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The learnable evolution model in agent-based delivery optimization

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Abstract

The learnable evolution model is a stochastic optimization method which employs machine learning to guide the optimization process. LEM3, its newest implementation, combines its machine learning mode with other search operators. The presented research concerns its application within a multi-agent system for autonomous control of container on-carriage operations. Specifically, LEM3 is used by transport management agents that act on behalf of the trucks of a forwarding agency for the planning of individual transport schedules.

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References

  1. Baluja S (1994) Population based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report (CMU-CS-94-63), Carnergie Mellon University

  2. Baluja S, Caruana R (1995) Removing the genetics from the standard genetic algorithm. In: Proceedings of the 12th international conference on machine learning, pp 38–46

  3. Bellifemine F, Caire G, Greenwood D (2007) Developing multi-agent systems with JADE. Wiley Series in Agent Technologies, Wiley Inter-Science, London

    Book  Google Scholar 

  4. Bent R, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper Res 52(6): 977–987

    Article  MATH  Google Scholar 

  5. Branke J, Kaussler T, Schmidt C, Schmeck H (2000) A multi-population approach to dynamic optimization problems. In: In adaptive computing in design and manufacturing, Springer, Berlin, pp 299–308

  6. Chia J, Goh C, Tan K, Shim V (2011) Memetic informed evolutionary optimization via data mining. Memet Comput 3:73–87. doi:10.1007/s12293-011-0058-7

    Google Scholar 

  7. Christofides N, Mingozzi A, Toth P (1989) The vehicle routing problem. In: Christofides N, Mingozzi A, Sandi C (eds) Combinatorial optimization. Wiley, New York, pp 315–338

    Google Scholar 

  8. Davidsson P, Henesey L, Ramstedt L, Törnquist J, Wernstedt F (2005) An analysis of agent-based approaches to transport logistics. Transp Res Part C Emerg Technol 13(4): 255–271

    Article  Google Scholar 

  9. Gehrke JD, Wojtusiak J (2008) Traffic prediction for agent route planning. In: Bubak M, van Albada G, Dongarra J, Sloot P (eds) 8th International conference on computational science 2008, vol 3. Springer, Berlin, pp 692–701

  10. Gendreau M, Guertin F, Potvin JY, Taillard E (1999) Parallel tabu search for real-time vehicle routing and dispatching. Transp Sci 33(4): 381–390

    Article  MATH  Google Scholar 

  11. Gendreau M, Guertin F, Potvin J, Séguin R (2006) Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transp Res Part C Emerg Technol 14(3): 157–174

    Article  Google Scholar 

  12. Hülsmann, M, Scholz-Reiter, B, Windt, K (eds) (2011) Autonomous cooperation and control in logistics. Springer, Berlin

    Google Scholar 

  13. Hülsmann, M, Windt, K (eds) (2007) Understanding autonomous cooperation and control in logistics. The impact on management, information and communication and material flow. Springer, Berlin

    Google Scholar 

  14. Hvattum L, Lokketangen A, Laporte G (2006) Solving a dynamic and stochastic vehicle routing problem with a sample scenario hedging heuristic. Transp Sci 40(4): 421–438

    Article  Google Scholar 

  15. Kilby P, Prosser P, Shaw P (1998) Dynamic VRPs: a study of scenarios. APES Technical Report APES-06-1998

  16. Larsen A (2000) The dynamic vehicle routing problem. Ph.D. thesis, Technical University of Denmark

  17. Llorà X, Goldberg D (2003) Wise breeding GA via machine learning techniques for function optimization. In: Cantú-Paz E, Foster J, Deb K et al (eds) Genetic and evolutionary computation—GECCO 2003, LNCS, vol 2723. Springer, Berlin, pp 1172–1183

    Chapter  Google Scholar 

  18. Lozano JA, Larrañaga P (2002) Estimation of distribution algorithms. A new tool for evolutionary computation. Genetic algorithms and evolutionary computation, vol 2. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  19. Machado P, Tavares J, Pereira F, Costa E (2002) Vehicle routing problem: doing it the evolutionary way. In: Proceedings of the genetic and evolutionary computation conference. Morgan Kaufmann Publishers Inc, Menlo Park, p 690

  20. Michalski R (1998) Learnable evolution: combining symbolic and evolutionary learning. In: Proceedings of the fourth international workshop on multistrategy learning (MSL’98). Desenzano del Garda, Italy, pp 14–20

  21. Michalski R (2000) Learnable evolution model: evolutionary processes guided by machine learning. Mach Learn 38: 9–40

    Article  MATH  Google Scholar 

  22. Michalski R, Wojtusiak J, Kaufman K (2007) Progress report on the learnable evolution model. Reports of the machine learning and inference laboratory MLI 07–2, George Mason University, Fairfax, VA

  23. Mitrovi-Mini S, Laporte G (2004) Waiting strategies for the dynamic pickup and delivery problem with time windows. Transp Res Part B Methodol 38(7): 635–655

    Article  Google Scholar 

  24. Montamenni R, Gambardella L, Rizzoli A, Donati A (2003) A new algorithm for a dynamic vehicle routing problem based on ant colony system. In: Second international workshop on freight transportation and logistics

  25. Mühlenbein H, Paaß G (1996) From recombination of genes to the estimation of distributions I. BinaryParameters. In: Voigt HM, Ebeling W, Rechenberg I, Schwefel HP (eds) Parallel problem solving from nature—PPSN IV, LNCS, vol 1141. Springer, Berlin, pp 178–187

    Chapter  Google Scholar 

  26. Parragh S, Doerner K, Hartl R (2008) A survey on pickup and delivery problems. Journal für Betriebswirtschaft 58: 81–117

    Article  Google Scholar 

  27. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1): 81–106

    Google Scholar 

  28. Reynolds RG, Zhu S (2001) Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming. IEEE Trans Syst Man Cybern Part B Cybern Publ IEEE Syst Man Cybern Soc 31(1): 1–18

    Article  Google Scholar 

  29. Reynolds R, Peng B (2004) Cultural algorithms: modeling of how cultures learn to solve problems. In: 16th IEEE international conference on tools with artificial intelligence. IEEE Comput Soc, pp 166–172

  30. Reynolds RG (1994) An introduction to cultural algorithms. In: Sebald AV, Fogel LJ (eds) Proceedings of the third annual conference on evolutionary programming. World Scientific Publishing, Rvier Edge, New Jersey, pp 131–139

  31. Saleem S, Reynolds RG (2001) Function optimization with cultural algorithms in dynamic environments. In: Proceedings of the workshop on particle swarm optimization 2001. Purdue School of Engineering and Technology, Indianapolis, IN

  32. Santana R, Larrañaga P, Lozano J (2009) Research topics in discrete estimation of distribution algorithms based on factorizations. Memet Comput 1:35–54. doi:10.1007/s12293-008-0002-7

    Google Scholar 

  33. Schuldt A (2011) Multiagent coordination enabling autonomous logistics. Springer-Verlag, Heidelberg

    Book  MATH  Google Scholar 

  34. Vokřínek J, Komenda A, Pěchouček M (2010) Agents towards vehicle routing problems. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems: volume 1, vol 1. AAMAS ’10, international foundation for autonomous agents and multiagent systems, Richland, SC, pp 773–780

  35. Warden T, Porzel R, Gehrke JD, Herzog O, Langer H, Malaka R (2010) Towards ontology-based multiagent simulations: the plasma approach. In: Bargiela A, Azam Ali S, Crowley D, Kerckhoffs EJH (eds) 24th European conference on modelling and simulation (ECMS 2010). European Council for Modelling and Simulation, pp 50–56

  36. Warden T, Wojtusiak J (2010) Learnable evolutionary optimization in autonomous pickup and delivery planning: a scenario, system architecture and initial results. TZI Technical Report 55, Center for Computing and Communication Technologies, Universität Bremen

  37. Wojtusiak J (2004) The LEM3 implementation of learnable evolution model: user’s guide. Reports of the machine learning and inference laboratory MLI 04–5, George Mason University, Fairfax, VA

  38. Wojtusiak J (2007) Handling constrained optimization problems and using constructive induction to improve representation spaces in learnable evolution model. PhD thesis, College of Science, George Mason University, Fairfax, VA

  39. Wojtusiak J (2008) Data-driven constructive induction in the learnable evolution model. In: Proceedings of the 16th international conference on intelligent information systems. Zakopane, Poland

  40. Wojtusiak J, Michalski R (2005) The LEM3 system for non-darwinian evolutionary computation and its application to complex function optimization Reports of the machine learning and inference laboratory MLI 05–2. George Mason University, Fairfax

    Google Scholar 

  41. Wojtusiak J, Michalski R (2006) The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems. In: Proceedings of genetic and evolutionary computation conference (GECCO 2006). Seattle, WA

  42. Wojtusiak J, Michalski R, Kaufman K, Pietrzykowski J (2006) The AQ21 natural induction program for pattern discovery: initial version and its novel features. In: Proceedings of the 18th IEEE internation conference on tools with artificial intelligence, Washington, DC. IEEE Computer Society, Los Alamitos, CA, pp 523–526

  43. Zeddini B, Temani M, Yassine A, Ghedira K (2008) An agent-oriented approach for the dynamic vehicle routing problem. In: Proceedings of the 2008 International Workshop on advanced information systems for enterprises, pp 70–76

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Authors and Affiliations

  1. Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA, USA

    Janusz Wojtusiak

  2. Center for Computing and Communication Technologies, University of Bremen, Bremen, Germany

    Tobias Warden & Otthein Herzog

Authors
  1. Janusz Wojtusiak
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  2. Tobias Warden
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  3. Otthein Herzog
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Correspondence to Janusz Wojtusiak.

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Wojtusiak, J., Warden, T. & Herzog, O. The learnable evolution model in agent-based delivery optimization. Memetic Comp. 4, 165–181 (2012). https://doi.org/10.1007/s12293-012-0088-9

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