# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 4 Methodology
### Step II: Building Scenarios  
The goal of this research was to find the optimal number and location of DCs to support the demand of retailers. In order to do this, eight possible scenarios were devised and summarized in Table 4. Scenario 1 assumes the existing distribution network. In addition, we propose three different approaches to select the location of distribution center. First, closest facility analysis was utilized to all eight available facilities in scenario 2. Second, in scenario 3, the number of facilities required to cover all demand points is minimized, which can be represented by n. Finally, we have utilized the approach for maximizing coverage which locates facilities where all demand points can be reached within a specified response time. By increasing the number of DCs which is secured from scenario 3 (n), we can design scenarios 4 to 8 represented by n + 1, n + 2, n + 3, n + 4 and n + 5, respectively. Even if the minimum number of DCs is equal to two, the maximum number of scenario using a maximize coverage approach should be equal to 7 (n + 5). Therefore, we can have at most eight scenarios. Each scenario will be explained in further detail.  
Currently, each wholesaler delivers products to the existing DC. Then, retailers should pick up their products based on demand and deliver them to the retail market (see Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Analysis Method** & **Lead Time (min)** & **Number of DCs** \\ \hline Scenario 1 & Existing DC & - & 1 \\ Scenario 2 & Closest facility & - & 8 \\ Scenario 3 & Minimize facility & 180 & n \\ Scenario 4 & Maximize coverage & 180 & n + 1 \\ Scenario 5 & Maximize coverage & 180 & n + 2 \\ Scenario 6 & Maximize coverage & 180 & n + 3 \\ Scenario 7 & Maximize coverage & 180 & n + 4 \\ Scenario 8 & Maximize coverage & 180 & n + 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of building scenario.  
Seenarios 2-8 were devised by assuming multi-location of DCs (see Figure 6) by locating new DCs and designing new transportation routes to reduce travel time and distance.  
The details of the assumption to build scenarios 2-8 are described below.  
* Scenario 2: Using all candidate DCs  
The analysis assumed that all candidate DC should be addressed as a new facility. First, the closest facility in ArcGIS was used to assign 141 retailers to each DC. The delivery quantity to each retailer, delivery time window, and truck capacity were considered. Second, each DC delivered fresh fruits and vegetables to a group of retailers by using VRPTW analysis. Finally, LRP was used to find the total travel time and distance from wholesaler to DC.  
* Scenario 3: From the result of minimizing the facility problem  
In this scenario, we first found the minimum number of DCs required to cover all retailers using the location-allocation analysis. Then, retailers were assigned to possible candidate DCs within the impedance cutoff. Finally, the routes and schedules for distributing products from DCs were developed.  
Figure 5: The transport network of scenario 1 (AS IS).  
Figure 6: The transport network of scenarios 2–8 (TO BE).  
* Scenario 4 to Scenario 8: Using the maximize coverage problem  
In Scenarios 4-8, we set the number of candidate DCs first. By increasing the number of candidate DCs from scenario 3 (minimizing the facility problem) to scenario 8 (the maximum number of DCs), we could extend the scenarios. Based on constraints in Table 4, maximizing coverage was used to choose the best set of candidate DCs, which could cover the maximum number of retailers within the impedance cutoff. Finally, the routes for delivery from selected DCs to 141 retailers were designed.