## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Research**} & \multicolumn{2}{c}{**Storage**} & \multirow{2}{*}{**Routing**} & \multirow{2}{*}{**Solution Simultaneously**} & \multirow{2}{*}{**Previous Storage**} & \multirow{2}{*}{**Stored in Multiple**} & \multirow{2}{*}{**Warehouse**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Type of**} \\  & & & & **or independently** & & **Assignment** & **Locations** & **Layout** & **Considered** & **Solution** \\ \hline
[16] & & & X & n/a & Yes & No & Conventional & n/a & Optimal \\
[23] & & X & n/a & Yes & No & Conventional & n/a & Optimal \\
[22] & & X & n/a & Yes & No & Conventional & n/a & Optimal \\
[31] & X & X & Independently & Yes & No & Conventional & Demand & Heuristic \\
[26] & X & X & Simultaneously & No & Yes & General & Demand & Tabu search \\
[18] & X & X & Independently & Yes & No & Conventional & Demand & Heuristic \\  & & & & & & & Simulation \\
[20] & & X & n/a & Yes & No & Conventional & n/a & Dynamic \\
[24] & X & X & Independently & Yes & No & Conventional & Demand and & Heuristic \\
[37] & X & X & Independently & Yes & No & Conventional & Demand & Analytical models \\  & & & & & & Simulation & Simulation \\
[17] & X & X & Independently & Yes & No & Conventional & Demand & Heuristic \\
[38] & X & X & Independently & Yes & No & Conventional & Demand & Heuristic \\
[27] & X & X & Independently & Yes & No & Conventional & Demand & Dynamic \\
[39] & X & X & Independently & Yes & No & Conventional & Demand & programming \\  & & & & & & weight, fragility, & Exact algorithms \\
[35] & X & X & Independently & Yes & No & Conventional & capacity and & \(\Delta\)NS \\  & & & & & & category constraints & & \\
[22] & X & X & Independently & Yes & No & Conventional & Demand and & Dynamic \\  & & & & & & weight of product & programming \\ This work & X & X & Simultaneously & No & No & General & Demand and & Optimal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research summary. Consequently, the order picking problem has been investigated plenty; nevertheless, in previous research, the proposed models were based on the fulfillment of demands, putting aside other factors, such as the weights of the products, which represent an important criterion at the moment of establishing the routes, as presented in [22; 34; 35; 40], since keeping the product in good condition is vital for the satisfaction of customers, mainly in warehouses where fragile products are handled. In the same way, contemplating more than one factor in the picking routing will improve the storage location assignment of the products and with it the order picking, as presented in the case study of this work. Although some works [5; 41] state that the most used method to solve these problems is heuristics, in the literature of order picking, as shown in the Table 1, a mathematical model has not been proposed so far through simultaneously determining storage location assignment, unique, and picker routing (SLAUPR), considering precedence constraints based on the weight of the products and only location for each product in a general warehouse, so this research contributes to the literature because a mathematical model under these characteristics is proposed.