<![CDATA[Product distribution is a critical component of supply chain performance in manufacturing industries, as inefficient routing decisions can significantly increase logistics costs and reduce delivery reliability, especially for small and medium-sized enterprises (SMEs) with limited resources. Despite its importance, many companies continue to rely on manual or experience-based route planning, which often results in suboptimal travel distances and unnecessary transportation expenses. This issue is not limited to a single firm but is commonly encountered in manufacturing distribution systems characterized by multiple delivery points and constrained operational capacity. This study develops and evaluates a distribution route optimization model using two heuristic algorithms, namely Nearest Neighbour and Nearest Insert, both of which are widely applied to solve the Travelling Salesman Problem (TSP) in practical logistics contexts. A quantitative research approach was employed by collecting data on delivery locations, inter-point distances, and transportation costs, which were subsequently analyzed through mathematical modeling and algorithmic simulations. The results show that the initial distribution routes required a total weekly travel distance of 362.43 km with an estimated transportation cost of Rp 578.792,31. After optimization, the Nearest Neighbor method reduced the distance to 329.89 km with a cost of Rp 553.761,54, while the Nearest Insert method resulted in a distance of 336.08 km and a cost of Rp 558.526,15. Overall, the Nearest Neighbour algorithm achieved the best performance, yielding a distance reduction of 32.54 km (8.98%) and a transportation cost saving of Rp 25.030,77 (4.32%) compared to the initial routes. These findings demonstrate that simple heuristic-based optimization models can significantly improve distribution efficiency and cost performance in manufacturing supply chains. The study contributes empirical evidence that such methods can be effectively adopted by SMEs as a scalable and resource-efficient decision-support tool for route planning, enabling cost reductions without increasing fleet size or reducing service coverage.]]>
