This research addresses the problem of assembly scheduling in crane-assisted precast construction while considering issues of building layout interference and optimal crane lifting. Traditionally, assembly scheduling and lifting path planning are treated as two separate issues due to their distinct natures. The current work introduces an approach that combines them for precast construction planning to achieve a comprehensive and cost-effective solution. A BIM4D-based Intelligent Assembly Scheduler (BIAS) is designed in conjunction with the Computer-Aided Lifting Planner developed at Nanyang Technological University, Singapore. BIM4D is the Building Information Modeling (BIM) with the time dimension (i.e., scheduling information). Our scheduler takes an inbuilt timeframe for selected precast elements from BIM4D as input and outputs the micro-schedule in terms of the assembly sequence for these precast elements. This problem is solved using multi-objective optimization. Given a group of precast elements and their BIM4D timeframe, the micro-scheduling is determined based on (1) the relative importance of the elements’ physical properties, (2) the interference (neighbouring relation) among the elements’ positions, and (3) collision-free lifting paths of the elements. A Multi-level Elitist Genetic Algorithm (MEGA) is proposed to determine the optimal sequence taking into consideration of both assembling and lifting for the elements. A case study is performed with the BIM4D data of a residential building. The results of the case study demonstrate BIAS's efficiency and effectiveness for BIM4D based construction scheduling.

本研究解决了起重机辅助预制施工中的装配调度问题，同时考虑了建筑布局干扰和最佳起重机吊装问题。传统上，由于其不同的性质，装配调度和提升路径规划被视为两个独立的问题。当前的工作引入了一种方法，将它们结合起来进行预制施工规划，以实现全面且具有成本效益的解决方案。基于 BIM4D 的智能装配调度器 (BIAS) 是与新加坡南洋理工大学开发的计算机辅助提升规划器一起设计的。BIM4D是具有时间维度（即调度信息）的建筑信息模型（BIM）。我们的调度程序将来自 BIM4D 的选定预制构件的内置时间范围作为输入，并根据这些预制构件的装配顺序输出微调度。这个问题是使用多目标优化解决的。给定一组预制构件及其 BIM4D 时间范围，微调度的确定基于 (1) 构件物理属性的相对重要性，(2) 构件位置之间的干扰（相邻关系），以及 (3) ) 元素的无碰撞提升路径。提出了一种多级精英遗传算法（MEGA）来确定最佳序列，同时考虑到元素的组装和提升。使用住宅建筑的 BIM4D 数据进行案例研究。案例研究的结果表明 BIAS'