastructure projects but has difficulty where change is common.

Therefore, it is effective if a lot of change is required in between the project phases, receiving frequent feedback or working on IT projects but large-scale organisations may have issues. Further, PRINCE2 enhances detailed planning and managing risks and quality within big projects; however, its highly formalised structure may hinder quick decision-making (Das, Luo and Cheng, 2020).

Moreover, this is ideal, especially in manufacturing where resources are limited because lean emphasises efficiency and reduction of waste, but for it to be successful it needs a cultural change. Therefore, there is no perfect methodology that can be followed for all large complex projects (Thota, Shajin and Rajesh, 2020).

Among the methodologies, there are Waterfall, Agile, PRINCE2 and Lean, all of which have their advantages and disadvantages. Hence, Austin et al. (2021) described that a blended method is a method that integrates aspects from the various approaches and offers a fair solution since they all have relative strengths for managing different projects. 

5 Project Management Frameworks

5.1 Introduction to Project Management Frameworks

Project management frameworks provide structures and procedures characteristic of the best practices, processes, and standards for the execution of projects (Kwon et al., 2023). For instance, some of the common frameworks that are used today are PMBOK and ISO 21500.

Further, Garg (2023) stated that PMBOK is a guide created by PMI providing a project management framework and organisation in five process groups and ten knowledge areas. Moreover, ISO 21500 is a worldwide guideline that aims at increasing the connection between projects and organisational strategy and to standardise projects all over the world (Xi et al., 2023).

Therefore, they both seek to promote sound and efficient delivery of projects across various industries and while PMBOK is reputed for its comprehensive and versatile nature (Deng et al., 2021). Thus, ISO 21500 builds toward a more comprehensive guideline that can be applied to projects of any size or scale of difficulty. 

5.2 Framework Application in Complex Projects

There are project management frameworks especially PMBOK and ISO 21500 that are so useful when it comes to overseeing large and complex projects (Brancalion and Holl, 2020). Therefore, these frameworks provide proactive guidelines for implementing projects to deliver all vital components of the project.

For instance, Biggs et al. (2021) stated that they make it easier to communicate and plan with the individuals who are part of the team, other interested persons and the management as everyone agrees with the goals of the project. Further, Zhu et al. (2020) mentioned that project managers should therefore follow tried and tested approaches that are in the industry and this will reduce the chances of making mistakes greatly.

Moreover, there have been successful implementations of PMBOK for infrastructure projects with tangible positive impacts on planning and risk management of the projects and stakeholder management (Maroufkhani et al., 2020). Likewise, ISO 21500 has helped maintain a standard approach to project management, especially in multinational projects and has easily integrated systems and processes. 

Despite the numerous benefits that may be derived from implementing project management frameworks, there are challenges associated with implementing the frameworks. Further, Chen et al. (2023) stated that education is crucial to ensure the correct application of the chosen framework, and a specific framework is not always scalable and optimal for highly dynamic working conditions.

Despite these challenges, the advantages of ensuring consistency, improved communication, and risk management are critical when it comes to large project implementation hence the importance of the frameworks in managing such projects (Hou et al., 2023). Therefore, it can be as a conclusion said that PMBOK and ISO 21500 are effective frameworks when it comes to the management of huge projects.

Moreover, Sánchez and Hartlieb (2020) reported that they enhance project performances and consistency to optimal methods, although integration requires the acquisition of knowledge to progress at an optimal level. From these frameworks, case studies criticism confirms the use of these frameworks and makes them relevant in different industries, adopting that is essential to apply them correctly for a specific project. 

6 Synthesis of Tools, Methodologies, and Frameworks

6.1 Integrated Approach

The use of software and documentation and other tools and best practices is the need of the hour in managing large cross-functional projects and programs (Osaba et al., 2021). Further, each component plays a distinct role: tools assist in the accomplishment of tasks, methodologies offer structure and organised methods to undertake projects and frameworks guarantee compliance with best practices.

Therefore, Hillson and Simon (2020) stated these individual facets when harmonised synergistically can help a project manager deal with issues effectively. For instance, combining Agile or Waterfall frameworks with instruments such as Gantt charts and CPM assists with timely and thorough planning (Surís, Menon and Vondrick, 2023).

Moreover, there are sources like PMBOK or ISO 21500 to avoid dispersions of standards and to follow proper guidelines. Hence, Zheng, Lu and Kiritsis (2022) concluded this approach allows for more effective decisions as it offers the project manager a flexible approach based on response to methodologies and tools. Therefore, these linkages improve risk management, optimise resources, and foster good relationships with people of interest. 

6.2 Case Studies and Examples 

Linear case comparisons indicate the need to use tools, methodologies, and frameworks at various stages of large projects. For instance, Sjödin et al. (2021) stated that the London Olympics 2012 and the construction of the Olympic Park is a clear demonstration of such success stories. Further, this project was executed in a way that integrated PRINCE2 with methods such as Earned Value Management (EVM) as well as the OGC framework.

Moreover, Miller et al. (2021) mentioned that PRINCE2 offered a framework for managing the project, EVM gave timely information about the project, and the OGC framework enshrined standard practices. Therefore, the project was delivered on schedule and to the set budget effectively demonstrating the virtues of flexibility, adequate monitoring tools, and, framework. Another is the construction of Burj Khalifa in Dubai, which also achieved its targets during construction (Teng et al., 2021). Hence, this project incorporated technical Agile tools such as the CPM and the Gantt chart in harmony with the PMBOK framework. 

Agile helped to make constant improvements, CPM and Gantt charts helped with further scheduling, and PMBOK helped to navigate through the best practices (Shinn et al., 2024). As with any project, there were some challenges but the completion of Burj Khalifa has reinforced the need to employ an integrated approach.

Similarly, Char, Abràmoff and Feudtner (2020) described that the changes in methodologies, the accurate monitoring, and the strict compliance with a check and balances made a remarkable success in civil engineering. Therefore, the components of project management should be integrated to enable proper control of large-scale projects. Further, Xia et al. (2021) reported that the London Olympic Park and Burj Khalifa projects present good examples and advantages of an integrated collaborative approach to construction projects.

Likewise, many goals include decision-making improvement, risk management, and engagement with key stakeholders (Amid et al., 2020). Therefore, these case studies provide paradigms for subsequent projects, stressing that effective project delivery is possible only through an integrated approach. 

7 Conclusion

In undertaking Large Complex Projects, there is a need to adopt tools, methodologies and frameworks to tackle the arising challenges. Further, Prime Gantt charts, EVM, methodologies like agile and PRINCE 2 and frameworks such as PMBOK, and ISO 21500 offer enriched execution strategies.

Moreover, real-life examples show that system integration is effective and should include flexibility, effective monitoring, and compliance with the industry standards. Therefore, it is essential to apply continuous improvement and adjustment for the project management practices given the shift in the project’s requirements. 

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8 Reference List

Abualdenien, J., Schneider-Marin, P., Zahedi, A., Harter, H., Exner, H., Steiner, D., Singh, M.M., Borrmann, A., Lang, W., Petzold, F. and König, M., 2020. Consistent management and evaluation of building models in the early design stages. Journal of Information Technology in Construction, 25, pp.212-232.

Akhtar, A., Bakhtawar, B. and Akhtar, S., 2022. Extreme programming vs scrum: A comparison of agile models. International Journal of Technology, Innovation and Management (IJTIM), 2(2), pp.80-96.

Al Baroudi, H., Awoyomi, A., Patchigolla, K., Jonnalagadda, K. and Anthony, E.J., 2021. A review of large-scale CO2 shipping and marine emissions management for carbon capture, utilisation and storage. Applied Energy, 287, p.116510.(EGDM01 project management)

Al-Saqqa, S., Sawalha, S. and AbdelNabi, H., 2020. Agile software development: Methodologies and trends. International Journal of Interactive Mobile Technologies, 14(11).

Amid, A., Biancolin, D., Gonzalez, A., Grubb, D., Karandikar, S., Liew, H., Magyar, A., Mao, H., Ou, A., Pemberton, N. and Rigge, P., 2020. Chipyard: Integrated design, simulation, and implementation framework for custom socs. IEEE Micro, 40(4), pp.10-21.

Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q. and Sutton, C., 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732.

Baduge, S.K., Thilakarathna, S., Perera, J.S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A. and Mendis, P., 2022. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, p.104440.

Benitez, G.B., Ayala, N.F. and Frank, A.G., 2020. Industry 4.0 innovation ecosystems: An evolutionary perspective on value cocreation. International Journal of Production Economics, 228, p.107735.

Biggs, J., Myers, J., Kufel, J., Ozer, E., Craske, S., Sou, A., Ramsdale, C., Williamson, K., Price, R. and White, S., 2021. A natively flexible 32-bit Arm microprocessor. Nature, 595(7868), pp.532-536.

Brancalion, P.H. and Holl, K.D., 2020. Guidance for successful tree planting initiatives. Journal of Applied Ecology, 57(12), pp.2349-2361.

Char, D.S., Abràmoff, M.D. and Feudtner, C., 2020. Identifying ethical considerations for machine learning healthcare applications. The American Journal of Bioethics, 20(11), pp.7-17.

Chen, X., Lin, M., Schärli, N. and Zhou, D., 2023. Teaching large language models to self-debug. arXiv preprint arXiv:2304.05128.

Das, M., Luo, H. and Cheng, J.C., 2020. Securing interim payments in construction projects through a blockchain-based framework. Automation in construction, 118, p.103284.

Deng, W., Xu, J., Song, Y. and Zhao, H., 2021. Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem. Applied Soft Computing, 100, p.106724.

Garg, A., 2023. Monitoring tools for setting up the hospital project: initial planning, building and equipment. Springer Nature.

Gomes, V.C., Queiroz, G.R. and Ferreira, K.R., 2020. An overview of platforms for big earth observation data management and analysis. Remote Sensing, 12(8), p.1253.

Gupta, T. and Kembhavi, A., 2023. Visual programming: Compositional visual reasoning without training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14953-14962).

Harold, K., 2021. Project management: case studies. John Wiley & Sons, Inc..

Hillson, D. and Simon, P., 2020. Practical project risk management: The ATOM methodology. Berrett-Koehler Publishers.

Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S.K.S., Lin, Z., Zhou, L. and Ran, C., 2023. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352.

Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J. and Wang, H., 2023. Large language models for software engineering: A systematic literature review. arXiv preprint arXiv:2308.10620.

Hunhevicz, J.J. and Hall, D.M., 2020. Do you need a blockchain in construction? Use case categories and decision framework for DLT design options. Advanced Engineering Informatics, 45, p.101094.

Imperial, M.T., 2021. Implementation structures: The use of top-down and bottom-up approaches to policy implementation. In Oxford research encyclopedia of politics.

Karalis, T., 2020. Planning and evaluation during educational disruption: Lessons learned from Covid-19 pandemic for treatment of emergencies in education. European Journal of Education Studies.

Koh, G.A. and Askell‐Williams, H., 2021. Sustainable school‐improvement in complex adaptive systems: a scoping review. Review of Education, 9(1), pp.281-314.

Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C.H., Gonzalez, J., Zhang, H. and Stoica, I., 2023, October. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Principles (pp. 611-626).

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P. and Zeng, A., 2023, May. Code as policies: Language model programs for embodied control. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 9493-9500). IEEE.

Lu, P., Peng, B., Cheng, H., Galley, M., Chang, K.W., Wu, Y.N., Zhu, S.C. and Gao, J., 2024. Chameleon: Plug-and-play compositional reasoning with large language models. Advances in Neural Information Processing Systems, 36.

Maroufkhani, P., Tseng, M.L., Iranmanesh, M., Ismail, W.K.W. and Khalid, H., 2020. Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International journal of information management, 54, p.102190.

McGilvray, D., 2021. Executing data quality projects: Ten steps to quality data and trusted information (TM). Academic Press.

Merrow, E.W., 2024. Industrial megaprojects: concepts, strategies, and practices for success. John Wiley & Sons.(EGDM01 project management)

Miller, C.J., Barnett, M.L., Baumann, A.A., Gutner, C.A. and Wiltsey-Stirman, S., 2021. The FRAME-IS: a framework for documenting modifications to implementation strategies in healthcare. Implementation Science, 16, pp.1-12.

Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D. and Sutton, C., 2021. Show your work: Scratchpads for intermediate computation with language models. arXiv preprint arXiv:2112.00114.

Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A.J., Molina, D., LaTorre, A., Suganthan, P.N., Coello, C.A.C. and Herrera, F., 2021. A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, p.100888.

Pan, Y. and Zhang, L., 2021. A BIM-data mining integrated digital twin framework for advanced project management. Automation in Construction, 124, p.103564.(EGDM01 project management)

Pan, Y. and Zhang, L., 2021. Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, p.103517.

Perera, S., Nanayakkara, S., Rodrigo, M.N.N., Senaratne, S. and Weinand, R., 2020. Blockchain technology: Is it hype or real in the construction industry?. Journal of industrial information integration, 17, p.100125.

Popkin, B.M. and Ng, S.W., 2022. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra‐processed foods is not inevitable. Obesity Reviews, 23(1), p.e13366.

Raji, I.D., Smart, A., White, R.N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D. and Barnes, P., 2020, January. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 33-44).

Royce, W.W., 2021. Managing the development of large software systems (1970).(EGDM01 project management)

Sánchez, F. and Hartlieb, P., 2020. Innovation in the mining industry: Technological trends and a case study of the challenges of disruptive innovation. Mining, Metallurgy & Exploration, 37(5), pp.1385-1399.

Shen, Y., Song, K., Tan, X., Li, D., Lu, W. and Zhuang, Y., 2024. Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. Advances in Neural Information Processing Systems, 36.

Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. and Yao, S., 2024. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36.

Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J. and Garg, A., 2023, May. Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 11523-11530). IEEE.(EGDM01 project management)

Sjödin, D., Parida, V., Kohtamäki, M. and Wincent, J., 2020. An agile co-creation process for digital servitization: A micro-service innovation approach. Journal of Business Research, 112, pp.478-491.

Sjödin, D., Parida, V., Palmié, M. and Wincent, J., 2021. How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, pp.574-587.

Skivington, K., Matthews, L., Simpson, S.A., Craig, P., Baird, J., Blazeby, J.M., Boyd, K.A., Craig, N., French, D.P., McIntosh, E. and Petticrew, M., 2021. Framework for the development and evaluation of complex interventions: gap analysis, workshop and consultation-informed update. Health technology assessment (Winchester, England), 25(57), p.1.(EGDM01 project management)

Smith, J.D., Li, D.H. and Rafferty, M.R., 2020. The implementation research logic model: a method for planning, executing, reporting, and synthesizing implementation projects. Implementation Science, 15, pp.1-12.

Surís, D., Menon, S. and Vondrick, C., 2023. Vipergpt: Visual inference via python execution for reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 11888-11898).

Teng, S.Y., Touš, M., Leong, W.D., How, B.S., Lam, H.L. and Máša, V., 2021. Recent advances on industrial data-driven energy savings: Digital twins and infrastructures. Renewable and Sustainable Energy Reviews, 135, p.110208.

Thota, M.K., Shajin, F.H. and Rajesh, P., 2020. Survey on software defect prediction techniques. International Journal of Applied Science and Engineering, 17(4), pp.331-344.(EGDM01 project management)

Wang, Z., Cai, S., Chen, G., Liu, A., Ma, X. and Liang, Y., 2023. Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents. arXiv preprint arXiv:2302.01560.

Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E. and Zheng, R., 2023. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.

Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A. and Harik, R., 2021. A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence. Journal of Manufacturing Systems, 58, pp.210-230.(EGDM01 project management)

Yuliansyah, A. and Ayu, M., 2021. The implementation of project-based assignment in online learning during covid-19. Journal of English Language Teaching and Learning, 2(1), pp.32-38.

Zheng, X., Lu, J. and Kiritsis, D., 2022. The emergence of cognitive digital twin: vision, challenges and opportunities. International Journal of Production Research, 60(24), pp.7610-7632.

Zhu, D., Feng, X., Xu, X., Yang, Z., Li, W., Yan, S. and Ding, H., 2020. Robotic grinding of complex components: a step towards efficient and intelligent machining–challenges, solutions, and applications. Robotics and Computer-Integrated Manufacturing, 65, p.101908.(EGDM01 project management)