Method for Generating Multiple MRR Solutions for Application in Cost-Leveling Models
Publication: Journal of Infrastructure Systems
Volume 23, Issue 3
Abstract
Cost-leveling models have been introduced recently to minimize the fluctuations in annual maintenance, repair, and rehabilitation (MRR) costs within a multiyear infrastructure asset-management plan. However, these models were found to produce limited results because they ignored the impacts on all the preceding/succeeding MRR activities in a multiyear plan when the initially identified implementation time of one MRR activity was adjusted for cost-leveling purposes. This paper proposes a method to generate multiple MRR solutions at the facility level for existing cost-leveling models at the network level. The method utilizes two concepts as follows: (1) the fast elitist nondominated sorting genetic algorithm (NSGA-II) to generate a Pareto optimal set of feasible MRR alternatives, and (2) the pseudoweight vector approach with the Euclidean distance and a confidence interval to select multiple MRR solutions from the Pareto optimal set identified by NSGA-II. The proposed method is applied to a sample concrete bridge deck as a case study in this paper; the results show that the proposed method was able to provide multiple MRR solutions having various profiles in terms of the intervention times of the MRR activities and annual MRR costs. Consequently, the proposed method advances the theoretical framework of existing cost-leveling models by combining four approaches to provide multiple MRR solutions at the facility level: (1) NSGA-II algorithm, (2) pseudoweight vector approach, (3) Euclidean distance, and (4) a confidence interval. In addition, it is expected that the proposed method can be well applied to existing multiobjective optimization models to generate a set of nondominated good policies for infrastructure asset management.
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Acknowledgments
This study was supported by the South Korean Ministry of Education, Science, and Technology (NRF-2014R1A1A1004766).
References
AASHTO. (2007). “Maintenance manual for roadways and bridges.” Washington, DC.
Affenzeller, M. (2009). Genetic algorithms and genetic programming: Modern concepts and practical applications, CRC Press, Boca Raton, FL.
ASCE. (2013). “2013 ASCE report card.” Washington, DC.
Bolar, A., Tesfamariam, S., and Sadiq, R. (2014). “Management of civil infrastructure systems: QFD-based approach.” J. Infrastruct. Syst., .
Cadini, F., Zio, E., and Petrescu, C. A. (2010). “Optimal expansion of an existing electrical power transmission network by multi-objective genetic algorithms.” Reliab. Eng. Syst. Saf., 95(3), 173–181.
Chen, J. S., and Hou, J. L. (2006). “A combination genetic algorithm with applications on portfolio optimization.” Advances in applied artificial intelligence, M. Ali and R. Dapoigny, eds., Springer, Berlin, 197–206.
Cho, K., and Hastak, M. (2013). “Time and cost-optimized decision support model for fast-track projects.” J. Constr. Eng. Manage., 90–101.
Coello, C. A. (2000). “An updated survey of GA-based multiobjective optimization techniques.” ACM Comput. Surv., 32(2), 109–143.
Coello, C. A. (2007). “Constraint-handling techniques used with evolutionary algorithms.” Proc., Genetic and Evolutionary Computation Conf. 2007, Association for Computing Machinery, New York.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, New York.
Diaz-Gomez, P. A., and Hougen, D. F. (2007). “Initial population for genetic algorithms: A metric approach.” Proc., 2007 Int. Conf. on Genetic and Evolutionary Methods, World Academy of Science, Engineering and Technology, Turkey.
Eiben, A. E., Hinterding, R., and Michalewicz, Z. (1999). “Parameter control in evolutionary algorithms.” IEEE Trans Evol. Comput., 3(2), 124–141.
Field, P. (1994). “Nonbinary transforms for genetic algorithm problems.” Evolutionary computing, T. Fogarty, ed., Springer, Berlin, 38–50.
Fwa, T. F., Chan, W. T., and Hoque, K. Z. (2000). “Multiobjective optimization for pavement maintenance programming.” J. Transp. Eng., 367–374.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addision-Wesley, Reading, MA.
Harris, R. B. (1978). Precedence and arrow networking techniques for construction, Wiley, New York.
Hou, Y. (2006). “Budgeting for fiscal stability over the business cycle: A countercyclical fiscal policy and the multiyear perspective on budgeting.” Public Administration Rev., 66(5), 730–741.
INDOT (Indiana Department of Transportation). (2013). INDOT design manual, Indianapolis.
INDOT (Indiana Department of Transportation). (2014). INDOT bridge and culvert preservation initiative policy statement, Indianapolis.
Kim, J., Han, S., and Hyun, C. (2015). “Minimizing fluctuation of the maintenance, repair, and rehabilitation cost profile of a building.” J. Perform. Constr. Facil., .
Konak, A., Coit, D. W., and Smith, A. E. (2006). “Multi-objective optimization using genetic algorithms: A tutorial.” Reliab. Eng. Syst. Saf., 91(9), 992–1007.
Liu, L. N., and Liu, W. L. (2015). “Study on a multi-objective optimization model for multi-reservoir ecological operation.” Energy and environmental engineering, Wu, Y., ed., Taylor & Francis Group, London, 109–112.
Liu, M., and Frangopol, D. (2005). “Balancing connectivity of deteriorating bridge networks and long-term maintenance cost through optimization.” J. Bridge Eng., 468–481.
Lunacek, M., and Whitley, D. (2006). “The dispersion metric and the CMA evolution strategy.” Proc., Genetic and Evolutionary Computation Conf. 2006, Association for Computing Machinery, New York.
Madanat, S., and Ben-Akiva, M. (1994). “Optimal inspection and repair policies for infrastructure facilities.” Transp. Sci., 28(1), 55–62.
Malhotra, R., Singh, N., and Singh, Y. (2011). “Genetic algorithms: Concepts, design for optimization of process controllers.” Comput. Inf. Sci., 4(2), 39–54.
Marinakis, Y., and Marinaki, M. (2010). “A hybrid genetic–particle swarm optimization algorithm for the vehicle routing problem.” Expert Syst. Appl., 37(2), 1446–1455.
Maulik, U., Bandyopadhyay, S., and Mukhopadhyay, A. (2011). “Genetic algorithms and multiobjective optimization.” Multiobjective genetic algorithms for clustering, Springer, Berlin.
Osman, H. (2012). “Agent-based simulation of urban infrastructure asset management activities.” Autom. Constr., 28, 45–57.
Prasad, T. D., and Park, N. S. (2004). “Multiobjective genetic algorithms for design of water distribution networks.” J. Water Resour. Plann. Manage., 73–82.
Robbins, W., and Baldwin, A. (2002). “GASB 34: New requirements for general capital assets.” CPA J., 72(10), 48–54.
Romero, C., Tamiz, M., and Jones, D. F. (1998). “Goal programming, compromise programming and reference point method formulations: Linkages and utility interpretations.” J. Oper. Res. Soc., 49(9), 986–991.
Šelih, J., Kne, A., Srdić, A., and Žura, M. (2008). “Multiple-criteria decision support system in highway infrastructure management.” Transport, 23(4), 299–305.
Sinha, K. C., Labi, S., McCullouch, B. G., Bhargava, A., and Bai, Q. (2009). “Updating and enhancing the indiana bridge management system (IBMS).”, Indiana Dept. of Transportation and Purdue Univ., West Lafayette, IN.
Sivanandam, S. N., and Deepa, S. N. (2008). Introduction to genetic algorithms, Springer, Berlin.
SolveXL [Computer software]. Exeter Advanced Analytics LLP, Devon, U.K.
USOMB (U.S. Office of Management and Budget). (2015). “2015 discount rates for OMB circular no. A-94.” ⟨https://www.whitehouse.gov/sites/default/files/omb/memoranda/2015/m-15-05.pdf⟩ (Sep. 15, 2015).
Varadarajan, M., and Swarup, K. S. (2008). “Solving multi-objective optimal power flow using differential evolution.” Gener. Trans. Distrib., 2(5), 720–730.
Vermeer, T. E., Patton, T. K., and Styles, A. K. (2011). “Reporting of general infrastructure assets under GASB Statement No. 34.” Accounting Horiz., 25(2), 381–407.
Vogt, A. J., and Rivenbark, W. C. (2007). “County and municipal government in North Carolina article 15: Budget preparation and enactment.” Univ. of North Carolina, Chapel Hill, NC.
Wang, N. F., and Tai, K. (2010). “Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms.” Comput. Struct., 88(19–20), 1064–1076.
Yoon, Y. (2012). “Planning of optimal rehabilitation strategies for infrastructure using time float and multiyear prioritization approach.” Ph.D. dissertation, Purdue Univ., West Lafayette, IN.
Yoon, Y., Shah, H., Hastak, M., and Lee, J. (2014). “Leveling process of annual budgetary requirements for pavement preservation projects.” J. Infrastruct. Syst., .
Zheng, D. X., Ng, S. T., and Kumaraswamy, M. M. (2004). “Applying a genetic algorithm-based multiobjective approach for time-cost optimization.” J. Constr. Eng. Manage., 168–176.
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©2016 American Society of Civil Engineers.
History
Received: Dec 30, 2015
Accepted: Sep 22, 2016
Published online: Nov 22, 2016
Discussion open until: Apr 22, 2017
Published in print: Sep 1, 2017
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