王家1,王洋1,邓铁军1,2,刘可心1.施工现场设施布局优化问题的新型启发式算法[J].湖南大学学报:自然科学版,2020,(9):128~136
施工现场设施布局优化问题的新型启发式算法
A Novel Meta-heuristic Algorithm forConstruction Site Facilities Layout Optimization
  
DOI:
中文关键词:  施工现场设施布局优化  启发式算法  全局优化算法  过渡马尔可夫链蒙特卡罗  遗传算法
英文关键词:construction site facilities layout optimization  meta-heuristic algorithm  global optimization algorithm  transitional Markov Chain Monte Carlo  genetic algorithm
基金项目:
作者单位
王家1,王洋1,邓铁军1,2,刘可心1 (1. 湖南大学 土木工程学院湖南 长沙 410082 2. 湖南大学设计研究院有限公司湖南 长沙 410082) 
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中文摘要:
      施工现场设施布局的合理性直接关系到项目成本等目标的实现.针对涉及设施较多的施工现场布局优化问题,首先将该离散变量优化问题转换为高维空间的随机抽样问题,进而利用过渡马尔可夫链蒙特卡罗方法的思想,提出一种高效的全局优化启发式算法.与针对连续型高维概率密度分布函数进行随机取样的过渡马尔可夫链蒙特卡罗方法相比,本文提出的启发式算法的框架基础需从概率密度分布函数转变为概率分布函数,进而需在马尔可夫链状态点的产生方法上进行修正,以适应离散型变量优化问题的不同特性.通过实例验证,与目前应用较广的遗传算法相比,本文提出的新型启发式算法在全局最优解的获取稳定性上有较大改进.
英文摘要:
      Layout of construction site facilities has great impact on the project objectives, such as project cost. In this paper, the problem of the construction site facilities layout with many facilities, which is an optimization problem with discrete variables, is considered. Firstly, the problem is transformed to a high-dimensional random sampling problem, and then addressed by a novel meta-heuristic algorithm based on transitional Markov chain Monte Carlo (TMCMC). Different from original TMCMC developed for optimization problems with continuous variables, the proposed meta-heuristic algorithm is based on introducing a sequence of probability distribution functions instead of probability density functions, and thus the method for iteratively generating states of Markov chains is modified in the proposed algorithm, in order to meet the specifics of optimization problems with discrete variables. As shown in an illustrative example, compared with the widely used genetic algorithm, the proposed meta-heuristic algorithm can obtain higher improvement in the stability of achieving global optimal solution.
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