摘要：AbstractSustainable highway infrastructure design requires a good balance between environment and cost. The available computer-based automated highway alignment optimization algorithms minimize the total highway alignment cost towards obtaining the optimized alignment. These are primarily single-objective optimization approaches with limited capability in considering various cost components and factors that may be dissimilar or conflicting in nature. In these approaches, the impacted area of environmentally preserved land is transformed to monetary value and included in the total highway alignment cost. Also, they have limited capability in yielding a set of alternatives with different levels of trade-off between the total highway alignment cost and impacted area of environmentally preserved land. This leads to the need for developing a model to estimate the impacted area of environmentally preserved land in terms of non-monetary values and use concurrently with total highway alignment cost providing a perspective comparison of highway alignment alternatives. A multiobjective analysis is presented in this paper to consider the impacted area of environmentally preserved land information expressed in non-monetary unit and total highway alignment cost expressed in monetary unit. In this analysis, a genetic algorithm (GA) based multiobjective optimization technique is adopted. The paper describes a special algorithm developed to estimate the impacted area of environmentally preserved land from a Geographic Information System (GIS) based study area map and simultaneous minimization of impacted area of environmentally preserved land and highway alignment cost. A Pareto-optimal front is used to graphically represent the trade-off values of highway alignment cost and impacted area of environmentally preserved land. Finally, the developed methodology is applied to a study area and results are compared with the available single objective highway alignment optimization model output.
关键词：multiobjective analysis;environmental sustainabiity;highway alignment optimization;genetic algorithms;geographic information system (GIS)
