Simulation for Selecting Road Works Equipment
DOI:
https://doi.org/10.3846/bjrbe.2015.27Keywords:
roadwork planning, repaving, queuing model, simulation, CYCLONE, optimizationAbstract
Efficiency of road works is conditioned by selection of machines and synchronizing their operations. Modelling these works using queuing theory allows the planner to conduct an in-depth analysis of the system’s operation to find the best machine types and their optimal number. For simple cases, analytic formulas are used to calculate such parameters as a probability of a server’s standing idle, average length of a queue, or average waiting time. However, complex real-life systems are to be analyzed more efficiently by means of simulations. The paper presents simulation model of a road repaving project. Using it, the authors evaluated the economic effect and output of the system served by different machine sets. Applying a number of additional optimization criteria (cost, productivity, machine utilization rates, etc.) the authors were able to find most suitable machine sets.References
Abduh, M.; Shanti, F.; Pratama, A. 2010. Simulation of Construction Operation: Search for a Practical and Effective Simulation System for Construction Practitioners, in Proc. of the 1st Makassar International Conference on Civil Engineering (MICCE2010), 9–10 March 2012, Makassar, Indonesia: 1311–1319. Available from Internet: http://www.ftsl.itb.ac.id/kk/manajemen dan_rekayasa_konstruksi/wp-content/ uploads/2010/10/ma-micce.pdf.
Biruk, S.; Jaskowski, P. 2008. Simulation Modelling Construction Project with Repetitive Tasks Using Petri Nets Theory, Journal of Business Economics and Management 9(3): 219–226. http://dx.doi.org/10.3846/1611-1699.2008.9.219-226
Cheng, T.; Feng, Ch. 2003. An Effective Simulation Mechanism for Construction Operations, Automation in Construction 12(3): 227–244. http://dx.doi.org/10.1016/S0926-5805(02)00086-9
Cheng, T.; Wu, H.–T.; Tseng, Y.–W. 2000. Construction Operation Simulation Tool – COST, in Proc. of the S17th IAARC/ CIB/IEEE/IFAC/IFR International Symposium on Automation and Robotics in Construction (ISARC), 18–20 September 2000, Taipei, Taiwan, 1143–1146.
Ghoddousi, P.; Hosseini, M. R. 2012. A Survey of the Factors Affecting the Productivity of Construction Projects in Iran, Technological and Economic Development of Economy 18(1): 99–116. http://dx.doi.org/10.3846/20294913.2012.661203
Halpin, D. W. 1977. CYCLONE – Method for Modeling Job Site Processes, Journal of the Construction Division 103(3): 489–499.
Halpin, D. W.; Jen, H.; Kim, J. 2003. A Construction Process Simulation Web Service, in Proc. of the 35th Winter Simulation Conference, vol 2. Ed. by Chick, S.; Sánchez, P. J.; Ferrin, D.; Morrice, D. J., 7–10 December 2003, New Orleans, USA, 1503–1509. http://dx.doi.org/10.1109/WSC.2003.1261595
Han, Z.; Liu, P. 2011. A Fuzzy Multi–Attribute Decision–Making Method under Risk with Unknown Attribute Weights, Technological and Economic Development of Economy 17(2): 246–258. http://dx.doi.org/10.3846/20294913.2011.580575
Jaskowski, P.; Sobotka, A. 2012. Using Soft Precedence Relations for Reduction of the Construction Project Duration, Technological and Economic Development of Economy 18(2): 262–279. http://dx.doi.org/10.3846/20294913.2012.666217
Jaskowski, P.; Biruk, S. 2011. The Method for Improving Stability of Construction Project Schedules through Buffer Allocation, Technological and Economic Development of Economy 17(3): 429–444. http://dx.doi.org/10.3846/20294913.2011.580587
Keršulienė, V.; Turskis, Z. 2011. Integrated Fuzzy Multiple Criteria Decision Making Model for Architect Selection, Technological and Economic Development of Economy 17(4): 645–666. http://dx.doi.org/10.3846/20294913.2011.635718
Martinez, J. C. 2001. EZStrobe-General-Purpose Simulation System Based on Activity Cycle Diagrams, in Proc. of the 2001 Winter Simulation Conference, 9–12 December 2001, Arlington, USA, 1556–1564. http://dx.doi.org/10.1109/WSC.2001.977485
Napalkova, L.; Merkuryeva, G. 2012. Multi-Objective Stochastic Simulation-Based Optimisation Applied to Supply Chain Planning, Technological and Economic Development of Economy 18(1): 132–148. http://dx.doi.org/10.3846/20294913.2012.661190
Özgün, O.; Barlas, Y. 2009. Discrete vs. Continuous Simulation: When Does it Matter?, in Proc. of the 27th International Conference of the System Dynamics Society, 26–30 July 2009, Albuquerque, NM, USA. Available from Internet: http://www.systemdy- namics.org/conferences/2009/proceed/papers/P1199.pdf.
Paslawski, J. 2011. Flexibility as Risk Management Option Implemented in the Bridge Repair, The Baltic Journal of Road and Bridge Engineering 6(4): 258–266. http://dx.doi.org/10.3846/bjrbe.2011.33
Roy, R. K.; Mohapatra, P. K. J. 1993. A System Dynamics Based Methodology for Numerically Solving Transient Behaviour of Queuing Systems, in Proc. of the International System Dynamics Conference, ed. by Zepeda, E.; Machuca, J. A. D., July 1993, Cancun, Mexico, 408–418. Available from Internet: http://www.systemdynamics.org/conferences/1993/proceed/ roy408.pdf.
Stanujkic, D.; Magdalinovic, N.; Jovanovic, R.; Stojanovic, S. 2012. An Objective Multi-Criteria Approach to Optimization Using MOORA Method and Interval Grey Numbers, Technological and Economic Development of Economy 18(2): 331– 363. http://dx.doi.org/10.3846/20294913.2012.676996
Zolfani, S. H.; Sedaghat, M; Zavadskas, E. K. 2012. Performance Evaluating of Rural ICT Centers (Telecenters), Applying Fuzzy AHP, SAW-G and TOPSIS Grey, a Case Study in Iran, Technological and Economic Development of Economy 18(2): 364–387. http://dx.doi.org/10.3846/20294913.2012.685110
Downloads
Published
Issue
Section
License
Copyright (c) 2015 Vilnius Gediminas Technical University (VGTU) Press Technika
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
