CUSTOMER'S OPTIMAL BILLING ROUTE USING QACO: QGIS MAPPING AND ANT COLONY OPTIMIZATION (ACO) ALGORITHM

Acceleration of PLN’s Cash-In is highly influenced by customers’ early payment behavior. Therefore, PLN needs an effective approach to shift the payment pattern of customers who often pay late or after the due date, into making payments before the due date. One contributing factor to delayed payments is the limited number of electricity payment counters and customers forgetting to pay their bills. This study analyzes customer electricity payment trends over a 6-month period at PLN UP3 XYZ and found that 9.29% of customers were in arrears, with their locations widely dispersed. Thus, a strategy is required to change customer payment behavior through preventive billing activities carried out by billmen. However, these preventive billing activities are time-constrained due to the billmen's other assigned duties. Therefore, a supporting tool in the form of an optimal billing route is needed. To construct this optimal route, the Traveling Salesman Problem (TSP) model is applied using geographic mapping through QGIS and the Ant Colony Optimization (ACO) algorithm. QGIS is used to map the distribution of delinquent customers based on geographic condition analysis. The results show that the ACO algorithm is capable of producing a total travel distance of 1,022 km, compared to the conventional method’s 4,300 km, resulting in a distance saving of 3,278 km (76.23%).