MilleMiglia：面向中间一英里物流的真实实例生成器

Aymane Lotfi, Software Engineer, Ads & Commerce, and Thibaut Cuvelier, Software Engineer, Google Research MilleMiglia bridges the gap between academic theory and industrial logistics by providing open-source, realistic benchmarks that allow researchers to optimize complex middle-mile networks, ultimately leading to more robust and efficient global supply chains. 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. Middle-mile logistics bridges the gap between first and last mile. To address the lack of standardized data for this domain, in “ A Novel Instance Generator for Simulating Middle-Mile Logistics Networks ”, we introduce MilleMiglia, a C++ instance generator designed to create realistic benchmarks for middle-mile delivery problems. This work serves as a foundational building block to enable future research results. In this post, we explore the unique constraints of the middle mile and how MilleMiglia successfully captures them to generate realistic, privacy-preserving data. The source code and documentation are available on GitHub . 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. In first- and last-mile logistics, a specific shipment remains in a single vehicle from its origin (the factory in the first mile, the distribution center in the last mile) to its destination (the distribution center in the first mile, the customer in the last mile). These VRPs involve optimizing a fleet of several vehicles over a limited time span, usually a single day. The optimization challenge is essentially one of assignment and sequencing: determining which vehicle handles which set of shipments, and in what order. In our example, the first mile corresponds to the collection of the items that have been sold by the manufacturer (e.g., pofferts ) while the last mile covers the final delivery to the consumers (some of them being quite hungry!). In both cases, a single truck transports goods to or from the regional distribution center. However, if the manufacturer and the consumer are in different regions, middle-mile logistics bridge the gap between far-away distribution centers. For instance, goods from a manufacturer in Groningen (Netherlands) would first move to the regional distribution c