## 3 Results  
### Results and Assessment of the Proposed Heuristic  
This section analyses the results obtained by applying the heuristic, for Lisbon and the Tagus Valley, for one working day. The company databases were used to collect the input values (e.g., customers' orders, teams' categories, or service times). The data set was imported into an Excel spreadsheet, which was programmed to run the heuristics. Excel was chosen because it was already used by the collaborator responsible for daily route planning; therefore, it did not require any training. In addition, by using Excel, there is no time or cost to ensure the heuristic integration with existing software and infrastructure. All the routes start at the depot point (the company warehouse). Here, all the materials necessary for satisfying the customer orders are loaded in each truck according to route planning and respective team allocation to customers. After satisfying the last customer en route, the teams do not need to travel back to the depot; so the last customer location corresponds to the route ending point. Through the application of the proposed heuristic, routes were obtained for P-type, M-type, and S-type teams. Figure 3 provides the route planning for team \(E_{m11}\), which is an assembly team with category 1; therefore, it can serve PAX and assembly-type customers. In this example, this team leaves the depot with all the materials necessary to serve four customers; two of them are PAX-type (i.e., \(C_{p14}\) and \(C_{p15}\)), and the other two are Assembly-type (i.e., \(C_{m47}\) and \(C_{m46}\)). Figure 3 represents the travel time between customers (i.e., \(Vti\), \(ti+1\)) and the service time (\(S_{ti}\)) in minutes, as well as the arrival instant at each customer (i.e., \(T_{ti}\), \(E_{tk}\)) and the total estimated route time in hours. There is a 1 h lunch break during a work day. The  
Figure 2: Heuristics graphic display. travel time from the depot to the first customer is not included in the route planning. In Appendix B, the results for M-type teams are presented. After obtaining the routes for all types of teams and assessing the performance of the developed heuristic, a day of work for the teams under study was selected to apply it. The aim was to compare two different scenarios: the routes obtained through the proposed heuristics (which are designated by \"future scenario\") with the ones obtained through the current process, i.e., created by the worker of the company (and designated by \"current scenario\"), for the Lisbon and Tagus Valley area. A set of performance indicators was selected to assess the proposed heuristic and compare the scenarios. The metrics used for each indicator are presented in the following Equations (1)-(6). \[\text{Vehicle occupancy rate by weight}=\frac{\text{En route weight}}{\text{Maximum weight established for the team}}\times 100\% \tag{1}\]  
\[\text{Vehicle occupancy rate by volume}=\frac{\text{Volume allocated en route}}{\text{Maximum vehicle volume}}\times 100\% \tag{2}\]  
\[\text{Ratio of the commodity value to be transported in relation to the minimum value}= \tag{3}\]  
\[\text{Minimum value established for the team}\times 100\% \tag{4}\]  
\[\text{Ratio of linear meters to be transported in relation to the maximum value}= \tag{5}\]  
\[\text{Number of linear meters allocated en route}\times 100\% \tag{6}\]  
\[\text{Rate of customers visited within the time window}=\frac{\text{Number of customers visited within the time window}}{\text{Number of customers allocated en route}}\times 100\% \tag{7}\]  
\[\text{Average travel time between customers}=\frac{\text{Team's total travel time}}{\text{Number of customers allocated en team's route}} \tag{8}\]  
Table 7 provides the values obtained for each of the performance indicators considering both current and future scenarios for all types of teams within the company under study.