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).
References
Burke, D.A.: Exploiting problem structure in distributed constraint optimisation with complex local problems. Ph.D. thesis, Department of Computer Science, University College Cork, Ireland (2008)
Burke, D.A., Brown, K.N., Dogru, M., Lowe, B.: Supply chain coordination through distributed constraint optimization. In: 9th International Workshop on Distributed Constraint Reasoning (2007)
Faltings, B., Yokoo, M.: Introduction: special issue on distributed constraint satisfaction. Artif. Intell. 161(1–2), 1–5 (2005)
Farinelli, A., Rogers, A., Petcu, A., Jennings, N.R.: Decentralised coordination of low-power embedded devices using the max-sum algorithm. In: Seventh International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2008, 11–15 May 2008, pp. 639–646 (2008)
Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. J. Artif. Intell. Res. 61, 623–698 (2018)
Gent, I.P., Walsh, T.: CSPlib: a benchmark library for constraints. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 480–481. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-540-48085-3_36
Gershman, A., Meisels, A., Zivan, R.: Asynchronous forward bounding for distributed cops. J. Artif. Intell. Res. 34, 61–88 (2009)
Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 222–236. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0017442
Léauté, T., Ottens, B., Szymanek, R.: FRODO 2.0: an open-source framework for distributed constraint optimization. In: Proceedings of the IJCAI 2009 Distributed Constraint Reasoning Workshop (DCR 2009), Pasadena, California, USA, pp. 160–164 (2009). https://frodo-ai.tech
Maheswaran, R.T., Pearce, J.P., Tambe, M.: Distributed algorithms for DCOP: a graphical-game-based approach. In: ISCA PDCS, pp. 432–439 (2004)
Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M.: An asynchronous complete method for distributed constraint optimization. In: Proceedings of the Second International Joint Conference on Autonomous Agents & Multiagent Systems, AAMAS 2003, Melbourne, Victoria, Australia, 14–18 July 2003, pp. 161–168. ACM (2003)
Petcu, A., Faltings, B.: DPOP: a scalable method for multiagent constraint optimization. In: IJCAI 2005, CONF, pp. 266–271 (2005)
Silaghi, M.C., Mitra, D.: Distributed constraint satisfaction and optimization with privacy enforcement. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2004, pp. 531–535. IEEE (2004)
Studio IICO: OPL language user’s manual version 12 release 7 (2017)
Williams, H.P.: Model Building in Mathematical Programming. Wiley, Hoboken (2013)
Yokoo, M., Hirayama, K.: Distributed constraint satisfaction algorithm for complex local problems. In: Proceedings International Conference on Multi Agent Systems (Cat. No. 98EX160), pp. 372–379. IEEE (1998)
Yokoo, M., Ishida, T., Durfee, E.H., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: 1992 12th International Conference on Distributed Computing System, pp. 614–615. IEEE Computer Society (1992)
Yuan, Y., Liang, P., Zhang, J.J., et al.: Using agent technology to support supply chain management: potentials and challenges. Technical report. University Hamilton (2001)
Zhang, W., Wang, G., Xing, Z., Wittenburg, L.: Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artif. Intell. 161(1–2), 55–87 (2005)
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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