A Data-Driven Simulation Model for Route Mapping and Warehouse Location Optimization

The increasing complexity of global supply chains and the demand for faster, cost-efficient, and sustainable delivery services have highlighted the critical importance of route mapping and warehouse location optimization in logistics operations. This paper proposes a data-driven simulation model that integrates real-time traffic, demand forecasting, and operational constraints to optimize route mapping and warehouse location decisions simultaneously. Leveraging a systematic literature review, this study identifies gaps in existing models, particularly their limitations in capturing dynamic demand patterns, real-time traffic data, and sustainability imperatives within logistics planning. The proposed simulation model employs agent-based and discrete-event simulation approaches, coupled with machine learning and metaheuristic optimization techniques, to enable logistics firms to evaluate multiple scenarios for warehouse siting and routing decisions while aligning with environmental, social, and governance (ESG) goals. The study contributes to the logistics and supply chain management literature by providing a scalable, technology-agnostic framework designed to improve operational efficiency, reduce carbon emissions, and enhance last-mile delivery performance. This research supports practitioners and policymakers in making data-driven, resilient, and sustainable logistics network design decisions in an increasingly volatile and competitive market environment.