Advanced Project Planning and Visualisation Assignment

Part 1

Communication plan

The communication plan plays a major role n the project management and to attain the project management n efficient for those who are working remote area, there re some considerations needs to be considered and they may include:

  • Promoting frequent communication

The project managers those who are working with the remote teams needs to stroll over to office or desk for discussing the task status in order to promote the fluid communication.

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Whether it has been instant messaging or conference calls, there are various ways for the project managers to get in touch with the team. Thus spending some time with the teams can be able to make the necessary feedback to make the certain changes as well as improvements to increase the motivation of the team as well as communication updates.

  • Maintenance of project visibility

It is easier to get disorganized while working with the remote teams and thus the team physical location get varies the project still requires to be visible to very individual throughout the economy of the project.

In order to ensure the project visibility, use the PM tools to create central as well as fully accessible location for the project so that it can be visible to the whole team and can be accessible by every individual. The sharing of the spaces along with the project updates can improvise the visibility of the project. The communication, documents and information needs to be shared online across the various location as well as teams.

  • Focusing on ground work first

Before engaging straight into the project, it is essential to ensure that the whole team understands the issues and trying to attain it. The whole team has to understand the project goals as well as objective of project even if they get engaged in small part of the project. It is necessary to get a preliminary meeting by means of conference calls or in person for the establishment of the timeline, vision, objectives and expectation of the project. The project can be broken down with the every individual of the team and better understanding their role.

Managing multi-cultural project stakeholders

It has been one of the tedious process as the project team members involved is from multi culture and thus bring collaboration is difficult. When collaborating in a multicultural team, there may be some challenges to overcome due to cultural differences. A multicultural team is made up of people from different countries and cultures. Naturally, these individuals speak a variety of languages.

Some of the ways that has been listed below to defeat the challenges and they may include:

  • Overcoming cultural and language barriers

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Handling language difficulties amongst employees is one of the most typical challenges while working in a multicultural team. If each member of the team speaks a various languages, you’ll need to come up with a common language that everyone can understand.

  • Considering various Cultural Communication Styles

Every culture has its unique communication style, which includes things like speech patterns and nonverbal cues. Gestures, facial expressions, and body language are all examples of nonverbal communication. It’s critical to be aware of different communication styles across cultures and to speak to your co-workers in accordance with these guidelines.

  • Organizing cross-cultural training

Organizing cross-cultural training can helps to boost workplace pleasure and morale. The goal of this training is to help employees deal with workplace cultural issues. People will get to know one another and learn about other cultural views this way.

Cost and time overruns Management

The project’s difficulties cause delays and expense overruns. Stakeholders were putting a lot of pressure on the project manager because of the time and expense overruns.

The team leader is more likely to follow the project manager’s directions and complete the project on schedule by sticking to the project plan. The fulfilment of tasks and any project concerns must be reported to the project manager on a daily basis.

The project manager manages the team and assigns responsibilities to ensure that the project is completed on time. In accordance with the recommendations, the performance report will be submitted to the project sponsor. Team members effectively controlled project activity control and timeline planning, and the final report could be submitted to the project manager.

Part 2

Multi-constraints planning

The discussion on the significance on the multi constraints planning in various project were reviewed.

The given article made the discussion on how the Multi-Constraint Optimized Planning plays a major role in Virtualized-Service Pool for Mission-Oriented Swarm Intelligent Systems were discussed. The algorithm such as Multi-Constraint Optimized Planning used for the task planning process for the earlier completion and discussion on the project completion can be made with the appropriate members on the basis of the task status. The key advantage of the proposed multi-constraint-based task planning method is that it is ideal for solving such complex optimization problems and takes benefit of GA by expanding operators.

The inadequacies of present applications with in construction and facility administration of MEP projects are addressed in this research, which proposes a multi-scale solution. The paper presents a BIM-based construction management system that provides sophisticated simulation scenes with spatial and temporal scales for various participants to connect and cooperate, as well as a BIM-based facility management system that shares information delivered from previous phases and improves the efficiency and security of MEP management during the operation and maintenance period, all based on this model.

The multi-constraints technique has been implemented in MS Project as just an embedded macro. Several experiments were carried out using a basic project, and it was discovered that GA can generate near-optimal and constraint-free schedules in a reasonable time. This will be crucial for improving construction site efficiency and reliability. For effective coordination across the distribution chain and numerous trades at the building work site, reliable construction timetables are critical. Construction schedules can be made more reliable by addressing all potential restrictions before they are performed out on site.

For an inter optimization problem, a genetic algorithm (GA) was designed and used. The GA adjusts task priorities and construction techniques in response to several constraints such as activity dependency, restricted working space, and resource and information readiness in order to arrive at an optimal or near-optimal set of duration of the project, cost, and smoothing resources profiles.

The research made presents a multi-scale solution that addresses the shortcomings of current applications in construction and facility administration of MEP projects. Based on this model, the paper presents a BIM-based construction management system that provides sophisticated simulation scenes with spatial and temporal scales for various stakeholders to connect and collaborate, as well as a BIM-based facility management solution that shares information conveyed from earlier stages and enhances the effectiveness and security of MEP planning during the operation and maintenance period (Ceberio, 2021).

The selected method builds on this adapt different to calculate both temporal constraints and consumption of resources for each plan change. As a result, the collection of timing and resource variables can be used to express scheduling constraints such as precedences, exclusive disjunctions, and resource capacity limits. We show how this approach allows for problem-dependent specialisation and improves planning and scheduling efficiency using examples from the aeronautics and spatial domains. Finally, we demonstrate how the methodology enables local / global trade-offs while constructing solving methods by employing real-world issue experiments (Aharonov∗, 2019).

On the basis of the task status, an algorithm such as Multi-Constraint Optimized Planning can be utilized for the task planning process for earlier completion and discussion on the project completion with the necessary participants. The suggested multi-constraint-based task planning method has the advantage of being appropriate for handling such complex optimization issues while also taking full advantage of GA by extending operators. For an inter optimization problem, a genetic algorithm (GA) was designed and used. The GA adjusts task priorities and construction techniques in response to several constraints such as activity dependency, restricted working space, and resource and information readiness in order to arrive at an optimal or near-optimal set of duration of the project, cost, and smoothing resources profiles (Ceberio, 2021).

Construction schedules can be made more reliable by addressing all potential restrictions before they are carried out on site. Potential constraints include resource availability, execution space, processing logic, actual dependency of building material, client instructions, and others. Scheduling tools and methodologies are disjointed and geared to address a small number of construction limitations. To showcase the idea, a methodology known as multi-constraint scheduling is provided, which considers four key kinds of construction constraints: structural, contractual, financial, and communication constraints. The algorithm such as Multi-Constraint Optimized Planning used for the task planning process for the earlier completion and discussion on the project completion can be made with the appropriate members on the basis of the task status (Alfred, 2020).

References

Ceberio, M., Kosheleva, O., & Kreinovich, V. (2021). Constraint approach to multi-objective optimization. Studies in Systems, Decision and Control, 21-25. https://doi.org/10.1007/978-3-319-61753-4_3

Considering quality aspects for construction scheduling using constraint-based simulation. (2019). Managing IT in Construction/Managing Construction for Tomorrow, 553-562. https://doi.org/10.1201/9781482266665-76

Dawood, N., & Sriprasert, E. (2006). Construction scheduling using multi‐constraint and genetic algorithms approach. Construction Management and Economics, 24(1), 19-30. https://doi.org/10.1080/01446190500310486

Deb, K., & Agrawal, S. (2020). A niched-penalty approach for constraint handling in genetic algorithms. Artificial Neural Nets and Genetic Algorithms, 235-243. https://doi.org/10.1007/978-3-7091-6384-9_40

Gharbi, I., Gharsellaoui, H., & Bouamama, S. (2021). New hybrid genetic based approach for real-time scheduling of Reconfigurable embedded systems. Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 1140-1155. https://doi.org/10.4018/978-1-7998-8048-6.ch055

Hu, Z., Zhang, J., Yu, F., Tian, P., & Xiang, X. (2016). Construction and facility management of large MEP projects using a multi-scale building information model. Advances in Engineering Software, 100, 215-230. https://doi.org/10.1016/j.advengsoft.2016.07.006

Huber, P., & Guida, T. (2019). Genetic algorithms: A heuristic approach to multi-dimensional problems. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3451302

Jianjun, G., & Dongbing, G. (2015). A direct visual-inertial sensor fusion approach in multi-state constraint Kalman filter. 2015 34th Chinese Control Conference (CCC). https://doi.org/10.1109/chicc.2015.7260595

Karypis, G., & Kumar, V. (2020). Multilevel algorithms for multi-constraint graph partitioning. Proceedings of the IEEE/ACM SC98 Conference. https://doi.org/10.1109/sc.1998.10018

Peter. (2021). Multi-agent soft constraint aggregation – A sequential approach. Proceedings of the 3rd International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0003156602770282

Shi, Y., & Liu, Y. (2020). A fuzzy potential solution approach to multi-criteria and multi-constraint level linear programming problems. Multicriteria Analysis, 213-224. https://doi.org/10.1007/978-3-642-60667-0_21

Tawil, E., & Hagras, H. (2019). A novel multi-objective multi-constraint genetic algorithms approach for Co-ordinating embedded agents. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). https://doi.org/10.1109/icsmc.2004.1400868

Wang, X., Yalaoui, F., & Dugardin, F. (2019). Genetic algorithms hybridized with the self controlling dominance to solve a multi-objective resource constraint project scheduling problem. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). https://doi.org/10.1109/soli.2017.8120966

Xu, S., & Wang, J. (2021). An efficient batch scheduling model for hospital sterilization services using genetic algorithm. Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 928-946. https://doi.org/10.4018/978-1-7998-8048-6.ch047

Xu, S., & Wang, J. (2021). An efficient batch scheduling model for hospital sterilization services using genetic algorithm. Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms, 928-946. https://doi.org/10.4018/978-1-7998-8048-6.ch047

Yalaoui, F., & Dugardin, F. (2019). Genetic algorithms to solve a multi-objective resource constraint project scheduling problem. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). https://doi.org/10.1109/soli.2017.8120966

Shi, Y., & Liu, Y. (2020). A fuzzy potential solution approach to multi-criteria and multi-constraint level linear programming problems. Multicriteria Analysis, 213-224. https://doi.org/10.1007/978-3-642-60667-0_21

Shien, Y., & Liu, Y. (2020). A multi-criteria and multi-constraint level linear programming problems.  Analysis, 213-224. https://doi.org/10.1007/978-3-642-60667-0_21

Sherlock, Y., & Liu, Y. (2020). A fuzzy potential solution approach to linear programming problems. Multicriteria Analysis, 213-224. https://doi.org/10.1007/978-3-642-60667-0_21

Deb, K., & Agrawal, S. (2020). Genetic algorithms. Artificial Neural Nets and Genetic Algorithms, 235-243. https://doi.org/10.1007/978-3-7091-6384-9_40

 

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