OPTIMIZING BI-OBJECTIVE MULTIPLE TRAVELING SALESMEN ROUTES FOR DISASTER RELIEF LOGISTICS USING GENETIC ALGORITHM - Sihombing, Amos Hatoguan; Herdiana, Ratna; Pratama, Jovian Dian (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2025)
Handling natural disasters such as floods requires efficient logistics distribution to minimize the negative impact on victims. Distribution route optimization becomes very important in this process. This paper applies a metaheuristic method using Genetic Algorithm to the Bi-objective Multiple Traveling Salesman Problem (BMTSP) to obtain a solution that minimizes the distance and time to deliver disaster relief logistics. Multiple vehicles are used in this study to represent delivery agents with two main objectives, namely minimizing total distance and travel time. Genetic Algorithm is applied by considering these two main objectives through the process of selection, crossover, mutation, and produces an effective Pareto solution. The results indicate that applying the Genetic Algorithm to the Bi-Objective Multiple Traveling Salesman Problem yields more efficient delivery routes—reducing both distance and time—compared to the Nearest Neighbor Algorithm. The simulation and testing in this study utilize data on distances and travel times among Central Java Regional Disaster Management Agency offices in 19 regencies—including a central depot—located in flood-prone areas of Central Java Province. The scenario involves two vehicles with identical load capacities.