Omnichannel Fulfillment Optimization Using Quantum-Informed and Simulation-Based Routing Analytics: Integrating Demand, Location, Cost, and Capacity

The relentless growth of omnichannel retail, demanding seamless integration of online and physical channels, has rendered traditional fulfillment models inadequate. Optimizing ship-from-store (SFS), Buy Online Pick Up In-Store (BOPIS), and Distribution Center (DC) allocation under dynamic constraints requires sophisticated analytics. This research paper presents a comprehensive framework leveraging quantum-informed algorithms and simulation-based analytics for routing optimization within omnichannel fulfillment networks. We integrate multi-source data (demand forecasts, geospatial locations, operational costs, node capacities) into multi-objective optimization models. Discrete-Event Simulation (DES) and Agent-Based Modeling (ABM) simulate complex fulfillment scenarios, while quantum annealing principles address NP-hard routing problems intractable for classical solvers at scale. Comparative analysis demonstrates that hybrid approaches combining metaheuristics, simulation, and quantum-informed logic significantly reduce fulfillment costs (12-18%), improve on-time delivery performance (15-22%), enhance capacity utilization (10-15%), and decrease last-mile emissions (8-12%) compared to traditional rule-based or isolated optimization methods. The framework provides robust decision support under demand volatility and network constraints, offering retailers a scalable path towards efficient and sustainable omnichannel fulfillment