Inventory routing for defense: Moving supplies in adversarial and partially observable environments

Future defense logistics will be heavily reliant on autonomous vehicles for the transportation of supplies. We consider a dynamic logistics problem in which: multiple supply item types are transported between suppliers and consuming (sink) locations; and autonomous vehicles (road-, sea-, and air-based) make decisions on where to collect and deliver supplies in a decentralized manner. Sink nodes consume dynamically varying demands (whose timing and size are not known a priori). Network arcs, and vehicles, experience failures at times, and for durations, that are not known a priori. These dynamic events are caused by an adversary, seeking to disrupt the network. We design domain-dependent planning algorithms for these vehicles whose primary objective is to minimize the likelihood of stockout events (where insufficient resource is present at a sink to meet demand). Cost minimization is a secondary objective. The performance of these algorithms, across varying scenarios, with and without restrictions on communication between vehicles and network locations, is evaluated using agent-based simulation. We show that stockpiling-based strategies, where quantities of resource are amassed at strategic locations, are most effective on large land-based networks with multiple supply item types, with simpler “shuttling†-based approaches being sufficient otherwise.