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Multi-objective Optimization for Location/Allocation Planning of Networked Facilities Using Genetic Algorithm

利用遗传算法进行网络设施选址/分配规划的多目标优化

基本信息

批准号：
08680459
负责人：
SHIMIZU Yoshiaki
金额：
$ 1.22万
依托单位：
TOYOHASHI UNIVERSITY OF TECHNOLOGY (1997)Kyoto University (1996)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1996
资助国家：
日本
起止时间：
1996 至 1997
项目状态：
已结题

项目摘要

Taking importance of developing flexible optimization methods managing conflict resolution in mind, we have concerned with site location problems under multi-objectives. As known from the term NIMBY (Not In My Back Yard), the site location problem of hazardous wastes is an eligible case study for such a situation, that is to say, associated with all human, environmental and economic concerns.First backgrounds of the site-location problems have been investigated broadly, and presented some kinds of typical formulation with a few examples. Then we have formulated the problem generally as multi-objective mixed-integer programs (MOMIP). For this, we have proposed an intelligence supported approach incorporating a hybrid use of genetic algorithm (GA) with mathematical program, and neural networks (NN) for value function modeling. To improve the performance of the mathematical program referred to the linear programming presently, at the stage of program implementation, we applied the progressive linear program developed by the author.Furthermore, concerned with the solution of multi-objective GA particularly, we have introduced a new genetic operation named repair operation to improve performance. From practical points of view, we also considered it very important to provide an approach reflecting certain qualitative aspects imbedded in real-world problem-solving. Concerning with this, we have shown the present approach can deal with such like situation very suitably.By taking an example in waste disposal site planning problem, which was formulated as a mixed-integer linear program (MILP), we have examined the effectiveness of proposed approach from every aspect concerned here numerically.

考虑到开发灵活的优化方法管理冲突解决的重要性，我们关注多目标下的选址问题。从“邻避”（Not In My Back Yard）一词可以看出，危险废物选址问题是一个很好的案例研究，也就是说，它涉及到人类、环境和经济的所有方面。然后，我们制定的问题一般为多目标混合整数规划（MOMIP）。为此，我们提出了一种智能支持的方法，将混合使用的遗传算法（GA）与数学规划，和神经网络（NN）的价值函数建模。为了提高目前以线性规划为主的数学规划的性能，在程序实现阶段，采用了作者提出的渐进式线性规划，并针对多目标遗传算法的求解，引入了一种新的遗传操作--修复操作，以提高性能。从实践的角度来看，我们还认为提供一种反映嵌入在现实世界问题解决中的某些定性方面的方法非常重要。针对这一问题，我们证明了本文方法可以很好地处理这类情况，并以垃圾处理场规划问题为例，从数值上检验了本文方法的有效性。