Utilizing Artificial Intelligence to Solve Construction Site Layout Planning Problems

Abstract

The facilities have conflicting purposes in the available space on the construction site, increasing the transportation flow of facilities, which is the cause of the increase in project construction costs. The research is applied to solve the construction site planning (CSLP) problem that depends on the quadratic assignment model (QAP). The QAP is intended to determine the best way to allocate facilities to locations so that each facility is mapped to only one location. In contrast, every location is mapped to an exact facility to minimize product flow and distance costs. Previously, there were many ways to solve the problem by meta-heuristic method such as (GA), (ACO). This paper proposes a new hybrid algorithm (M-ALOB), to solve the problem (QAP), the goal of the problem is to solve the ground layout problem built by the quadratic assignment problem (QAP). Compare the new hybrid algorithm with previously studied algorithms (GA) and the original algorithm (ALO), giving assessment of advantages and disadvantages when applied to solving discrete problems. (M-ALOB) for stable running results, tested for stability after 30 optimal runs. The optimal outcome in Case Study 1 (M -ALOB) min=688.4 is lower than (GA) is 19.7%. And Case Study 2 (M-ALOB) min = 171,970.19 lower (GA) is 2%. The results showed that (M-ALOB) found the optimal value smaller than the algorithms (GA), (ALO). Solve the problem of arranging facilities on the ground (CLSP) to minimize the workflow, save operating costs of construction projects.

Keywords

• ant lion optimizer
• quadratic assignment problem
• construction site layout planning
• site layout
• particle swarm optimization algorithm selection