AI-enabled integrated omnichannel network design, inventory control, and pricing

This thesis topic aligns perfectly with the research focus of the DISP laboratory, at the intersection of decision support, complex systems optimization, artificial intelligence, and sustainable organizational performance. It leverages advanced methods of optimization under uncertainty, machine learning, and simulation to design more resilient and efficient omnichannel logistics networks. The issues addressed include the integration of strategic, tactical, and operational decisions; consideration of demand patterns; inventory management; dynamic pricing; and environmental constraints. It directly responds to the current challenges of digital transformation and supply chain sustainability. The thesis will also contribute to the development of explainable and scalable decision-making models, consistent with DISP's ambition to produce research with strong scientific and socio-economic impact. Finally, the integration of urban and environmental dimensions will strengthen the laboratory's work on the ecological transition of production, distribution, and service systems.

Keywords: Omnichannel logistics,Urban logistics,Logistics network design,Inventory management,Dynamic pricing,Stochastic optimization