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Simulation as a Construction Tool and Its Economics Essay


Introduction

In the course of recent decades, simulation has become a widely used tool in the field of construction. Simulation may take various forms and is used for different purposes within the construction process. To ensure the stability of the progress in the use of simulation for construction purposes, it is necessary to perform a constant evaluation and assessment of the work that has already been done. The present work contains a comprehensive literature review on simulation as a construction tool and such related topics as the economic aspects of construction and the use of simulation to reduce expenses, simulation in product system designs, hybrid simulations and variability, buffers and batches.

Economic aspects of construction processes

The topic of the economic aspects of construction processes is extremely important for anyone interested in planning and fulfilling construction. No wonder that this topic is of special interest of the researchers in this field. Among the brilliant ideas expressed by specialists that aim to influence the economic side of construction, the idea of A. A. Alsudairi, the professor of architecture, can be mentioned.

Professor Alsudairi (2015) has proposed to use simulation as a tool for reducing the costs of construction. To illustrate his idea with examples, the author picked two case studies. The first one was focused on the construction of one of the campuses of the University of Dammam, and the second one was dealing with the repairmen of the local electrical company. In the University case study, the sequence of actions causing four key scenarios was modified.

In the electrical company case study, the concept of multi-skilled technicians was applied. The simulation models were maintained and altered with the use of flowcharts. Professor Alsudairi managed to prove that the use of simulation models for construction was an efficient decision. It allowed to develop complicated systems accurately and in a relatively short period of time. The use of simulation models was also proven to be cost-effective.

It also helps to perform an analysis of the available benefits and necessary costs prior to beginning the construction process. Even though such obstacles as weak process design can hinder the success in particular cases, this research may have great implications for construction since it presents a wise, effective, sustainable and well-working solution of the funding problem that is among the primary concerns of those in charge of construction and maintenance.

Simulation as a tool for production system designs

Simulation is also considered to be a valuable tool for developing production system designs. Because of the lack of structured environment and the exclusiveness of the constructed facilities, the theories that help automate work processes and analyse their overall efficiency did not receive substantial attention of the researchers until the very end of the 20th century. Over the last two decades of the 20th century, the concepts of system simulation have been extensively researched in the context of the design and analysis of various construction operations.

It was then understood that the majority of the processes that are going on during construction are monotonous and repetitive and, therefore, can be modelled and automated. One of the first researchers to express this opinion were AbouRizk with the colleagues (AbouRizk et al. 1992).

Even though the complete automation of equipment was regarded as an impossible task those times because of the variety of uncontrolled and frequently changing conditions that influenced the construction process (Paulson 1985), the other industries had already adopted automation, and it managed to penetrate the construction industry, which was recognized by the researchers in this field. The researchers, such as Vanegas and the colleagues (1993), AbouRizk and Dozzi (1993), and Griffis with the colleagues (1991), developed new software-based planning tools, including simulation, and these tools were employed by the industry.

A variety of case studies and analytical works, especially those focused on the process-based type of simulation, were performed after Lluch and Halpin (1981) have successfully introduced the new CYCLONE methodology. Among the other works that made the construction world acquainted with CYCLONE were the works of Paulson (1978), Dabbas and Halpin (1982) and Chang and Carr (1987).

Then, the methodology was expanded and applied to the object-oriented simulation environments. Liu and Ioannou (1992) applied it to COOPS, Odeh with the colleagues (1992) used it for CIPROS, and Martinez, along with Ioannou (1994) employed it for STROBOSCOPE. Ziegler (1987) and Luna (1992) have described modularity and hierarchical concepts, respectively. Oloufa (1993) has worked out a unique approach in order to demonstrate the benefits of the use of object-oriented simulation for the facilitation of construction processes. In addition to this, in his 1993 work, Oloufa has stressed the importance of connecting simulation objects with the actual construction-like objects while developing simulation models.

After these achievements have been obtained, S.M. AbouRizk and D. Hajjar (1997) have made an attempt to overcome the mostly theoretical nature of this direction (the issue was confined within the borders of the academic environment). The scholars intended to make the methodology more acceptable, clear and understandable for the industrial sphere. Closing the gap between theory and practice, the researchers have prepared the path for the industry to adopt this approach.

In their article, they have accumulated five years of research work in a very industrial environment, where they tested their simulation tools in close collaboration with several construction firms. AbouRizk and Hajjar also presented a summary of three simulation implementations for every moving contractor, as well as for every aggregate producer and general contractor. The three implementations were developed to reflect the three ‘world views’ of simulation, particularly ‘dynamic process interaction, continuous time-dependent and static simulation’.

The work had serious practical implications for construction. The approach developed by the authors was adopted and successfully employed by several construction companies working in Western Canada, such as the North American Construction Group. The findings of AbouRizk and Hajjar and the successful translation of these findings into reality proves the reliability and validity of simulation and justifies its use in construction. Still, the room for further research exists in this field.

Hybrid simulation

The topic of combining various types of simulations, which leads to the forming of hybrid simulations, is also rather important and popular among researchers. Simulation has become one of the most often used techniques in such fields as operational and managerial research. In the field of industry, simulation is recognized as a powerful tool for modifying the construction process and increasing the level of productivity.

Park has identified the main factors that may influence the level of productivity: the planned overtime, the orders of change, the maintenance of materials, weather and the human factor. The clear and plain presentation of these factors created an opportunity to use this knowledge for developing a comprehensive model that will facilitate productivity estimation within a particular chain of manufacturing.

The researchers typically use historical data from similar projects conducted in previous years to form a basis for their new projects. As an example, Marzouk and Moselhi (2003) have worked out a decision support system named WEATHER intended to study the weather conditions and assess their impact on the current productivity of various steps of the construction operations. Not only does this model estimate the productivity of the construction process, but it also assesses the duration of the activities and analyse the weather patterns in various modes to ensure the highest level of accuracy of the planning process and scheduling.

Moselhi and Marzouk have proven the usefulness of their model. They have studied its impact on 57 construction projects and concluded that the model was beneficial for planning since it allowed to estimate productivity with a higher level of accuracy. They have also discovered a strong connection between weather and productivity, which further proves the usefulness of their model. Even though it is a well-developed and highly useful model, its invention opens the path to further research since new models that take into account not only weather conditions but also other external factors influencing the construction process are needed.

A significant contribution was made by Forrester (1961), who has introduced the SD method. This method was developed for the analysis of the complex system behaviour and was intended to be used in industrial management. The method was widely used for social science research since, for this kind of research, feedback, and holistic view are vital, and the SD method allowed to identify the feedback responsive to the system behaviour. SD helps close gaps between the actual performance for the project and the established goals.

As for the other models for productivity estimation, the regression model should be mentioned. When it comes to the consideration of specific factors, the regression model is the most convenient model. It was developed by Hanna and the colleagues in order to examine the impact of change orders on the overall productivity of the entire construction process. Koehn and Brown (1985) used non-linear equations to investigate the influence of the changes in weather on the general level of productivity. In the filed of productivity estimation, such an important achievement as the use of the learning curve theory has led to serious implications.

According to Oglesby, Parker, and Howell (1989), the learning curve theory explains that the productivity in a monotonous, repetitive process can increase gradually because of the fact that the actor was becoming more and more familiar with the process and used to it. Mirahadi and Zayed (2016) have investigated the issue of the use of Fuzzy models and Artificial Neural Network systems for hybrid simulations. They have stated that Neural-Network-Driven Fuzzy Reasoning possesses great potential for the future.

Variability, buffers and batches

Among the most widely researched topics in this field is the topic of variability, buffering and batching.

Hopp and Spearman (2000) have defined variability as the concept that means ‘non-uniformity in a class of entities’. In the research literature, this concept is closely linked to the concept of randomness. Hopp and Spearman distinguish random variability, i.e. when it occurs as a result of uncontrolled circumstances, and controllable variability that occurs as a result of a managerial decision. Random variability is impossible to control or predict, as the researchers state.

Kim (2002) has done an important task of identifying the root causes of the emergence of variability in the construction process. According to Kim, these causes include the insufficient standardisation of the parts and operations of the construction process, as well as the indicators of managers, such as penalties and incentives. Howell with the colleagues (1993) and Ballard (2000) also wrote on this issue.

They have established that the other root causes of this event include the strong linkage between various construction activities and the low level of such activities that are completed exactly as they were planned. Schmenner (1993) has also discussed the problem of demand variability and forecasting. He established that such forecasts could be done with the use of both qualitative and quantitative methodology.

Alves and Tommelein (2004) have given a comprehensive definition of buffering and batching. They established that batching practices define the way in which buffers operate in the system.

Hopp and Spearman (2000) claim that buffers can be employed for the protection of the system against variability by using the combination of inventory, time and capacity or each of these resources alone. According to the scholars, the use of buffers can protect the system against such disasters as the shortage of materials, the low quality of customer service, extensive lead times, the randomness of the operation cycle times and other problems created by variability.

Shingo (1989) worked out a ‘cushion stock system’ that can help to handle the introduction and size of buffers used in production systems. The researcher cited the example of Toyota Production Systems that had successfully employed such an approach. According to the scholar, even though Toyota intends to achieve non-stock production, everybody understands that stock is necessary to handle the unexpected problems that may arise during the production cycle. In such a way, Shingo has emphasized the importance of his ‘cushion stock system’ for the cases of emergency since a variety of problems may arise in the course of the manufacturing process.

The batching practices are a well-researched topic. Arbulu and the colleagues (2002) have devoted a serious work to this topic, stating that batch sizing is an important issue, and a right approach to this issue can aid the production system with obtaining the necessary quotas for buffers in order to prevent the starvation of the system. Shingo (1988) has also touched this topic. According to him, the Toyota Production System presents a perfect example of a system that questioned the concept of established set-up and long-time durations by means of developing several ways that allowed to reduce these durations from inside and outside. Shingo also cites a practise employed by Toyota in order to reduce set-ups.

The firm has used a single-minute exchange of a die that made set-ups of all the equipment and machines reduced to the minimum. Using the relevant examples from Toyota operation, Shingo manages to prove the importance of the right batch sizing for the outcomes of the production process and reducing the possibilities of an unexpected problem.

Conclusion

This work presents a comprehensive review on the topic of simulation in construction. It touches such important subtopics as the use of simulation to reduce expenses, simulation in product system designs, hybrid simulations and variability, buffers and batches.

Reference List

AbouRizk, SM & Dozzi, SP 1993, ‘Applications of simulation in resolving construction disputes’, ASCE Journal of Construction Engineering and Management, vol. 119, no. 2, pp. 355-373.

AbouRizk. SM & Hajjar, D 1997, ‘A framework for applying simulation in construction’, Journal of Construction Engineering and Management, vol. 123, no. 1, pp. 26-33.

Alsudairi, AA 2015, ‘Simulation as a tool for assessing the economical aspects of construction processes’, Procedia Engineering vol. 118, no. 1, pp. 1086-1095.

Alves, TCL & Tommelein, ID 2004, ‘Simulation of buffering and batching practices’, Proceedings of the 12th Annual Conference: International Group for Lean Construction, pp. 277-290.

Ballard, GH 2000, The last planner system of production control. Ph.D. Thesis.. School of Civil Engineering. The University of Birmingham, Birmingham, UK.

Chang, DY & Carr, RI 1987, ‘RESQUE: a resource oriented simulation system for multiple resource constrained processes,’ Proceedings of the PMI Seminar/Symposium, pp. 4-19.

Dabbas, M & Halpin, DW 1982, ‘Integrated project and process management’, ASCE Journal of the Construction Division, vol. 109, no. CO1, pp. 361-373.

Forrester, J 1961, Industrial dynamics, Productivity Press, Cambridge, Massachusetts.

Griffis, FH, Rubinson, D, O’Brien, WJ & Sascha, R 1991, ‘Productivity applications: 3D models & simulation, preparing for construction in the 21st century’, Proceedings, Construction Congress ‘91, American Society of Civil Engineers, pp. 247-252.

Hopp, WJ & Spearman, ML 2000, Factory physics. McGraw-Hill International Editions, Boston, Massachusetts.

Howell, GA, Laufer, A & Ballard, G 1993, ‘Interaction between sub-cycles: One key to improved methods.” Journal of Construction Engineering and Management, vol. 119, no. 4, pp. 714-728.

Kim, YW 2002, The implications of a new production paradigm for project cost control. Ph.D. Dissertation, University of California, Berkeley, California.

Koehn, E & Brown, J 1985, ‘Climatic effects on construction’, Journal of Construction Engineering and Management, vol. 111, no. 2, pp. 129-137.

Liu, LY & Ioannou, PG 1992, ‘Graphical object-oriented discrete-event simulation system’, Proceedings of the 1992 Winter Simulation Conference, pp. 1285-1291.

Lluch, JF & Halpin, DW 1981, ‘Analysis of construction operations using microcomputers’, ASCE Journal of the Construction Division, vol. 108, no. CO, pp. 129-145.

Luna, J 1992, ‘Hierarchical, modular concepts applied to an object-oriented simulation model development environment’, Proceedings of the 1992 Winter Simulation Conference, pp. 694-699.

Marzouk, M & Moselhi, O 2003, ‘Object-oriented simulation model for earthmoving operations’, Journal of Construction Engineering and Management, vol. 129, no. 3, pp. 173-182.

Mirahadi, F & Zayed, T 2016, ‘Simulation-based construction productivity forecast using Neural-Network-Driven fuzzy reasoning’, Automation in Construction, vol. 65, no. 1, pp. 102-115.

Odeh, AM, Tommelein, ID & Carr, RI 1992, ‘Knowledge-based simulation of construction plans’, Proceedings of the 8th Conference on Computing in Civil Engineering, pp. 1042-1049.

Oloufa, A.A. 1993. Modelling operational activities in object-oriented simulation. ASCE Journal of Computing in Civil Engineering, 7(1): 94–106.

Oglesby, CH, Parker, HW & Howell, GA 1989, Productivity improvement in construction, McGraw-Hill, New York City, New York.

Paulson, BC 1978, ‘Interactive graphics for simulating construction operations’, ASCE Journal of the Construction Division, vol. 104, no. 1, pp. 69-76.

Paulson, BC 1985, ‘Automation and robotics for construction’, ASCE Journal of Construction Engineering and Management, vol. 111, no. 3, pp. 190-207.

Schmenner, RW 1993, Production/operations management. Prentice Hall, Englewood Cliffs, New Jersey.

Shingo, S 1988, Non-stock production: the Shingo system for continuous improvement. Productivity Press, Cambridge, Massachusetts.

Shingo, S 1989, A study of the Toyota production system. Productivity Press, Portland, Oregon.

Vanegas, JA, Bravo, EB & Halpin, DW 1993, ‘Simulation technologies for planning heavy construction processes’, ASCE Journal of Construction Engineering and Management, no. 119, vol. 2, pp. 336-354.

Ziegler, BP 1987, ‘Hierarchical, modular discrete-event modelling in an object-oriented environment’, Simulation, vol. 49, no. 5, pp. 219-230.

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"Simulation as a Construction Tool and Its Economics." IvyPanda, 9 Sept. 2020, ivypanda.com/essays/simulation-as-a-construction-tool-and-its-economics/.

1. IvyPanda. "Simulation as a Construction Tool and Its Economics." September 9, 2020. https://ivypanda.com/essays/simulation-as-a-construction-tool-and-its-economics/.


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IvyPanda. 2020. "Simulation as a Construction Tool and Its Economics." September 9, 2020. https://ivypanda.com/essays/simulation-as-a-construction-tool-and-its-economics/.

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