Hybrid Modelling for Successful Construction Projects - Free Paper

The construction industry is one economic sector where different modelling concepts can be combined for efficient and accurate results in creating practical frameworks for construction projects. Because of the dynamic factors that can influence the building industry's outcomes, it is found to be a valuable exercise to combine more than one modelling methodology in a hybrid simulation. (Alvanchi et al., 2011). It is argued that in large projects, there may be a need to involve different agents for diverse sections and that there should be a greater exploration into the utilization of an integrated hybrid simulation framework (Nasirzadeh et al., 2017).

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

You may find that one agent is more conversant with commercial leasing. Simultaneously, the forte of another lies mainly in the sale of single-family residential units, while still, another has expertise primarily based in the itinerant accommodation more suited to students and migrant workers. A case in point would be where a large construction project envisions three-bedroom self-contained units, a student hostel, and a commercial hub consisting of a mini supermarket, a barbershop, and a local neighbourhood bar. The unique capabilities required by each of these agents can be harnessed and brought together in a hybrid integrated agent-based modelling and system dynamics ABM-SD simulation. This dual approach mitigates the varying safety behaviours inherent in each of these agents in as far as project management is concerned.

In such a framework, one system's capabilities can be fine-tuned to compensate for the other model's shortcomings adequately. The multi-method-based methodology has been successfully used in projects where workers' behaviours on projects can be considered uncertain. Such an approach is especially suitable and common in multi-cultural or multi-national projects (Wu et al., 2019). Labour management must be perfected to minimize project deviations, be it in the expected timelines of completion or the expected standard of quality of a particular unit (Siriram, 2011).

There is a much more delicate world of health care, where the integration of modelling systems is considerably more desirable. Healthcare systems are complicated and enormous, fraught with politics and emotions (Mustafee et al., 2015). While in construction, the outcomes can sometimes be determined and analyzed in brick-and-mortar terms, it is not so quickly done where matters of health are concerned. Empirical results for a vaccine will involve drawn-out experimentation and laboratory work before any conclusive results can be documented and ascertained as fit for public consumption.

Even when a medical study can be ready for peer-to-peer review, there are many hurdles to overcome. There is, of course, the dynamic aspect of human life that hangs in the balance. The world view of an oncologist, for example, could be totally at variance with that of a paediatrician when it comes to the testing required to determining the efficacy of a vaccine regime or alternate course of treatment for cancer in children. The cancer specialist's hardened world will likely view the possibility of side-effects of such treatment methods with a more realistic view than a doctor whose daily operations revolve around the care of little ones, whose fragile emotions and gentler handling of their wards is more to their modus operandi. This subjective representation of their outlook is based mainly on their interrelationships with patients (Daellenbach, n.d.).

In such a case, due to the divergent views that the stakeholders will have, methodology skewed towards soft systems methodology (SSM) is preferable (Powell & Mustafee, 2017). The reason for this is that there is generally a lengthier discourse on the process. It emphasizes learning and studying the situation with consideration of social, political, and human aspects. These will typically be structured and rigorous, but at the same time, non-mathematical (Mingers, 2011). The benefit of such an approach is that it tends to build consensus among professionals on the course of action to take. Furthermore, the repetition of such exercises leads to continuous improvements in such processes.

Using the above examples, you can determine that a hybrid of two methodologies is favourable when there are complex social problematic situations to be tackled. The soft systems methodology (SSM) and the dynamics of the system (SD) approaches can be integrated to produce a soft systems dynamics methodology (SSDM). This combined methodology transverses three significant aspects and comprises ten stages (Rodriguez-Ulloa & Paucar-Caceras, 2005). These defined aspects are (1) the RealWorld; (2) the Problem–Situation-Oriented System Thinking World; and (3) the Solving–Situation-Oriented System Thinking World. This system's advantage is it divides the problem, separating the 'what' and the 'how,' the problem and the solution.

The identifying ten stages can be looked at as a wholesome approach. Initially, the problem is looked at and defined. They systematically break down the problem and take the participants through a series of learning and application of lessons from a theoretical view to one that can be applied in the real world. By making appropriate cultural and system adjustments along the way, a holistic approach to problem-solving is achieved. Making a culturally feasible solution means that this approach can be applied successfully anywhere in the world.

A case in point, the SSDM was applied favorably in a Peruvian company, Tubos S.A., a company dealing in steel products. There was an issue of control in power by one director of the company over another, causing many operational problems. However, a concerted effort to improve communication and participation between them saw the change in the company's managerial behaviour and helped solve a problematic situation.

References

Alvanchi A, Lee S, AbouRizk S. (2011). Modeling framework and architecture of hybrid system dynamics and discrete event simulation for construction. Comput Aided Civ Inf, 26, 77–91.

Crawford, L., & Pollack, J. (2004). Hard and soft projects: a framework for analysis. International Journal of Project Management, 22(8), 645-653. HYPERLINK "https://doi.org/10.1016/j.ijproman.2004.04.004" https://doi.org/10.1016/j.ijproman.2004.04.004

Daellenbach HG (n.d.). Hard OR, soft OR, problem structuring methods, critical systems thinking: A primer. Retrieved September 2, 2020, from HYPERLINK "http://orsnz.org.nz/conf36/papers/Daellenbach.pdf" http://orsnz.org.nz/conf36/papers/Daellenbach.pdf.

Mingers, J. (2011). Soft OR comes of age—but not everywhere!. Omega, 39(6), 729-741. HYPERLINK "https://doi.org/10.1016/j.omega.2011.01.005" https://doi.org/10.1016/j.omega.2011.01.005

Mustafee, N., & Powell, J. (2018). From hybrid simulation to hybrid systems modelling. In Proceedings of the 2018 Winter Simulation Conference. Piscataway, NJ; IEEE.

Mustafee, N., Brailsford, S., Diallo, S., Padilla, J., Powell, J., & Tolk, A. (2015). Hybrid simulation studies and hybrid simulation systems: Definitions, challenges, and benefits. In Proceedings of the 2015 Winter Simulation Conference, 1678 - 1692. Piscataway, NJ; Institute of Electrical and Electronics Engineering, Inc.

Nasirzadeh, F., Khanzadi, M., & Mir, M. (2017). A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: an application to model construction workers' safety behavior. International Journal of Construction Management, 18(2), 132-143. HYPERLINK "https://doi.org/10.1080/15623599.2017.1285485" https://doi.org/10.1080/15623599.2017.1285485

Powell, J., & Mustafee, N. (2017). Widening requirements capture with soft methods: an investigation of hybrid M&S studies in health care. Journal of The Operational Research Society, 68(10), 1211-1222. HYPERLINK "https://doi.org/10.1057/s41274-016-0147-6" https://doi.org/10.1057/s41274-016-0147-6

Rodriguez-Ulloa, R., & Paucar-Caceres, A. (2005). Soft system dynamics methodology (SSDM): Combining soft systems methodology (SSM) and system dynamics (SD). Systemic Practice and Action Research, 18(3), 303-334. HYPERLINK "https://doi.org/10.1007/s11213-005-4816-7" https://doi.org/10.1007/s11213-005-4816-7

Siriram, R. (2017). A hybrid (soft and hard) systems approach to project management. International Journal of Industrial Engineering, 4(6), 1-16. HYPERLINK "https://doi.org/10.14445/23499362/ijie-v4i6p101" https://doi.org/10.14445/23499362/ijie-v4i6p101

Siriram, R. (2011). A soft and hard systems approach to business process management. Systems Research and Behavioral Science, 29(1), 87-100. HYPERLINK "https://doi.org/10.1002/sres.1095" https://doi.org/10.1002/sres.1095

Wu, C., Chen, C., Jiang, R., Wu, P., Xu, B., & Wang, J. (2019). Understanding laborers' behavioral diversities in multi-nationalmulti-national construction projects using integrated simulation approach. Engineering, Construction and Architectural Management, 26(9), 2120-2146. HYPERLINK "https://doi.org/10.1108/ecam-07-2018-0281" https://doi.org/10.1108/ecam-07-2018-0281