Asian Journal of Current Research

Efficient delivery route planning is a critical component of modern logistics and transportation systems, as it directly influences operational cost, delivery time, and service quality. However, delivery route optimization problems are computationally challenging due to their NP-hard nature, especially as the number of delivery points increases.

In this study, delivery route optimization is formulated as a Travelling Salesman Problem (TSP), and a Genetic Algorithm (GA)-based framework is proposed to obtain near-optimal routing solutions within reasonable computational time. Candidate routes are encoded as permutation chromosomes, and evolutionary operators including Order Crossover and swap mutation are employed to effectively explore the solution space. The proposed approach is evaluated on synthetic datasets ranging from 10 to 50 delivery nodes, as well as on standard benchmark instances from TSPLIB. Experimental results demonstrate that the GA consistently outperforms traditional heuristic methods such as greedy and nearest neighbor algorithms, achieving route length reductions of approximately 17–18% on medium-sized instances. Furthermore, on benchmark datasets, the obtained solutions remain within 2% of the known optimal values. These results indicate that the proposed GA framework provides an effective and practical solution for small- to medium-scale delivery route optimization problems, balancing solution quality and computational efficiency.