Abstract
Precast concrete component utilization for building projects has been considered to become the leading trend in construction industry around the world. The logistics costs for precast concrete elements have usually accounted for a large proportion in whole costs executing projects. According to previous studies of logistics cost optimization that have been focused on the costs of transportation and warehouse, however, other necessary costs have not been examined carefully as costs of purchase, depot, load and unload, installation. Therefore, this study provides the comprehensive model of logistics expenditures for precast concrete structures using the Activity-based costing (ABC) method. Also, this study develops the ALO algorithm by hybriding with other algorithms as opposition-based learning, mutation and crossover strategy to optimize costs based on ABC. By evaluating and comparing with previous study applying the genetic algorithm GA, the particle swarm optimization (PSO), the gray wolf algorithm (GWO) and the dragonfly-particle swarm optimization (DA-PSO) the conclusion has generated the superior results in terms of convergence speed, the high degree of accuracy and reducing the logistic cost.
Published Version
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