Multi-Objective Invasive Weed Optimization &combination of Metaheuristic Algorithms forConstruction Site Layout Planning

Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

ICSAU09_060

تاریخ نمایه سازی: 24 فروردین 1403

Abstract:

Safety importance on construction site layout plan is an essential requirement to improveconstruction project management. In previous studies the safety objective function isconsidered without risk factors analysis. Metaheuristics are widely used to solveconstruction site layout problems (CSLP). Invasive Weed Optimization (IWO) isemployed as multi- objective optimization method to design and optimize two safetyobjective functions and total cost. Safety objective functions (due to potential risks arisingfrom hazardous sources and interaction flows) connecting temporary facilities byconsidering total cost reduction.A case study is presented to find out accuracy of theproposed model. Finally, the performance of four metaheuristic algorithms calledInvasive Weed Optimization (IWO),Firefly Algorithm (FA) and Ant ColonyOptimization (ACO) previously studied by researchers are compared in terms of theireffectiveness in resolving a practical construction site layout problem. In order to takeadvantage of a more optimal response, a combination of firefly and weed algorithm wasalso investigated. Results show that the combination of FA and IWO algorithms worksbetter than ACO, FA and IWO algorithms separately.

Keywords:

Invasive Weed Optimization algorithm , Firefly algorithm , construction site layoutplanning (CSLP) , Multi-objective Optimization model

Authors

S. S. Shahebrahimi

Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Tehran, Iran

a LORK

Department of Civil Engineering, Safadasht Branch, Islamic Azad University, Tehran, Iran

D Sedaghat Shayegan

Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Tehran, Iran

A.Amir Kardoust

Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Tehran, Iran