In this paper we consider the problem of allocating multiple resources to a number of clients by a group of heterogeneous agents over time such that the clients can produce products while maximizing a profit function. We propose an approximate optimization framework in which every client provides multiple bids from which the agents choose such that an allocation is feasible and that the profit function is maximized over time. The proposed framework exploits decomposition techniques that can be used for large-scale multi-agent resource allocation problems in which the cost objective is additive, the dynamics of product generation is non-linear and the agents have different capabilities. Interestingly, the decomposition can be solved in a distributed fashion, enabling application to large-scale problems. We apply this decomposition to the management of resources and agents in precision agriculture as an inspirational and important application domain of the obtained results. We show that our framework can be used in order to schedule the time, location and quantity of resources that every agent must provide whilst optimizing the profit of the entire farm over the growing season.
- Approximate dynamic programming
- Distributed optimization
- Multi-agent resource allocation
- Optimal control