Construction site layout planning (CSLP) problem is important in construction management. Facilities have conflicting purposes in the available space on the construction site, increasing the inefficient transportation of facilities. This is the main cause leading to the loss of operational productivity and increasing project construction costs. Therefore, the facilities planning and layout to be established in the appropriate locations to find an optimal solution in the available space is a

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... lem to be solved using quadratic assignment problems (QAP) method. In the past, there were several ways to solve the QAP problem using metaheuristic methods such as genetic algorithm (GA),mixed integer programming (M.I.P), and artificial bee colony. However, each method has both advantages and disadvantages. Therefore, this study proposes a new algorithm that combines mutation, crossover, tournament selection (TS), and opposition-based learning with improved ant lion optimization based on ant lion optimizer (ALO) to solve the QAP problem of optimizing facilities layout on the construction site to find the most optimal results in the shortest time. The comparison results in the research paper belowhave shown that the new, improved hybrid algorithm outperformed previous algorithms such as the GA, MIP, and original ALO algorithm.