Allocation Efficiency Based on Multiple Matrix Model

This paper investigates the application of matrix-based optimization models in improving transport logistics efficiency, with a primary focus on Indonesias archipelagic logistical challenges. Using the Clarke-Wright savings algorithm, this paper analyzes route optimization for a public logistics company distributing rice across multiple islands. The study demonstrates a 45.91% reduction in travel distance-achieved through dynamic rerouting and load consolidation-translating to significant cost savings (approximately 32% lower fuel and labor expenditures). Additionally, this paper examines a parallel case in the Philippines, where similar geographic constraints apply, and find comparable efficiency gains (38.7% distance reduction, with 28% cost savings), validating the models robustness in diverse archipelagic contexts. Beyond empirical results, the research explores three critical dimensions: the scalability matrix models for larger island networks, computational limitations (e.g., runtime inefficiencies for >50-node problems), and integration challenges with real-world constraints such as weather disruptions and port delays. A sensitivity analysis further reveals that fuel price volatility disproportionately affects savings in remote routes, underscoring the need for adaptive algorithms. The study concludes with policy recommendations for emerging economies facing high transport costs, emphasizing public-private data sharing, investment in digital infrastructure, and pilot testing of optimization tools in priority corridors. These insights contribute to both operational logistics literature and sustainable development goals (SDG 9: Industry, Innovation, and Infrastructure).