TY  - SER
AU  - Akkarapol Tangphaisankun
AU  - Pannapa Herabat,
AU  - Pannapa Herabat,
AU  - Ogunlana,Stephen O.
AU  - Hanaoka,Shinya
ED  - Royal Thai Government,
TI  - Multi-objective optimization model using constraint-based genetic algorithms for Thailand pavement management
T2  - Thesis
PY  - 2004///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Genetic algorithms
KW  - Pavements
KW  - Management
KW  - Thailand
KW  - Decision-making
N1  - 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
N2  - 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
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B08750
ER  -