A constraint-based, efficiency optimisation approach to network-level pavement maintenance management

References

• Abaza, K. (2002). Optimum flexible pavement life-cycle analysis model. Journal of Transportation Engineering, 128(6), 542–549. doi:10.1061/(ASCE)0733-947X(2002)128:6(542)
   Web of Science ®Google Scholar
• Abaza, K. (2017). Empirical markovian-based models for rehabilitated pavement performance used in a life cycle analysis approach. Structure and Infrastructure Engineering, 13(5), 625–636. doi:10.1080/15732479.2016.1187180
   Web of Science ®Google Scholar
• Abaza, K., Ashur, S., & Al-Khatib, I. (2004). Integrated pavement management system with a markovian prediction model. Journal of Transportation Engineering, 130(1), 24–33. doi:10.1061/(ASCE)0733-947X(2004)130:1(24)
   Web of Science ®Google Scholar
• Alfandari, L., Plateau, A., & Tolla, P. (2001). A two-phase path relinking algorithm for the generalized assignment problem. In Proceedings of the Fourth Metaheuristics International Conference (pp. 175–179).
   Google Scholar
• Archilla, A. (2006). Repeated measurement data analysis in pavement deterioration modeling. Journal of Infrastructure Systems, 12(3), 163–173. doi:10.1061/(ASCE)1076-0342(2006)12:3(163)
   Web of Science ®Google Scholar
• Archondo-Callao, R. (2008). Applying the HDM-4 model to strategic planning of road works. Washington, DC: World Bank.
   Google Scholar
• Bardeesi, M., & Attallah, Y. (2015). Economic and environmental considerations for pavement management systems. European Scientific Journal, ESJ, 11(29).
   Google Scholar
• Boyd, S., & Vandenberghe, L. (2004). Convex optimization. New York, NY: Cambridge University Press.
   Google Scholar
• Cafiso, S., & Di Graziano, A. (2012). Definition of homogenous sections in road pavement measurements. Procedia-Social and Behavioral Sciences, 53, 1069–1079. doi:10.1016/j.sbspro.2012.09.956
   Google Scholar
• Cattrysse, D. (1990). Set partitioning approaches to combinatorial optimization problem. Unpublished doctoral dissertation, Katholieke Universiteit Leuven, Centrum Industrieel Beleid, Belgium.
   Google Scholar
• Cattrysse, D., Salomon, M., & Van Wassenhove, L. (1994). A set partitioning heuristic for the generalized assignment problem. European Journal of Operational Research, 7272, 167–174. doi:10.1016/0377-2217(94)90338-7
   Google Scholar
• Chabot, A., Tran, Q., & Ehrlacher, A. (2005). A simplified modeling for cracked pavements. Bulletin des Laboratoires des Ponts et Chaussées, 258, 105–120.
   Google Scholar
• Cheng-Yu, D., Cupjin, H., & Kevin, S. (2018). Implementation of approximation algorithm for the generalized assignment problem [Computer program]. https://github.com/kevinsung/generalized-assignment.
   Google Scholar
• Chootinan, P., Chen, A., Horrocks, M., & Bolling, D. (2006). A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach. Transportation Research Part A: Policy and Practice, 40(9), 725–743. doi:10.1016/j.tra.2005.12.003
   Web of Science ®Google Scholar
• Chu, P., & Beasley, J. (1997). A genetic algorithm for the generalised assignment problem. Computers & Operations Research, 24(1), 17–23. doi:10.1016/S0305-0548(96)00032-9
   Web of Science ®Google Scholar
• De la Garza, J., Akyildiz, S., & Bish, D. (2011). Development of network level linear programming optimization for pavement maintenance programming. In Proceedings of the international conference on computing in civil and building engineering. University of Nottingham, UK.
   Google Scholar
• Dıaz, J., & Fernández, E. (2001). A tabu search heuristic for the generalized assignment problem. European Journal of Operational Research, 132(1), 22–38. doi:10.1016/S0377-2217(00)00108-9
   Web of Science ®Google Scholar
• Eiben, Á. E., Hinterding, R., & Michalewicz, Z. (1999). Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2), 124–141. doi:10.1109/4235.771166
   Web of Science ®Google Scholar
• Feltl, H., & Raidl, G. (2004). An improved hybrid genetic algorithm for the generalized assignment problem. In Proceedings of the 2004 ACM symposium on Applied computing (pp. 990–995).
   Google Scholar
• Fisher, M., Jaikumar, R., & Wassenhove, L. V. (1986). A multiplier adjustment method for the generalized assignment problem. Management Science, 32(9), 1095–1103. Retrieved from doi:10.1287/mnsc.32.9.1095
   Web of Science ®Google Scholar
• Freitas, N., Lepert, P., & Renault, D. (1998). Aide à la gestion de l’entretien des réseaux routiers avec la gamme GiRR [decision support for road network maintenance management with girr software]. Revue Générale des Routes et des Aérodromes, 765, 24–26.
   Google Scholar
• Fwa, T., Chan, W., & Hoque, K. (2000). Multiobjective optimization for pavement maintenance programming. Journal of Transportation Engineering, 126(5), 367–374. doi:10.1061/(ASCE)0733-947X(2000)126:5(367)
   Web of Science ®Google Scholar
• Fwa, T., Chan, W., & Tan, C. (1996). Genetic algorithm programming road maintenance and rehabilitation. ASCE Journal of Transportation Engineering, 122(3), 246–253. doi:10.1061/(ASCE)0733-947X(1996)122:3(246)
   Web of Science ®Google Scholar
• Gao, L., & Zhang, Z. (2008). Robust optimization for managing pavement maintenance and rehabilitation. Transportation Research Record: Journal of the Transportation Research Board, 2084(1), 55–61. doi:10.3141/2084-07
   Google Scholar
• Gaschnig, J. (1978). Experimental case studies of backtrack vs. waltz-type vs. new algorithms for satisficing assignment problems. In Proceedings of the Second Canadian Conference on Artificial Intelligence (pp. 268–277). Toronto.
   Google Scholar
• Ginsberg, M. (1993). Dynamic backtracking. Journal of Artificial Intelligence Research, 1, 25–46. doi:10.1613/jair.1
   Web of Science ®Google Scholar
• Gosse, C., Smith, B., & Clarens, A. (2013). Environmentally preferable pavement management systems. Journal of Infrastructure Systems, 19(3), 315–325. doi:10.1061/(ASCE)IS.1943-555X.0000118
   Web of Science ®Google Scholar
• Harik, G., Cantu-Paz, E., Goldberg, D., & Miller, B. (1999). The gambler’s ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3), 231–253. (Vol. doi:10.1162/evco.1999.7.3.231
   PubMed Web of Science ®Google Scholar
• Herabat, P., & Tangphaisankun, A. (2005). Multi objective optimization model using constraint based genetic algorithms for Thailand pavement management. Journal of the Eastern Asian Society for Transportation Studies, 6, 1137–1152.
   Google Scholar
• Holland, J. (1975). Adaption in natural and artificial systems. Ann Arbor, MI: University of Michigan.
   Google Scholar
• Laboratoire central des Ponts et Chaussées. (1997). Relevé des dégradations de surface des chaussées. Paris: LCPC.
   Google Scholar
• Laboratoire central des Ponts et Chaussées. (1998). Catalogue des dégradations de surface des chaussées [Manuel of pavement surface distress]. Paris: LCPC.
   Google Scholar
• Laguna, M., Kelly, J., González-Velarde, J.-L., & Glover, F. (1995). Tabu search for the multilevel generalized assignment problem. European Journal of Operational Research, 82(1), 176–189. doi:10.1016/0377-2217(93)E0174-V
   Web of Science ®Google Scholar
• Larsen, H., Hildebrand, G., & Macdonald, R. (2002). Economic evaluation of pavement maintenance (No. 114). Copenhagen: Road Directorate, Ministry of Transport.
   Google Scholar
• Lepert, P. (1996). Outil d’aide à la programmation d’entretien GiRR: premières applications en site pilote [Decision support tool for maintenance programming GiRR: Application on a pilot site]. Paris: Laboratoire central des Ponts et Chaussées.
   Google Scholar
• Lepert, P. (2006). Gestion technico-économique des infrastructures routières, [technico-economic maintenance management of road infrastructure]. Bulletin des Laboratoires des Ponts et Chaussées, 261, 3–24.
   Google Scholar
• Lepert, P., Savard, Y., & Leroux, D. (2003). Use of pavement performance models to improve efficiency of data collection procedures. In Maintenance and Rehabilitation of Pavements and Technological ControlInstituto dos Estrados de Portugal, Auto-Estrados de Norte, SA, Galp Engeria, Monte and Monte, SA, 12.
   Google Scholar
• Li, Y., & Madanat, S. (2002). A steady-state solution for the optimal pavement resurfacing problem. Transportation Research Part A: Policy and Practice, 36(6), 525–535. doi:10.1016/S0965-8564(01)00020-9
   Web of Science ®Google Scholar
• Li, Z., & Madanu, S. (2009). Highway project level life-cycle benefit/cost analysis under certainty, risk, and uncertainty: Methodology with case study. Journal of Transportation Engineering, 135(8), 516–526. doi:10.1061/(ASCE)TE.1943-5436.0000012
   Web of Science ®Google Scholar
• Liu, L., Mu, H., Song, Y., Luo, H., Li, X., & Wu, F. (2012). The equilibrium generalized assignment problem and genetic algorithm. Applied Mathematics and Computation, 218(11), 6526–6535. doi:10.1016/j.amc.2011.12.025
   Web of Science ®Google Scholar
• Lu, P., & Tolliver, D. (2013). Multiobjective pavement-preservation decision making with simulated constraint boundary programming. Journal of Transportation Engineering, 139(9), 880–888. doi:10.1061/(ASCE)TE.1943-5436.0000573
   Google Scholar
• Lu, Q., Ullidtz, P., Basheer, I., Ghuzlan, K., & Signore, J. (2009). Calback: Enhancing Caltrans mechanistic-empirical pavement design process with new back-calculation software. Journal of Transportation Engineering, 135(7), 479–488. doi:10.1061/(ASCE)TE.1943-5436.0000010
   Google Scholar
• Marler, R., & Arora, S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395. doi:10.1007/s00158-003-0368-6
   Web of Science ®Google Scholar
• Mathew, B., & Isaac, K. (2014). Optimisation of maintenance strategy for rural road network using genetic algorithm. International Journal of Pavement Engineering, 15(4), 352–360. doi:10.1080/10298436.2013.806807
   Web of Science ®Google Scholar
• Matousek, J., & Gärtner, B. (2006). Understanding and using linear programming. Berlin: Springer.
   Google Scholar
• Meneses, S., & Ferreira, A. (2013). Pavement maintenance programming considering two objectives: Maintenance costs and user costs. International Journal of Pavement Engineering, 14(2), 206–221. doi:10.1080/10298436.2012.727994
   Web of Science ®Google Scholar
• Misra, R., & Das, A. (2003). Identification of homogeneous sections from road data. International Journal of Pavement Engineering, 4(4), 229–233. doi:10.1080/10298430410001672237
   Google Scholar
• Nasser, H., & Chabot, A. (2015). Two-dimensional software for analysing mechanical fields in elastic cracked pavements. In CSC 2015, Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing.
   Google Scholar
• Nasser, H., Piau, J.-M., Chupin, O., & Chabot, A. (2016). M4-5n numerical solution using the mixed fem, validation against the finite difference method. In 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements (pp. 363–369).
   Google Scholar
• Nauss, R. (2003). Solving the generalized assignment problem: An optimizing and heuristic approach. INFORMS Journal on Computing, 15(3), 249–266. Retrieved from https://pubsonline.informs.org/doi/abs/10.1287/ijoc.15.3.249.16075 doi:10.1287/ijoc.15.3.249.16075
   Web of Science ®Google Scholar
• Nesbit, D. M., Sparks, G. A., & Neudorf, R. D. (1993). A semi-markov formulation of the pavement maintenance optimization problem. Canadian Journal of Civil Engineering, 20(3), 436–447. doi:10.1139/l93-058
   Web of Science ®Google Scholar
• Nocedal, J., & Wright, S. (2006). Numerical Optimization (2nd ed.). New York, NY: Springer.
   Google Scholar
• Officials, T. (2008). Mechanistic-empirical pavement design guide: A manual of practice. AASHTO.
   Google Scholar
• Onar, A., Thomas, F., Choubane, B., & Byron, T. (2001). Estimation of rutting models by combining data from different sources. Transportation Engineering, 127, 379–389.
   Google Scholar
• Osman, I. (1995). Heuristics for the generalised assignment problem: Simulated annealing and tabu search approaches. Operations-Research-Spektrum, 17(4), 211–225. doi:10.1007/BF01720977
   Google Scholar
• Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & Torres-Machi, C. (2018). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169–1181. doi:10.1080/15732479.2017.1402064
   Web of Science ®Google Scholar
• Pilson, C., Hudson, W., & Anderson, V. (1999). Multiobjective optimization in pavement management by using genetic algorithms and efficient surfaces. Transportation Research Record: Journal of the Transportation Research Board, 1655(1), 42–48. doi:10.3141/1655-07
   Google Scholar
• Racer, M., & Amini, M. (1994). A robust heuristic for the generalized assignment problem. Annals of Operations Research, 50(1), 487–503. doi:10.1007/BF02085655
   Google Scholar
• Rèche, M. (2003). Effet des travaux d’entretien sur les lois d’évolution des dégradations de chaussées [effects of maintenance interventions on pavement performance models]. Revue Française de Génie Civil, 7(1), 132–132.
   Google Scholar
• Ross, G.T., & Soland, R. M. (1975). A branch and bound based algorithm for the generalized assignment problem. Mathematical Programming, 8, 91–103. doi:10.1007/BF01580430
   Web of Science ®Google Scholar
• Rossi, F., van Beek, P., & Walsh, T. (2006). Handbook of constraint programming. New York, NY: Elsevier.
   Google Scholar
• Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach. Malaysia: Pearson Education Limited.
   Google Scholar
• Saaty, T. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. doi:10.1016/0377-2217(90)90057-I
   Web of Science ®Google Scholar
• Santos, J., Bryce, J., Flintsch, G., & Ferreira, A. (2017). A comprehensive life cycle costs analysis of in-place recycling and conventional pavement construction and maintenance practices. International Journal of Pavement Engineering, 18(8), 727–743. doi:10.1080/10298436.2015.1122190
   Web of Science ®Google Scholar
• Santos, J., Ferreira, A., Flintsch, G., & Cerezo, V. (2018). A multi-objective optimisation approach for sustainable pavement management. Structure and Infrastructure Engineering, 14(7), 854–868. doi:10.1080/15732479.2018.1436571
   Web of Science ®Google Scholar
• Sarker, R., Runarsson, T., & Newton, C. (2001a). A constrained multiple raw materials manufacturing batch sizing problem. International Transactions in Operational Research, 8(2), 121–138. doi:10.1111/1475-3995.00254
   Google Scholar
• Sarker, R., Runarsson, T., & Newton, C. (2001b). Genetic algorithms for solving a class of constrained nonlinear integer programs. International Transactions in Operational Research, 8(1), 61–74. doi:10.1111/1475-3995.00006
   Google Scholar
• Savelsbergh, M. (1997). A branch-and-price algorithm for the generalized assignment problem. Operations Research, 45(6), 831–841. Retrieved from doi:10.1287/opre.45.6.831
   Web of Science ®Google Scholar
• Schwefel, H.-P. (2000). Advantages (and disadvantages) of evolutionary computation over other approaches. Evolutionary Computation, 1, 20–22.
   Google Scholar
• Senga Kiessé, T., Lorino, T., & Khraibani, H. (2014). Discrete nonparametric kernel and parametric methods for the modeling of pavement deterioration. Communications in Statistics - Theory and Methods, 43(6, 1164–1178. doi:10.1080/03610926.2012.670355
   Web of Science ®Google Scholar
• Shahin, M. (1997). PAVER Asphalt Distress Manual. Champaign, IL: Construction Engineering Research Lab (Army).
   Google Scholar
• Martello, S., & Toth, P. (1981). An algorithm for the generalized assignment problems. In J.P. Brans (Ed.), Operational research (pp. 589–603). Amsterdam: North-Holland.
   Google Scholar
• Martello, S., & Toth, P. (1990). Knapsack problems: Algorithms and computer implementations. New York: Wiley.
   Google Scholar
• Stallman, R., & Sussman, G. (1977). Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9(2), 135–1996. doi:10.1016/0004-3702(77)90029-7
   Web of Science ®Google Scholar
• Tsang, E. (1993). Foundations of constraint satisfaction. London: Academic Press.
   Google Scholar
• Van Hoeve, W. (2001). The all different constraint: A survey (pp. 1–42). Cornell University.
   Google Scholar
• Williamson, D., & Shmoys, D. (2011). The design of approximation algorithms (1st ed.). New York, NY: Cambridge University Press.
   Google Scholar
• Yagiura, M., Ibaraki, T., & Glover, F. (2001). An effective metaheuristic algorithm for the generalized assignment problem. In 2001 IEEE International Conference on Systems, Man, and Cybernetics (Vol. 2001, 242–250).
   Google Scholar
• Yagiura, M., Ibaraki, T., & Glover, F. (2004). An ejection chain approach for the generalized assignment problem. INFORMS Journal on Computing, 16(2), 133–151. doi:10.1287/ijoc.1030.0036
   Web of Science ®Google Scholar
• Yagiura, M., Yamaguchi, T., & Ibaraki, T. (1998). A variable depth search algorithm with branching search for the generalized assignment problem. Optimization Methods and Software, 10(2), 419–441. doi:10.1080/10556789808805722
   Web of Science ®Google Scholar
• Yagiura, M., Yamaguchi, T., & Ibaraki, T. (1999). A variable depth search algorithm for the generalized assignment problem. In Meta-heuristics (pp. 459–471). Boston: Springer.
   Google Scholar
• Yu, B., Gu, X., Ni, F., & Guo, R. (2015). Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment. Transportation Research Part D: Transport and Environment, 41, 64–74. doi:10.1016/j.trd.2015.09.016
   Web of Science ®Google Scholar
• Yuan, X., & Pandey, M. (2009). A nonlinear mixed-effects model for degradation data obtained from in-service inspections. Reliability Engineering and System Safety, 94(2), 509–519. doi:10.1016/j.ress.2008.06.013
   Web of Science ®Google Scholar