Urban Planning
Bahram Amin zadeh Gohar rizi; saeid tohidi rad; Roshanak Asadi
Volume 5, Issue 19 , August 2016, , Pages 15-26
Abstract
Land-use location planning is one of the most important urbanism issues with different scales. The choice of location planning method is determined by the kind of problem and the importance of the supposed Land-use to be location planned. For example, to choose a location from some limited options, the ...
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Land-use location planning is one of the most important urbanism issues with different scales. The choice of location planning method is determined by the kind of problem and the importance of the supposed Land-use to be location planned. For example, to choose a location from some limited options, the multi-attribute decision making methods should be used; but when there is no initial option for location planning, the multi-objective decision making method should be used. Nowadays, the use of computational techniques for location planning is inevitable. The reason is that a manager's decisions in this field are influenced by various qualitative and quantitative factors, generally in conflict with each other, so that optimization of a factor may lead to the destruction of other factors. Thus, to prevent errors in decision-making, multi-objective decision-making techniques have been considered and used in recent years. There are different methods for solving multi-objective decision making problems which are categorized into two general groups: decomposition methods and direct methods. In the decomposition methods, first, the multi-objective optimization problem turns into a single-objective problem and then the problem is solved. However, in multi-objective optimization methods, the problem is solved in a multi-objective manner. Decomposition methods comprise of the 4 techniques of Weighted sum, Goal programming, Goal Attainment and -Constraint. Each of the 4 techniques tries to simplify the problem and solve it with specific measures. To turn a multi-objective problem into a single-objective one, these methods are forced to lose some of the space decision information. To solve this issue, the problem should be solved several times, which is very time-consuming. Furthermore, each time the problem is solved with these methods, a different answer is obtained. However, direct methods are not faced with this problem and are much faster and more accurate. The purpose of this study is to introduce an efficient technique of direct methods to solve all the multi-objective location planning problems and resolve classic and decomposition methods issues. Therefore, an airport location planning problem for the one of the major cities, based on Non-dominated Sorting Genetic algorithm (NSGA-II) was considered as a case study. Based on indicators such as easy access, noise pollution reduction, visibility, and access to infrastructure, the problem was modeled as a mathematical programming problem with 6 objective functions such as the minimum distance from main roads and highways, maximum distance from industrial factories, minimum distance from power transmission lines, maximum distance from the city and traditional gardens, minimum distance from the main gas transportation pipeline, the maximum distance from residential areas around the city and a certain number of required conditions. Finally, amongst the 200 final solutions of the proposed algorithm, including different front solutions, a solution with 4 points was chosen as the optimum location for the construction of the airport. Because of greater speed and accuracy, as well as providing a decision support system, the Non-dominated Sorting Genetic algorithm (NSGA-II) which is a direct multi-objective location planning problem solving method, can be considered as an appropriate alternative to the Decomposition and other traditional multi-objective location planning methods.
