Mountain Gazelle Optimization algorithm for efficient rebar cutting: balancing material cost and waste in construction projects

Rebar is one of the primary materials in the construction industry, contributing significantly to the total material cost of projects. To meet structural design requirements, standard rebar bars must be cut into specific lengths, which inevitably results in steel waste and is a major source of material wastage in construction. This study proposes the application of a mordern optimization algorithm, the Mountain Gazelle Optimization (MGO), which is part of the swarm intelligence algorithms, to the stage of planning optimal cutting schemes for various rebar layout options. The goal is to optimize the total cost of raw materials and reduce material waste, providing an optimal solution for decision-making and contributing to the sustainable development of the industry. To evaluate the effectiveness of the MGO algorithm, this study used the results of previous research as benchmarks and applied them to real-world project rebar requirements. The results indicate that the proposed algorithm outperforms existing algorithms, affirming the competitive advantage of MGO in solving the rebar cutting optimization problem