Hybrid artificial intelligence approaches on vehicle routing problem in logistics distribution

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7208 LNAI, Issue PART 1, 2012, Pages 208-220

  Simić, Dragan ^a   Simić, Svetlana ^b  

^a UNIVERSITY OF NOVI SAD   (Serbia)

^b UNIVERSITY OF NOVI SAD   (Serbia)

Author keywords

artificial immune systems; bio inspired models; genetic algorithms; hybrid artificial intelligence; Logistics distribution; vehicle routing problem

Indexed keywords

ANT COLONY OPTIMIZATION (ACO); ARTIFICIAL IMMUNE SYSTEM; BIO-INSPIRED; CHAIN MANAGEMENT; EVOLUTIONARY COMPUTING; HYBRID APPROACH; LOGISTICS DISTRIBUTION; SELF-ORGANIZING SYSTEMS; SUPPLY CHAIN MANAGEMENT SYSTEM; VEHICLE ROUTING PROBLEM; VEHICLE ROUTING PROBLEMS;

GENETIC ALGORITHMS; IMMUNOLOGY; INTELLIGENT SYSTEMS; NETWORK ROUTING; SUPPLY CHAIN MANAGEMENT;

ROUTING ALGORITHMS;

EID: 84858766345     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-28942-2_19     Document Type: Conference Paper

References (39)

1. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Management Science 6(1), 80-91 (1959)
2. Abraham, A., Corchado, E., Corchado, J.M.: Hybrid Learning Machines. Neurocomputing 72(13-15), 2729-2730 (2009)
3. Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61-63 (2012)
4. Pedrycz, W., Aliev, R.: Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73(1-3), 10-23 (2009)
5. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044-2064 (2010)
6. Simić, D., Simić, S.: A review: Approach of fuzzy models applications in logistics. In: Burduk, R., Kurzyñski, M., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems 4. AISC, vol. 95, pp. 717-726. Springer, Heidelberg (2011)
7. Kudumovic, D., Mujevic, M., Sukic, C.: New trends and ideas in the application of multimodal transportation systems. Technics Technologies Education Management 6(2), 241-246 (2011)
8. Huanglan, C.C.: The mode of vehicle routing problem and the of brain power heuristic algorithm. Computer Development and Application 16, 2-5 (2003)
9. Laporte, G.: The vehicle routing problem: an overview of exact and approximate algorithms. European Journal of Operational Research 59, 345-358 (1992)
10. Rao, V.B.: Neural networks and fuzzy logic. M&T Books, IDG Books Worldwide (1995)
11. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B.: The traveling salesman problem: A guided tour of combinatorial optimization. Wiley (1985)
12. Eielst, H.A., Laporte, G.: A historical perspective on arc routing. In: Dror, M. (ed.) Arc Routing: Theory, Solutions and Applications. Springer, Berlin (2000)
13. Lin, S.: Computer solutions of travelling salesman problem. Bell System Technical Journal 44, 2245-2269 (1965)
14. Beyer, H.-G., Schwelef, H.-P.: Evolution strategies: A comprehensive introduction. Natural Computing 1(1), 3-52 (2002)
15. Homberger, J., Gehring, H.: Two evolutionary metaheuristics for the vehicle routingproblem with time windows. INFOR 37, 297-318 (1999)
16. Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)
17. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony cooperation agents. IEEE Transactions on Systems, Man and Cybernetics, Part 2 26(1), 1-13 (1996)
18. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computing 1(1), 53-66 (1997)
19. Stutzle, T., Hoos, H.H.: MAX-MIN ant system. Future Generation Computer System 16(8), 889-914 (2000)
20. Gambardella, L.M., Taillard, E.D., Agazzi, G.: MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 63-76. McGraw-Hill, London (1999)
21. Berger, J., Barkaoui, M., Braysys, O.: A route-direction hybrid genetic approach for vehicle routing problem with time windows. Information System and Operational Research 41, 179-194 (2003)
22. de Castro, L.N., Timmis, J.: Artificial immune systems: A new computational intelligence approach. Springer, London (2002)
23. Keko, H., Skok, M., Skrelec, D.: Solving the distribution network routing problem with artificial immune systems. In: Proceedings of the IEEE Mediterranean Electrotechnical Conference, pp. 959-962 (2004)
24. Ma, J., Zou, H., Gao, L.-Q., Li, D.: Immune genetic algorithm for vehicle routing problem with time windows. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp. 3465-3469 (2006)
25. Corchado, E., Abraham, A., de Carvalho, A.: Hybrid Intelligent Algorithms and Applications. Information Science 180(14), 2633-2634 (2010)
26. Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 105-144. Kluwer, Boston (2003)
27. Mester, D., Braysys, O.: Active guided evolution strategies for large scale vehicle routing problems with time windows. Computer & Operations Research 32, 1593-1614 (2005)
28. Voudouris, C., Tsanh, E.P.K.: Guided local search. Technical Report CSM-247. Department of Computer Sciences. University of Essex, Colchester, UK (1995)
29. Mester, D., Braysy, O., Dullaert, W.: A multi-parametric evolution strategies algorithm for vehicle routing problems. Expert Systems with Applications 34, 2964-2975 (2007)
30. Christofides, N., Mingozzi, A., Toth, P.: The vehicle routing problem. In: Christofides, N., Mingozzi, A., Toth, P. (eds.) Combinatorial Optimization, pp. 315-338. Wiley (1979)
31. Prakash, A., Deshmukh, S.G.: A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach. Expert Systems with Applications 38(4), 3199-3208 (2011)
32. Hajiaghaei-Keshteli, M.: The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches. Applied Soft Computing Journal 11(2), 2069-2078 (2011)
33. Wang, Y.J.: Improving particle swarm optimization performance with local search for high-dimensional function optimization. Optimization Methods and Software 25(5), 781-795 (2010)
34. Meeran, S., Morshed, M.S.: A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. Journal of Intelligent Manufacturing, doi:10.1007/s10845-011-0520-x
35. Wang, X., Gao, X.Z., Ovaska, S.J.: A hybrid artificial immune optimization method. International Journal of Computational Intelligence Systems 2(3), 249-256 (2009)
36. Guo, D., Wang, J., Huang, J., Han, R., Song, M.: Chaotic-NSGA-II: an effective algorithm to solve multi-objective optimization problems. In: Proceedings of the International Conference on Intelligent Computing and Integrated Systems (ICISS 2010), pp. 20-23 (2010)
37. Zhang, G.: Quantum-inspired evolutionary algorithms: a survey and empirical study. Journal of Heuristics 17(3), 303-351 (2010)
38. Tao, F., Zhang, L., Zhang, Z.H., Nee, A.Y.C.: A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise. Manufacturing Technology 59(1), 485-488 (2010)
39. Wozniak, M., Zmyslony, M.: Designing Fusers on the Basis of Discriminants - Evolutionary and Neural Methods of Training. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 590-597. Springer, Heidelberg (2010)