## 2 Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Studies** & **Facility Planning Methods** & **Objective (s)** & \
\begin{tabular}{c} **Uncertain Factors** \\ **Considered** \\ \end{tabular} &
\begin{tabular}{c} **Deciding the** \\ **Optimal Capacity** \\ \end{tabular} \\ \hline
[29] & AHP & NA & No & No \\
[30] & AHP & NRA & No & No \\
[31] & Stakeholder network & Provide high-value recycled & No & No \\
[33] & MILP & Minimize the total costs & No & No \\  & & Maximize the recycling rate; & & \\  & & Maximize the profits of & & \\
[21] & MILP & recycling firms; & No & Yes \\  & & Minimize the costs of & & \\  & & contractors for disposing of & & \\  & & CDW & & \\
[40] & Swarm algorithm & Minimize environmental & & \\
[40] & Swarm algorithm & impact; & No & Yes \\  & & Minimize total costs & Demand for goods & No \\
[39] & Swarm algorithm & Minimize total costs & No & No \\
[41] & GA-PSO algorithm & Minimize environmental & All input data & No \\
[42] & LCA & impact & Construction waste & No \\ [PERSON] and [PERSON] (2020) [26] & RO & Minimize total costs & generation & No \\  & & Construction waste & & \\
[43] & RO & Minimize total costs & generation; & No \\  & & & Transportation cost & \\  & & Construction waste & \\
[25] & RO & Minimize total costs & generation; & No \\  & & & Demand for recycled & No \\  & & & construction material & \\
[44] & RO & Minimize total costs & Construction waste & No \\
[45] & RO & Minimize total costs & Generation & \\  & & Construction waste & \\
[46] & SO & Maximize the expected profit & generation; & No \\  & & Recycling rate in & recycling facility & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the existing literature. warehouses [48], electronic equipment [49], and distribution centers [50]. In the topic related to construction waste facility planning, [PERSON] et al. [46] developed a two-stage stochastic model for a reverse logistics network tailored to cope with the uncertainty of the supply and quality of the materials, aiming to achieve maximum profits by selling recycled materials. Primarily, the operational status of recycling centers was denoted by binary variables; however, these variables did not capture the optimal area or capacity of these facilities. In summary, existing methods were unable to optimize the capacity and location of recycling centers in uncertain situations. Evaluation tools such as AHP and stakeholder network plans were relatively simple to operate, but due to their reliance on less quantitative data, they were not capable of handling uncertainty about construction waste or optimizing the capacity of recycling centers. MILP and heuristic algorithms solved multi-objective problems in construction management, but they considered uncertainty in a rather limited manner, focusing primarily on the demand for recycled material. RO methods protected decision makers against stochastic uncertainty by considering the worst possible scenarios. This approach often required substantial investments to achieve the objective function. Additionally, the objectives of optimization focused on minimizing total costs or environmental impact; however, the impacts of government interventions and understanding the strategies remained a noticeable gap. The government, whose objective is to maximize the waste recycling rate, plays an important role in construction waste management [17]. Thus, from a governmental perspective, particularly on its strategies and impacts, they remain relatively underexplored.