  We address the warehouse servicing problem (WSP) in automated warehouses,
which use teams of mobile agents to bring products from shelves to packing
stations. Given a list of products, the WSP amounts to finding a plan for a
team of agents which brings every product on the list to a station within a
given timeframe. The WSP consists of four subproblems, concerning what tasks to
perform (task formulation), who will perform them (task allocation), and when
(scheduling) and how (path planning) to perform them. These subproblems are
NP-hard individually and become more challenging in combination. The difficulty
of the WSP is compounded by the scale of automated warehouses, which frequently
use teams of hundreds of agents. In this paper, we present a methodology that
can solve the WSP at such scales. We introduce a novel, contract-based design
framework which decomposes an automated warehouse into traffic system
components. By assigning each of these components a contract describing the
traffic flows it can support, we can synthesize a traffic flow satisfying a
given WSP instance. Component-wise search-based path planning is then used to
transform this traffic flow into a plan for discrete agents in a modular way.
Evaluation shows that this methodology can solve WSP instances on real
automated warehouses.
