ALMM Solver - A Tool for Optimization Problems

  • Ewa Dudek-Dyduch
  • Edyta Kucharska
  • Lidia Dutkiewicz
  • Krzysztof Rączka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8468)


The aim of our paper is to present the concept and structure of a software tool named the ALMM Solver. The goal of the solver is to generate solutions for discrete optimization problems, in particular for NP-hard problems. The solver is based on Algebraic Logical Meta-Model of Multistage Decision Process (ALMM of MDP) methodology, which is briefly described in the paper. Functionality and modular structure of the ALMM Solver is presented. SimOpt, the core module of the solver, is described in detail. Some possible future advances regarding the solver are also given.


solver optimizer algebraic-logical meta-model (ALMM) multistage decision process scheduling problem simulation tool 


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  1. 1.
    Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: JABAT Middleware as a Tool for Solving Optimization Problems. T. Computational Collective Intelligence 2, 181–195 (2010)CrossRefGoogle Scholar
  2. 2.
    Danping, L., Lee, C.K.M., Zhang, W.: Integrated GA and AHP for re-entrant flow shop scheduling problem. In: IEEE International Conference on Quality and Reliability, ICQR (2011)Google Scholar
  3. 3.
    Dudek-Dyduch, E.: Formalization and Analysis of Problems of Discrete Manufacturing Processes. Scientific bulletin of AGH University, Automatyka, vol. 54 (1990) (in Polish)Google Scholar
  4. 4.
    Dudek-Dyduch, E.: Learning based algorithm in scheduling. Journal of Intelligent Manufacturing 11(2), 135–143 (2000)CrossRefGoogle Scholar
  5. 5.
    Dudek-Dyduch, E., Dutkiewicz, L.: Substitution Tasks Method for Discrete Optimization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 419–430. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Dudek-Dyduch, E., Kucharska, E.: Learning method for co-operation. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part II. LNCS, vol. 6923, pp. 290–300. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Sękowski, H., Dudek-Dyduch, E.: Knowledge based model for scheduling in failure modes. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 591–599. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Evans, E.: Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison Wesley (2011)Google Scholar
  9. 9.
    Grobler-Dębska, K., Kucharska, E., Dudek-Dyduch, E.: Idea of switching algebraic-logical models in flow-shop scheduling problem with defects. In: Proceedings of the 18th International Conference on Methods and Models in Automation and Robotics, MMAR, pp. 532–537 (2013)Google Scholar
  10. 10.
    Hyun-Seon, C.: Scheduling algorithms for two-stage reentrant hybrid flow shops: minimizing makespan under the maximum allowable due dates. The International Journal of Advanced Manufacturing Technology (2009)Google Scholar
  11. 11.
    Jędrzejowicz, P., Wierzbowska, I.: JADE-Based A-Team Environment. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 719–726. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Kucharska, E., Dutkiewicz, L., Grobler-Dębska, K., Rączka, K.: ALMM approach for optimization of the supply routes for multi-location companies problem. In: Skulimowski, A. (ed.) Advances in Decision Sciences and Future Studies - Proceedings of the 8th International Conference on Knowledge, Information and Creativity Support Systems: Krakǫw, Poland, November 7-9, vol. 2, pp. 321–332 (2013)Google Scholar
  13. 13.
    Ligęza, A.: Improving Efficiency in Constraint Logic Programming Through Constraint Modeling with Rules and Hypergraphs. In: Federated Conf. on Computer Science and Information Systems, pp. 101–107. IEEE Computer Society Press (2012)Google Scholar
  14. 14.
    Mróz, H., Wąs, J.: Discrete vs. Continuous Approach in Crowd Dynamics Modeling Using GPU Computing. Cybernetics and Systems 45(1), 25–38 (2014)CrossRefGoogle Scholar
  15. 15.
    Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier (2006)Google Scholar
  16. 16.
    Sze, S.N., Tiong, W.K.: A Comparison between Heuristic and Meta-Heuristic Methods for Solving the Multiple Traveling Salesman Problem. World Academy of Science, Engineering and Technology, 300–303 (2007)Google Scholar
  17. 17.
    Tomczuk-Piróg, I., Wójcik, R., Banaszak, Z.: Decision Support Systems Based on CLP Approach in SMEs. In: IEEE Conference on Emerging Technologies & Factory Automation, vol. 1-3, pp. 1078–1083 (2006)Google Scholar
  18. 18.
    Wąs, J., Kułakowski, K.: Multi-agent Systems in Pedestrian Dynamics Modeling. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 294–300. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Wirfs-Brock, R.J.: Characterizing Classes. IEEE Software 23(2), 9–11 (2006)CrossRefGoogle Scholar
  20. 20.
    Verginadis, Y., Apostolou, D., Papageorgiou, N., Mentzas, G.: An architecture for collaboration patterns in agile event-driven environments. In: 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, WETICE 2009, pp. 227–230. IEEE (2009)Google Scholar
  21. 21.
    Zhang, C., Budgen, D.: What Do We Know about the Effectiveness of Software Design Patterns? IEEE Transaction on Software Engineering 38(5), 1213–1231 (2012)CrossRefGoogle Scholar
  22. 22.
  23. 23.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ewa Dudek-Dyduch
    • 1
  • Edyta Kucharska
    • 1
  • Lidia Dutkiewicz
    • 1
  • Krzysztof Rączka
    • 1
  1. 1.Department of Automatics and Biomedical EngineeringAGH University of Science and TechnologyKrakowPoland

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