Maritime container gates involve a sequence of tasks, including vehicle and cargo identification, document verification, routing decisions, and access authorization. However, the information required to support these activities is often fragmented across multiple independent platforms. This paper introduces GateOS.ai, a Smart Gate information system designed for the Port of Sines. The study followed an echeloned Design Science Research methodology informed by stakeholder co-creation and human-centered design principles. The resulting artifact comprises validated checkpoint-based requirements, an operator-facing interface prototype, and a layered architecture grounded in Digital Twin concepts. The architecture connects gate infrastructure and captured events with data management, external systems, analytical models, and user decision support. Qualitative stakeholder reviews helped refine the proposed workflows and confirm their alignment with the intended gate process. The study provides a structured path from operational needs to system design and supports the gradual evolution of the gate environment toward a data-driven ecosystem.

Rapid growth in parcel delivery and increasing demand for time-sensitive freight, such as fresh food and pharmaceuticals, have intensified the need for fast and congestion-resilient urban logistics systems. However, ground-based logistics infrastructure in dense metropolitan areas is already highly saturated, and further facility expansion is often spatially and operationally constrained. This study proposes a multi-period facility location model for an integrated air–ground–underground urban logistics system that connects Urban Air Mobility, ground vehicles, and subway-based freight transportation with existing logistics infrastructure. Unlike conventional hub location or multi-echelon facility location problems, the proposed model determines whether candidate locations can serve as integrated logistics hubs capable of supporting multimodal transshipment among aerial, ground, and underground transportation modes. Candidate sites include existing large-scale ground terminals, micro-fulfillment centers, subway stations suitable for underground logistics facilities, and vacant sites where new facilities can be constructed. The model simultaneously considers facility construction and operation decisions, inter-hub freight flows, last-mile delivery allocation, unmet demand penalties, transshipment costs, and annual budget constraints over multiple planning periods. The proposed framework also reflects the shared-use nature of UAM infrastructure by considering the interaction between passenger and freight demand when estimating available cargo capacity. To examine the applicability of the model, a case study is conducted for the Seoul metropolitan area, where ground logistics facilities are highly saturated and subway infrastructure is widely distributed. The results demonstrate that the proposed model can support phased investment decisions for multimodal logistics hubs and provide strategic insights for improving the responsiveness of time-sensitive urban freight delivery.

The Storage Location Assignment Problem (SLAP) determines warehouse handling costs and energy consumption. We introduce the Mergeable Storage Location Assignment Problem (MSLAP), which allows adjacent locations to be merged, increasing capacity for high-demand products and reducing replenishment frequency. A direct cost-minimizing integer program formulates MSLAP exactly but its dense overlap constraints stall at industrial scale. We recast the problem on an extended flow network with proportional side constraints and solve it in three phases: greedy initialization, flow-based topology exploration, and exact bipartite matching. On a real instance (1,543 products, 3,285 locations), the flow model reaches in one minute a cost the direct model cannot match in two hours, validating four times as many merges. Optimal assignment cuts material-handling energy by about 12%, meeting a 10% target; among compliant layouts the recommended one reduces operating cost by roughly a third, most of it from merging. Station workload balance improves by 21%, an observed side effect of spreading merges across the grid.

This paper studies a joint inventory and fulfillment problem for an omnichannel retailer under stochastic demand. The retailer first allocates inventory across fulfillment locations and then decides how to fulfill customer orders after demand is realized. We formulate the problem as a two-stage stochastic optimization model that minimizes expected holding, fulfillment, and penalty costs. To reduce the computational effort, we use a scenario reduction method based on a modified Wasserstein distance that accounts for the cost structure of the problem. Numerical results show that this method achieves lower costs than the benchmark algorithm.