Abstract
Supply chain management, which is composed of interdependent entities that have defined roles and responsibilities, shows several characteristics in common with Multi-Agent Systems (MAS). This type of problem may be divided into several local subproblems, which can be optimized separately. However, in general, the full problem cannot be solved in a centralized way due to its complexity or the need for information privacy. This work presents a distributed heuristic method which provides an acceptable optimization of this type of complex problem when compared to the centralized approach available for the considered instances, and better than a similar approach in the literature. It is based on modeling the considered problem first as a Distributed Constraint Optimization Problem (DCOP), and then by integrating it with Mixed-Integer Linear Programming (MILP) optimization models of its subproblems. We have obtained a value which is about 5% better than a similar distributed method in the literature and only about 7% worse than the actual optimum one. We consider a promising approach for increasingly real settings.
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Notes
- 1.
(\(1\dots 10\) in CSPLib).
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Acknowledgments
This work has been supported by the National Agency for Petroleum, Natural Gas and Biofuels through the clauses for funding of Research, Development and Innovation investments established by the Resolution no. 50/2015 and by PETROBRAS under grant TC 5900.0112830.19.9. Fernanda N. T. Furukita was also supported by CNPq, Brazil, grant number 136228/2020-8.
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Furukita, F.N.T., Marcellino, F.J.M., Sichman, J. (2022). Combining DCOP and MILP for Complex Local Optimization Problems. In: Bandini, S., Gasparini, F., Mascardi, V., Palmonari, M., Vizzari, G. (eds) AIxIA 2021 – Advances in Artificial Intelligence. AIxIA 2021. Lecture Notes in Computer Science(), vol 13196. Springer, Cham. https://doi.org/10.1007/978-3-031-08421-8_5
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