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Abstract: Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or design phase, built on static empirical data. However, due to its intrinsic uncertainties, rockfill dam construction is a dynamic process which requires the tradeoff to adjust dynamically to changes in construction conditions. In this study, a dynamic time-cost-quality tradeoff (DTCQT) method is proposed to balance time, cost, and quality at any stage of the construction process. A time-cost-quality tradeoff model is established that considers time cost and quality cost. Time, cost, and quality are dynamically estimated based on real-time monitoring. The analytic hierarchy process (AHP) method is applied to quantify the decision preferences among time, cost, and quality as objective weights. In addition, an improved non-dominated sorting genetic algorithm (NSGA-II) coupled with the technique for order preference by similarity to ideal solution (TOPSIS) method is used to search for the optimal compromise solution. A case study project is analyzed to demonstrate the applicability of the method, and the efficiency of the proposed optimization method is compared with that of the linear weighted sum (LWS) and NSGA-II.

This paper focuses on field construction management (e.g., suitable allocation of machinery) of rockfill dam construction by addressing dynamic time-cost-quality tradeoff (DTCQT) over the construction process. Multiple techniques have been integrated to implement the proposed method, including GPS+RFID-based real-time monitoring, discrete-event simulation for estimating time, nonlinear programming-based mathematical model of the DTCQT problem, analytic hierarchy process (AHP) method for quantifying the decision preferences and genetic algorithm (GA) for searching for optimal solutions. The study also addresses the nonlinear relationship between cost and quality as well as indirect cost such as prevention cost of the rockfill dam construction. The proposed method has been justified through a case study by comparing with the traditional methods. This study contributes to development and applicability of construction management as well as broadening and combination of real-time monitoring, simulation and optimization techniques. In addition, the proposed methodology has the potential to be developed along with modern information techniques such as cloud-computing, big data and BIM for intelligent construction management. Moreover, the proposed method can be applied to construction management for other engineering projects once the relevant particularities are considered. Generally, the paper is written and structured well.

基于实时监控的面板堆石坝施工进度-成本-质量动态均衡

[1]Afruzi, E.N., Najafi, A.A., Roghanian, E., et al., 2014. A multi-objective imperialist competitive algorithm for solving discrete time, cost, and quality trade-off problems with mode-identity and resource-constrained situations. Computers & Operations Research, 50(10):80-96.

[2]Akkan, C., Drexl, A., Kimms, A., 2005. Network decomposition-based benchmark results for the discrete time-cost tradeoff problem. European Journal of Operational Research, 165(2):339-358.

[3]Azaron, A., Perkgoz, C., Sakawa, M., 2005. A genetic algorithm approach for the time-cost trade-off in PERT networks. Applied Mathematics and Computation, 168(2):1317-1339.

[4]Burns, S.A., Liu, L., Feng, C.W., 1996. The LP/IP hybrid method for construction time-cost trade-off analysis. Construction Management & Economics, 14(3):265-276.

[5]Cheng, J., Duan, G.F., Liu, Z.Y., et al., 2014. Interval multiobjective optimization of structures based on radial basis function, interval analysis, and NSGA-II. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 15(10):774-788.

[6]Cheng, J., Liu, Z.Y., Wu, Z.Y., et al., 2015. Robust optimization of structural dynamic characteristics based on adaptive Kriging model and CNSGA. Structural and Multidisciplinary Optimization, 51(2):423-437.

[7]De, P., Dunne, E.J., Ghosh, J.B., et al., 1997. Complexity of the discrete time-cost tradeoff problem for project networks. Operations Research, 45(2):302-306.

[8]Deb, K., Pratap, A., Agarwal, S., et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197.

[9]El-Rayes, K., Kandil, A., 2005. Time-cost-quality trade-off analysis for highway construction. Journal of Construction Engineering & Management, 131(4):477-486.

[10]Fallah-Mehdipour, E., Haddad, O.B., Tabari, M.M.R., et al., 2012. Extraction of decision alternatives in construction management projects: application and adaptation of NSGA-II and MOPSO. Expert Systems with Applications, 39(3):2794-2803.

[11]Giretti, A., Carbonari, A., Naticchia, B., et al., 2009. Design and first development of an automated real-time safety management system for construction sites. Journal of Construction Engineering & Management, 15(4):325-336.

[12]Gomar, J.E., Haas, C.T., Morton, D.P., 2002. Assignment and allocation optimization of partially multi skilled workforce. Journal of Construction Engineering & Management, 128(2):103-109.

[13]Heravi, G., Faeghi, S., 2014. Group decision making for stochastic optimization of time, cost, and quality in construction projects. Journal of Computing in Civil Engineering, 28(2):275-283.

[14]Heravi, G., Jafari, A., 2014. Cost of quality evaluation in mass-housing projects in developing countries. Journal of Construction Engineering and Management, 140(5):63-70.

[15]Hildreth, J., Vorster, M., Martinez, J., 2005. Reduction of short-interval GPS data for construction operations analysis. Journal of Construction Engineering and Management, 131(8):920-927.

[16]Hindelang, T.J., Muth, J.F., 1979. A dynamic programming algorithm for decision CPM networks. Operations Research, 27(2):225-241.

[17]Hwang, C.L., Yoon, K., 1981. Multiple Attribute Decision Making: Methods and Applications, A State-of-the-Art Survey. Springer-Verlag, New York, USA, p.58-191.

[18]Jin, X., Zhang, J., Gao, J.L., et al., 2008. Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II. Journal of Zhejiang University-SCIENCE A, 9(3):391-400.

[19]Juran, J.M., 1998. Juran’s Quality Handbook, 5th Edition. McGraw-Hill, New York, USA, p.8.4-8.12.

[20]Kelley, J.E., 1961. Critical-path planning and scheduling: mathematical basis. Operations Research, 9(3):296-320.

[21]Khaled Omar, M., Murgan, M., 2014. An improved model for the cost of quality. International Journal of Quality & Reliability Management, 31(4):395-418.

[22]Khataie, A.H., Bulgak, A.A., 2013. A cost of quality decision support model for lean manufacturing: activity-based costing application. International Journal of Quality & Reliability Management, 30(7):751-764.

[23]Liu, D.H., Sun, J., Zhong, D.H., et al., 2012. Compaction quality control of earth-rock dam construction using real-time field operation data. Journal of Construction Engineering & Management, 138(9):1085-1094.

[24]Liu, D.H., Li, Z.L., Lian, Z.H., 2014. Compaction quality assessment of earth-rock dam materials using roller-integrated compaction monitoring technology. Automation in Construction, 44:234-246.

[25]Liu, D.H., Lin, M., Li, S., 2016. Real-time quality monitoring and control of highway compaction. Automation in Construction, 62:114-123.

[26]Monghasemi, S., Nikoo, M.R., Fasaee, M.A.K., et al., 2015. A novel multi criteria decision making model for optimizing time-cost-quality trade-off problems in construction projects. Expert Systems with Applications, 42(6):3089-3104.

[27]Montaser, A., Moselhi, O., 2012. RFID+ for tracking earthmoving operations. Construction Research Congress 2012: Construction Challenges in a Flat World, p.1011-1020.

[28]Moselhi, O., El-Rayes, K., 1993. Scheduling of repetitive projects with cost optimization. Journal of Construction Engineering & Management, 119(4):681-697.

[29]Mungle, S., Benyoucef, L., Son, Y.J., et al., 2013. A fuzzy clustering-based genetic algorithm approach for time-cost-quality trade-off problems: a case study of highway construction project. Engineering Applications of Artificial Intelligence, 26(8):1953-1966.

[30]Peng, W.L., Wang, C.G., 2009. A multi-mode resource-constrained discrete time-cost tradeoff problem and its genetic algorithm based solution. International Journal of Project Management, 27(6):600-609.

[31]Radziwill, N.M., 2006. Cost of quality (CoQ) metrics for telescope operations and project management. Proceedings of SPIE-The International Society for Optical Engineering, No. 627104.

[32]Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1):9-26.

[33]Schiffauerova, A., Thomson, V., 2006. A review of research on cost of quality models and best practices. International Journal of Quality & Reliability Management, 23(6):647-669.

[34]Senouci, A.B., Eldin, N.N., 1996. Dynamic programming approach to scheduling of nonserial linear project. Journal of Computing in Civil Engineering, 10(2):106-114.

[35]Shannon, C.E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27:379-423.

[36]Sonmez, R., Bettemir, O.H., 2012. A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert Systems with Applications, 39(13):11428-11434.

[37]Srinivas, N., Deb, K., 1994. Multiobjective optimization using nondominated sorting genetic algorithms. Evolutionary Computation, 2(3):221-248.

[38]Taguchi, G., 1986. Introduction to quality engineering: designing quality into products and processes. Asian Productivity Organization, p.17-21.

[39]Tavana, M., Abtahi, A.R., Khalili-Damghani, K., 2014. A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems. Expert Systems with Applications, 41(4):1830-1846.

[40]Tran, D.H., Cheng, M.Y., Cao, M.T., 2015. Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem. Knowledge-Based Systems, 74(1):176-186.

[41]Vanhoucke, M., Debels, D., Sched, J., 2007. The discrete time/cost trade-off problem: extensions and heuristic procedures. Journal of Scheduling, 10(4-5):311-326.

[42]Yang, I.T., 2011. Stochastic time-cost tradeoff analysis: a distribution-free approach with focus on correlation and stochastic dominance. Automation in Construction, 20(7):916-926.

[43]Zhang, L., Du, J., Zhang, S., 2014. Solution to the time-cost-quality trade-off problem in construction projects based on immune genetic particle swarm optimization. Journal of Management in Engineering, 30(2):163-172.

[44]Zhang, P., 2010. Research on Simulation and Schedule Control Basing on Real-time Monitoring for High Core Rockfill Dam. PhD Thesis, Tianjin University, Tianjin, China (in Chinese).

[45]Zhong, D.H., Zhang, P., 2009. Theory and application of construction simulation for high core rock-fill dam based on real-time monitoring. Water Resources and Hydropower Engineering, 8(40):103-107.

[46]Zhong, D.H., Zhang, P., Wu, K.X., 2007. Theory and practice of construction simulation for high rock-fill dam. Science in China Series E: Technological Sciences, 50(1):51-61.

[47]Zhong, D.H., Cui, B., Liu, D.H., 2009. Theoretical research on construction quality real-time monitoring and system integration of core rock-fill dam. Science in China Series E: Technological Sciences, 52(11):3406-3412.

[48]Zhong, D.H., Liu, D.H., Cui, B., 2011. Real-time compaction quality monitoring of high core rockfill dam. Science China Technological Sciences, 54(7):1906-1913.