## 3 Methods
### Best-Worst Method (BWM)  
A new method [27], called best-worst method (BWM) is a multi-criteria decision-making method that uses two vectors of pairwise comparisons to determine the weights of criteria. BWM has been significantly ranked in the field of MCDM as a model providing reliable and relevant results for optimal decision-making [28]. It compares to the existing MCDM methods, as described below: (1) it requires less comparison data; and (2) it leads to more consistent comparisons, which means that it produces more reliable results [27]. First, the best (e.g., most desirable, most important), and the worst (e.g., least desirable, \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Function** & **Description** \\ \hline
1 & Receiving & This is the process of unloading an incoming truck, identifying, registering, and sometimes repacking. \\ \hline
2 & Put away & This happens when goods are moved from the unloading dock to the storage area. \\ \hline
3 & Storage, in and out & Activities at the warehouse affect the goods in storage: to count and verify \\ \hline
4 & Replenishment & If inventory levels of the pick storage drop to certain amounts, it is replenished with \\ \hline
5 & Pick and pack & Upon an order for a needed item in storage, either full pallets are picked from the bulk \\ \hline
6 & Shipping & The picked items are packed, consolidated, and staged for shipping. \\ \hline
7 & Cross-dock & Some goods do not make their way into stocks but, upon receipt, are transferred to the \\ \hline
8 & Value-added service & There is also recognition for the value added at distribution centers. Such value can be the labeling of goods to the specific customer or the country of destination. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Picking and materials handling in the warehouse facility [25]. least important) criteria are identified by the decision-maker, after which the best criterion is compared to the other criteria, and the other criteria to the worst criterion. In addition, a nonlinear minimax model is then used to identify the weights so that the maximum absolute difference between the weight ratios and their corresponding comparisons is minimized [16]. Furthermore, the grey values represent the information as an interval for which the linguistic terms for pairwise comparison of RPN elements is shown in Table 2. The steps of BMW are used to derive the weights of the criteria, which are briefly described as follows [19]:  
Step 1. Determine a set of RPN elements  
The expert identifies \(n\) RPN elements (e.g., frequency and severity) that are used to calculate the RPN values. Step 2. Determine the best and worst RPN elements  
The expert selects the best (i.e., most acceptable, preferred, or vital) and worst (i.e., least acceptable, preferred, or vital) RPN elements from the \(n\) RPN elements determined in Step 1. Step 3. Determine the preference of the best RPN element over the other RPN elements  
The expert assigns the preference of the best RPN element over the other RPN elements on a ranking scale of 1 (least important) to 9 (most important). The resulting best-to-others (BO) vector is:  
\[A_{B}=(a_{B1},a_{B2},\cdots,a_{Bn})\]  
where \(a_{Bj}\) is the preference of the best element B over element \(j\). Clearly, \(a_{BB}=1\). Step 4. Determine the preference of the other RPN elements over the worst RPN element  
The relative importance of the other RPN elements over the worst RPN element is assigned by the expert on a ranking scale of 1-9. The resulting others-to-worst (OW) vector is:  
\[A_{W}=(a_{1W},a_{2W},\cdots,a_{nW})^{T}\]  
where \(a_{jW}\) is the preference of the element j over the worst RPN element w. Clearly, \(a_{WW}=1\). Step 5.