Container logistics is under increasing pressure to deliver efficient and sustainable hinterland transport. Achieving this requires improving the performance of environmentally friendly modes, such as inland waterway transport. This study examines the potential of cargo consolidation as a strategy to tackle suboptimal filling rates of containers, one of the most persistent inefficiencies in the Rhine-Alpine Corridor. We develop an integrated optimization model that holistically accounts for the operational and spatial requirements of consolidation, assessing the sensitivity of the strategy to labor costs, fuel prices, value of time, and vessel costs. The results show that, despite additional handling and coordination costs, consolidation can reduce overall transport costs by up to 4 % and attract as much as 42 % more container volumes to IWT on specific origin-destination connections. Vessel occupation rates emerge as a decisive factor in determining consolidation benefits, while cost parameters such as labor and fuel prices strongly influence outcomes. The research illustrates how optimizing this strategy can contribute to the sustainability of port-hinterland container transport and discusses the conditions required for its realization.

This study introduces an optimization framework for deploying Mobile Fleet Inventories (MFIs) to address operational inefficiencies in on-demand delivery systems. Traditionally, these systems rely on stationary facilities to organize operations and manage resources. While stationary facilities provide stability and structured coverage, they are inherently rigid and struggle to adapt to the spatial and temporal fluctuations characteristic of urban service demand. By leveraging urban waterways, MFIs act as dynamic, mobile facilities, enabling real-time resource redistribution and offering greater flexibility to meet evolving demand patterns efficiently. We formulate the problem as a mixed-integer linear programming model to optimize MFI deployment, minimizing total system costs. The model incorporates both capital investments (e.g., MFI leasing and docking infrastructure) and operational expenses (e.g., rider idle time). Key decisions include determining the optimal number, placement of MFIs, and fleet size. To validate the approach, we apply it to a meal delivery platform in Amsterdam, demonstrating its practicality and scalability. Results show that implementing MFIs reduces overall system costs by 17% and decreases rider idle time by 35% compared to stationary facility operations. These findings underscore the transformative potential of MFIs to enhance the efficiency, sustainability, and adaptability of on-demand delivery systems in urban settings.