This thesis highlights the potential of demand management to enhance transportation efficiency, reduce costs, and improve sustainability. By employing demand management tools such as differentiated pricing, differentiated service offerings, and inventory allocation, it introduces innovative approaches to address the rising costs and environmental impact of modern logistics. Through three methodological studies, this thesis tackles distinct yet interconnected problems, offering practical solution methodologies, and motivating future exploration.nnThe first study focuses on demand steering in last‑mile delivery, proposing a framework to selectively incentivize customers to opt for pickup point delivery instead of home delivery. Utilizing a tailored branch‑and‑bound algorithm and heuristics, the study demonstrates substantial cost reductions. The second study addresses carbon emissions in last‑mile delivery, applying deep reinforcement learning to dynamically optimize pickup point offerings. By minimizing emissions from both delivery vehicles and customer travel, this study showcases the potential for environmental benefits of demand management. The third study extends the scope of demand management to both demand and supply in inventory repositioning, addressing imbalances in reusable transport item pooling systems. A heuristic based on the relax‑and‑fix framework is proposed, demonstrating cost savings and operational improvements in real‑world applications.nnCollectively, these studies emphasize the effectiveness of integrating demand management concepts into optimization to address transportation problems. The findings motivate future research directions, including the incorporation and elaboration of customer behavior models in optimization frameworks, scaling solutions to larger and more dynamic settings, and exploring novel applications of demand management to promote sustainable and cost‑effective transportation operations.