MilleMiglia: A realistic instance generator for middle-mile logistics

How can a Dutch poffert arrive at your door, 450 miles (700 km) away, the very next day? It’s thanks to careful logistics optimization — especially the middle-mile segment. This part of the journey covers the longest distance, represents a huge portion of the overall costs, and most importantly dictates whether your poffert arrives fresh or stale.

Logistics research has historically focused on the first mile (moving goods from producers to initial consolidation points) and the last mile (delivering to the consumer). Both stages are typically modeled as variants of the vehicle routing problem (VRP). However, the middle mile, which handles the bulk movement of goods between distribution centers at a regional or continental scale, has received significantly less attention in operational research despite representing a sizable portion of total logistics expenditure. Academic progress in middle-mile optimization has been hindered by a lack of public, high-quality data. Indeed, most logistics companies treat their network topologies and demand volumes as highly sensitive proprietary information.

Middle-mile logistics has many applications in the supply chain. These range from moving goods from factories to consumers in e-commerce and retailers in city centers, to carrying the right parts from individual plants and central storage to car manufacturers and shops. It also includes time-sensitive movements, like transporting temperature-controlled pharmaceuticals between storage facilities and hospitals.

The logistics spectrum: First, last and middle mile

The distinction between first-, middle- and last-mile logistics lies in the journey of an individual shipment. Throughout this journey, the primary operational goal is to efficiently use a fleet of vehicles to visit multiple locations. Consider the example of a manufacturer that sells goods on a typical online marketplace to reach individual consumers.