Abstract
Service delivery optimization has an important impact on organizational profitability, where changes in allocation of resources (e.g. humans, equipment and materials) to services increases profit. Simulation and optimization techniques generally suffer from three main drawbacks; firstly, the limited knowledge and skill of researchers in modeling social complexities. Secondly, having assumed that a fairly realistic model of the problem is simulated, finding optimal solutions requires an exhaustive search that is almost impossible in problems with a large search space. Thirdly, mathematical optimization techniques often require the acquisition of knowledge in a central unit, which is problematic e.g. for privacy reasons. This article introduces a new technique, which combines Agent Based Modeling (ABM) and Distribution Constraint Optimization (DCOP) to overcome these difficulties. Our empirical results present a successful model for finding optimized resourced allocation settings in comparison with two different ABM simulated models on a sample of a real-life service delivery problem.
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References
Spohrer, J., Vargo, S.L., Caswell, N., Maglio, P.P.: The service system is the basic abstraction of service science. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences, p. 104. IEEE (2008)
Ramaswamy, L., Banavar, G.: A formal model of service delivery. In: IEEE International Conference on, Services Computing, SCC 2008, vol. 2, pp. 517–520. IEEE (2008)
Maglio, P.P., Srinivasan, S., Kreulen, J.T., Spohrer, J.: Service systems, service scientists, SSME, and innovation. Communications of the ACM 49(7), 81–85 (2006)
Glover, F., Kelly, J.P., Laguna, M.: New advances for wedding optimization and simulation. In: Proceedings of 1999 Winter Simulation Conference, vol. 1, pp. 255–260. IEEE (1999)
Luo, Y., Lim, E.: Simulation-based optimization over discrete sets with noisy constraints. In: Proceedings of the 2011 Winter Simulation Conference (WSC), pp. 4008–4020. IEEE (2011)
Ghose, A.K., Koliadis, G.: Actor eco-systems: From high-level agent models to executable processes via semantic annotations (2007)
Yokoo, M., Ishida, T., Durfee, E.H., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: Proceedings of the 12th International Conference on Distributed Computing Systems, pp. 614–621. IEEE (1992)
Yokoo, M., Hirayama, K.: Algorithms for distributed constraint satisfaction: A review. Autonomous Agents and Multi-Agent Systems 3(2), 185–207 (2000)
Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 438–445. IEEE Computer Society (2004)
Modi, P.J., Shen, W.-M., Tambe, M., Yokoo, M.: An asynchronous complete method for distributed constraint optimization. AAMAS 3, 161–168 (2003)
Yeoh, W., Felner, A., Koenig, S.: BnB-ADOPT: An asynchronous branch-and-bound DCOP algorithm. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 591–598. International Foundation for Autonomous Agents and Multiagent Systems (2008)
Yeoh, W., Felner, A., Koenig, S.: IDB-ADOPT: A depth-first search DCOP algorithm. In: Oddi, A., Fages, F., Rossi, F. (eds.) CSCLP 2008. LNCS, vol. 5655, pp. 132–146. Springer, Heidelberg (2009)
Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. No. EPFL-REPORT-52705 (2005)
Petcu, A., Faltings, B., Parkes, D.C.: MDPOP: Faithful distributed implementation of efficient social choice problems. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1397–1404. ACM (2006)
Petcu, A., Faltings, B.: ODPOP: an algorithm for open/distributed constraint optimization. Proceedings of the National Conference on Artificial Intelligence 21(1), 703 (1999, 2006)
Liu, J.-S., Sycara, K.P.: Exploiting Problem Structure for Distributed Constraint Optimization. In: ICMAS, vol. 95, pp. 246–254 (1995)
Modi, P.J., Jung, H., Tambe, M., Shen, W.-M., Kulkarni, S.: A dynamic distributed constraint satisfaction approach to resource allocation. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 685–700. Springer, Heidelberg (2001)
Leaute, T., Ottens, B., Szymanek, R.: FRODO: a FRamework for Open/Distributed Optimization Version 2.6. 2 User Manual (2010)
Mohagheghian, M., Sindhgatta, R., Ghose, A.: Combining Agent Based Modeling with Distributed Constraint Optimization for Service Delivery Optimization. In: Proceedings of the EDOC-2014 Workshop on Service-Oriented Enterprise Architecture for Enterprise Engineering. IEEE CS Press (2014)
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Mohagheghian, M., Sindhgatta, R., Ghose, A. (2014). An Extended Agent Based Model for Service Delivery Optimization. In: Dam, H.K., Pitt, J., Xu, Y., Governatori, G., Ito, T. (eds) PRIMA 2014: Principles and Practice of Multi-Agent Systems. PRIMA 2014. Lecture Notes in Computer Science(), vol 8861. Springer, Cham. https://doi.org/10.1007/978-3-319-13191-7_22
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DOI: https://doi.org/10.1007/978-3-319-13191-7_22
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