An artificial neural network for resource leveling problems
An artificial neural network for resource leveling problems
复制标题
用于解决资源均衡问题的人工神经网络
DOI：
10.1017/s0890060498123053
复制
发表时间：
1998
期刊：
影响因子：
--
通讯作者：
Tanit Tongthong
中科院分区：
文献类型：
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作者：
N. Kartam;Tanit Tongthong
介绍了一种利用人工神经网络解决资源均衡问题的新方法。本文介绍了一种新的高效和强大的方法，这是独特的传统的启发式和优化资源均衡技术所利用的。资源均衡人工神经网络（RLANN）充分利用了Hopfield网络和基于竞争的人工神经网络的优点。RLANN的通用格式适用于用关键路径法（CPM）生成的工程项目网络，其形式可以是箭头图，也可以是优先图。该方案包括两层，输入和竞争层，人工节点矩阵完全连接的链接。RLANN内部的解决机制基于运动方程和控制日常资源使用水平的竞争策略。当运动方程支配活动在时间表限制内移动时，竞争过程为活动找到最佳位置以实现最佳结果。该方法简单，可以在个人计算机或并行处理设备上实现。所产生的解决方案是可比的，或更好的，那些产生的其他启发式或优化技术。本文介绍了RLANN的发展，它的解决机制，以及它在施工资源均衡问题中的应用。并将该方法与其他传统方法的结果进行了比较。结论突出了该模型对其他土木工程问题的适用性。
A new methodology for solving resource leveling problems is introduced using Artificial Neural Networks (ANN). This paper describes a new efficient and robust approach which is unique to those utilized by traditional heuristic and optimization resource leveling techniques. The Resource Leveling Artificial Neural Network (RLANN) exploits advantages of both Hopfield networks and competition-based artificial neural networks. The universal scheme of the RLANN is applicable to construction project networks produced with Critical Path Method (CPM), in forms of either arrow or precedence diagrams. The scheme is comprised of two layers, an input and a competition layer, of artificial node matrices fully connected by links. Solving mechanisms inside the RLANN are based on an equation of motion and a competition strategy that control the level of daily resource usage. While the equation of motion governs activities to be shifted within schedule constraints, the competition process finds the best positions for the activities to achieve optimum results. The approach is simple and can be implemented on either a personal computer or a parallel processing device. The solutions produced are comparable to, or better than, those generated by other heuristic or optimization techniques. This paper describes the development of the RLANN, its solving mechanisms, and its uses in construction resource leveling problems. The comparison of the result of the RLANN to those of other traditional techniques is also included. The conclusions highlight the applicability of this model to other civil engineering problems.