\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & **Year** & **Objectives** & **Objective** & **Metaheuristic Method** & **Exact Solution,** & **Multi-** & **Multi-** & **Time** & **Case** \\  & & & & & **Software** & **Suppliers** & **Products** & **Window** & **Study** \\ \hline [PERSON] et al. & 2015 (14) & Cost & Multi- & Hybrid genetic algorithm & CPLEX & ✓ & ✓ & - & - \\ \hline C\(\check{\text{c}}\)[PERSON] et al. & 2015 (16) & Cost & Single & Branch-and-price algorithm & CPLEX & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. & 2016 (17) & Road traffic flow & Single & Water flow algorithm (WFA) & CPLEX & ✓ & ✓ & - & ✓ \\ \hline [PERSON] et al. & 2018 (18) & Time, Cost & Multi- & Multi-objective specialist competitive algorithm (MOICA) & MATLAB & ✓ & ✓ & ✓ & - \\ \hline [PERSON] and [PERSON] & 2018 (19) & Cost & Multi- & Hybrid of the genetic algorithm and particle swarm optimization (HGP) & CPLEX & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2018 (20) & Cost & Multi- & Genetic algorithm (GA) & CPLEX/MATLAB & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2018 (21) & Cost & Multi- & Hybrid genetic algorithm-reduced variable neighborhood search algorithm (HGARVNS) & GAMS/MATLAB & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2018 (22) & Time & Single & Hybrid Lagrangian Metaheuristic & CPLEX & ✓ & ✓ & - & - \\ \hline [PERSON] et al. & 2018 (23) & Cost & Single & - & CPLEX & ✓ & ✓ & - & - \\ \hline [PERSON] et al. & 2018 (24) & Time, Fuel consumption & Multi- & Archived multi-objective (AMOSA) & MATLAB & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2018 (25) & Cost & Multi- & Column generation algorithm & CPLEX & ✓ & ✓ & - & - \\ \hline [PERSON] et al. & 2018 (26) & Cost & Multi- & Rounding algorithm (RA), Single-period algorithm (SPA) & CPLEX & ✓ & ✓ & - & - \\ \hline [PERSON] et al. & 2019 (27) & Cost & Multi- & Novel genetic algorithm & & & & \\  & & & Modified with & & & & & \\  & & & (GM-MVNS), Artificial bee colony (ABC) & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline [PERSON] et al. & 2019 (28) & Time & Single & Cross-Dock Heuristic & GAMS & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2019 (29) & Cost & Multi- & Adaptive large neighbourhood search (ALNS) & Python, Gurobi Optimizer & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. & 2019 (30) & Cost & Single & - & Gurobi Optimizer & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] & 2019 (31) & Cost & Multi- & Novel delayed start parallel evolutionary algorithm & GAMS/CPLEX & ✓ & ✓ & - & - \\ \hline [PERSON] et al. & 2019 (32) & Cost, Weight & Multi- & - & GAMS/CPLEX & ✓ & ✓ & ✓ & - \\ \hline [PERSON] et al. & 2019 (33) & Cost & Multi- & Multi-objective water-flow algorithm (MOWFA) & MATLAB & ✓ & ✓ & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reviewed Articles on cross-docks. For this work. Despite the strong desire to research cross-docks, provide a new concept of modeling, and design different algorithms, this field still faces many shortcomings, which are mainly related to the extension of existing models to approach real-world problems. As a result, the main motivation of this article is to present a model that reduces the time of product transfer and increases the quality of the system's performance, and also causes integration in the entire supply chain system. As can be seen, most of the researches have considered the problem by assuming the accuracy of the input parameters. Few studies have considered warehouse entry and service rates to be probable. In all these researches, the probability distribution of entering the warehouse and the probability distribution of the service rate in the warehouse follow the exponential or normal process. Therefore, it can be claimed that none of the researches have examined and modeled the warehouse service rate and warehouse entry rate with a general probability distribution. Therefore, this research intends to model and optimize cross-docks by considering the entry and service rates with the distribution of general probability in warehouses.