05023nas|a2200565 i 4500 20260818155857.0 040827s2004 th uzm rtt 00| a1eng d .b11938067 AIT Thesis no.CM-04-21 Akkarapol Tangphaisankun Multi-objective optimization model using constraint-based genetic algorithms for Thailand pavement management Pathum Thani, Thailand : Asian Institute of Technology, 2004 127 leaves Thesis ; no. CM-04-21 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Civil Engineering Thesis (M.Eng.) - Asian Institute of Technology, 2004 In Thailand, maintenance program is planned based on the TPMS Budgeting Module. Proper treatments are recommended based on h·affic volume and road condition and implemented based on the prioritization. Most maintenance treatments are assigned to the heavy h·affic sections and deferred for the light h·affic roads which make the maintenance programs not optimal. Due to the limited budgets, the overall network condition is not well-maintained because most of the roads are defened until their IRI values exceed the acceptable levels. The objective of this study is to develop a multi-objective optimization model to suppo1t the decision making process of DOH to provide the optimal maintenance programs. Various levels of preventive maintenance programs are set based on the best condition and the maximum acceptable level to determine the most suitable preventive maintenance levels. Vehicle operating cost (VOC) minimization is taken into account in a singleobjective optimization, and road network condition maximization is established to be simultaneously considered in the multi-objective optimization. This study selects the flexible pavements in Pathumtani province to be the study area. To accomplish this study, optimization models, both single- and multi-objective, over a multiyear planning period (30 years) are developed by incorporating with the consh·aint-based genetic algorithms to deal with the combinated characteristic of the network-level maintenance planning. Two consh·aints, budget and system preservation, are employed to make the solutions more realistic. Pareto optimality and non-dominated sorting are inh·oduced to handle the multi-objective genetic algorithms model. The results show that the optimal maintenance programs of both single- and multi-objective models typically implement preventive maintenance before their conditions reach the maximum acceptable levels. Genetic algorithms Pavements Management Thailand Decision-making Thailand Pannapa Herabat, Chairperson Pannapa Herabat, Chairperson Ogunlana, Stephen O., Examination Committee Hanaoka, Shinya, Examination committee Royal Thai Government, Scholarship donor Asian Institute of Technology. Thesis ; no. 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