TY - JOUR
T1 - Heterogeneous multi-agent resource allocation through multi-bidding with applications to precision agriculture⁎
AU - Cobbenhagen, A. T.J.R.
AU - Antunes, D. J.
AU - van de Molengraft, M. J.G.
AU - Heemels, W. P.M.H.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Agriculture
KW - Approximate dynamic programming
KW - Distributed optimization
KW - Multi-agent resource allocation
KW - Optimal control
UR - http://www.scopus.com/inward/record.url?scp=85058469019&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2018.12.034
DO - 10.1016/j.ifacol.2018.12.034
M3 - Conference article
AN - SCOPUS:85058469019
SN - 2405-8963
VL - 51
SP - 194
EP - 199
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
IS - 23
T2 - 7th IFAC Workshop on Distributed Estimation and Control in Networked Systems NECSYS 2018
Y2 - 27 August 2018 through 28 August 2018
ER -