Urban last-mile distribution in emerging economies suffers from unique levels of operational complexity, largely due to the prevailing dominance of the highly fragmented traditional retail channel.
To support companies in strategically designing efficient urban distribution networks in such uncertain market environments, we propose a large-scale stochastic mixed-integer linear programming model that incorporates three commonly deployed measures of physical distribution flexibility. Being able to flexibly adjust transportation capacities, facility capacities, and demand allocations may enable companies to serve urban markets more efficiently, especially in the presence of demand uncertainty. The model supports strategic network design decisions by optimizing the number, type, and capacity of distribution facilities as well as the composition of corresponding vehicle fleets. We apply the model to two large-scale, real-world case studies based on real data from Coca-Cola Femsa’s last-mile operations in Bogotá and Cali, Colombia.
