Volume 16, Issue 3 (7-2026)
IJOCE 2026, 16(3): 427-440 |
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Alipour A, Sedaghat Shayegan D, Amirkardoust A A. APPLICATION OF THE MOUTH BROODING FISH ALGORITHM FOR CONSTRUCTION SITE LAYOUT OPTIMIZATION IN INDUSTRIAL PROJECTS. IJOCE 2026; 16 (3) :427-440
URL: http://ijoce.iust.ac.ir/article-1-684-en.html
URL: http://ijoce.iust.ac.ir/article-1-684-en.html
1- Department of civil engineering, Ro.C., Islamic Azad University, Roudehen, Iran, Tehran, Iran
Abstract: (58 Views)
Deciding where to place temporary facilities on a construction site is one of the more consequential choices a project team makes, since that arrangement shapes how materials and people move around the site, how accessible different zones are, and how smoothly logistics run overall. This paper examines whether the Mouth Brooding Fish (MBF) algorithm can handle a standard construction site layout planning problem. The goal is to minimize the total distance that must be traveled between facilities that interact with each other. A well-known benchmark involving eleven facilities and eleven predetermined locations is solved with MBF and compared against Genetic Algorithm (GA), Ant Colony Optimization (ACO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO). Fifty independent runs are performed, each with 200 iterations. The best solution obtained by MBF yields a total travel distance of 12,538, while the mean, worst, and standard deviation values are 12,608, 12,888, and 96, respectively. These figures place MBF in a competitive position relative to the other methods on this particular benchmark. The study does not claim direct field validation for oil and gas projects; rather, it demonstrates that the optimization framework works on a benchmark whose structure is relevant to industrial construction logistics. Further work that incorporates project-specific constraints and actual field data is recommended.
Keywords: Construction site layout planning, Mouth Brooding Fish algorithm, Metaheuristic optimization, Industrial construction, Oil and gas projects, Facility arrangement optimization
Type of Study: Research |
Subject:
Optimal design
Received: 2026/06/28 | Accepted: 2026/08/28 | Published: 2026/08/29
Received: 2026/06/28 | Accepted: 2026/08/28 | Published: 2026/08/29
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