Integrated Location–Allocation and Dynamic Routing for E-commerce Networks Under Demand Uncertainty

Abstract

E-commerce logistics networks are under increasing pressure to strike a balance between operational efficiency and service quality under unstable patterns of demand. The classical planning models treat facility location and vehicle routing as a set of independent optimization problems, which do not reflect important interdependencies between strategic network design and operational dispatch decisions. These decision layers are combined in a two-stage stochastic programming framework with distribution facilities in place before demand is realized, and operational flexibility is retained by taking recourse actions of dynamic routing decisions. The former is the determination of facility activation and the initial demand zone assignments in the face of uncertainty, and the latter is the adaptive construction of vehicle routes as the customer orders become known. Rolling horizon heuristics allow path replanning within operating windows, which can handle real-time arrivals of orders without the need to add too much computational load. Geospatial road network data provides realistic estimates of distances that accommodate real driving routes and city topography. The validation of simulation has shown that the efficiency in transportation, cost reduction, and responsiveness of service are significantly improved as compared to traditional centralized or static planning strategies. The combined framework is specifically useful where third-party logistics providers, online grocery delivery businesses, and business-to-consumer retailers are dealing with uncertain demand and managing distributed customer bases with strong demand expectations in terms of delivery time.