ERiM Seminar – Advancing time window assignment and vehicle routing inlast-mile logistics

We explore three closely related time window assignment practices, each reflecting how and when commitments are made: (i) next-day delivery with stochastic travel times, where windows must be promised before full uncertainty is revealed, (ii) dynamic next-day booking systems, where providers must decide when to communicate commitments, and (iii) same-day services, where customers select time slots in real time and the design of the slot menu directly influences both customer choices and operational feasibility. Service providers face multiple sources of uncertainty, including fluctuating demand, variable travel times, and dynamic booking patterns. Early commitments can improve customer experience but restrict routing flexibility, while delayed communication allows for more efficient planning but risks frustrating customers. We develop novel models and methodologies that integrate stochastic optimization, anticipatory decision-making, and machine learning. 

Together, these contributions provide a unified framework for balancing efficiency, reliability, and fairness in last-mile delivery systems, and they illustrate how optimization and learning can be combined to meet the growing demands of customer-centric logistics.