Classical Hybrid Approaches on a Transportation Problem with Gas Emissions Constraints

  • Camelia-M. Pintea
  • Petrica C. Pop
  • Mara Hajdu-Macelaru
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)


Nowadays the efforts of humanity to keep the planet safe are considerable. This aspect is reflected in everyday problems, including the minimization of vehicle’s air pollution. In order to keep a green planet, in particular transportation problems, the main purpose is on limiting the pollution with gas emissions. In a specific capacitated fixed-charge transportation problem with fixed capacities for distribution centers and customers with particular demands the objective is to keep the pollution factor in a given range while the total cost of the transportation is as low as possible. In order to solve this problem, we have developed some hybrid variants of the nearest neighbor classical approach. The proposed algorithms are tested and analyzed on a set of instances used in the literature. The preliminary results point out that our approaches are attractive and appropriate for solving the described transportation problem.


Hybrid heuristics Transportation Problem Optimization 


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  1. 1.
    Adlakha, V., Kowalski, K.: On the fixed-charge transportation problem. OMEGA: The Int.J.of Management Science 27, 381–388 (1999)CrossRefGoogle Scholar
  2. 2.
    Chira, C., Pintea, C.-M., Dumitrescu, D.: Sensitive Stigmergic Agent Systems: a Hybrid Approach to Combinatorial Optimization. In: Innovations in Hybrid Intelligent Systems, Advances in Soft Computing, vol. 44, pp. 33–39. Springer (2008)Google Scholar
  3. 3.
    Cost, S., Salzberg, S.: A weighted nearest neighbor algorithm for learning with sym-bolic features. Machine Learning 10, 57–78 (1993)Google Scholar
  4. 4.
    Deerwester, S., Dumals, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41, 391–407 (1990)CrossRefGoogle Scholar
  5. 5.
    Devroye, L., Wagner, T.J.: Nearest neighbor methods in discrimination. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, North-Holland (1982)Google Scholar
  6. 6.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI Press/MIT Press (1996)Google Scholar
  7. 7.
    Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic, Boston (1991)Google Scholar
  8. 8.
    Hitchcock, F.L.: The distribution of a product from several sources to numerous localities. J. of Mathematical Physic 20, 224–230 (1941)MathSciNetGoogle Scholar
  9. 9.
    Hugo, A., Pistikopoulos, E.: Environmentally conscious process planning under uncertainty. In: Floudas, C.A., Agrawal, R. (eds.) Sixth International Conference on Foundations of Computer Aided Process Design, CACHE Corporation, Princeton (2004)Google Scholar
  10. 10.
    Molla-Alizadeh-Zavardehi, S., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R.: Solving a capacitated fixed-charge transportation problem by artificial immune and genetic algorithms with a Prüfer number representation. Expert Systems with Applications 38, 10462–10474 (2011)CrossRefGoogle Scholar
  11. 11.
    Pintea, C.-M., Chira, C., Dumitrescu, D., Pop, P.C.: Sensitive Ants in Solving the Generalized Vehicle Routing Problem. Int. J. Comput. Commun. & Control VI(4), 731–738 (2011)Google Scholar
  12. 12.
    Pintea, C.-M., Sitar, C.P., Hajdu-Macelaru, M., Petrica, P.: A Hybrid Classical Approach to a Fixed-Charged Transportation Problem. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part III. LNCS, vol. 7208, pp. 557–566. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Pintea, C.-M.: Combinatorial optimization with bio-inspired computing, PhD Thesis, Babes-Bolyai University (2008)Google Scholar
  14. 14.
    Santibanez-Gonzalez, E., Del, R., Robson Mateus, G., Pacca Luna, H.: Solving a public sector sustainable supply chain problem: A Genetic Algorithm approach. In: Proc. of Int. Conf. of Artificial Intelligence (ICAI), Las Vegas, USA, pp. 507–512 (2011)Google Scholar
  15. 15.
    Seuring, S., Muller, M.: From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production 16, 1699–1710 (2008)CrossRefGoogle Scholar
  16. 16.
    Sun, M., Aronson, J.E., Mckeown, P.G., Drinka, D.: A tabu search heuristic proce-dure for the fixed charge transportation problem. European Journal of Operational Research 106, 441–456 (1998)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Camelia-M. Pintea
    • 1
  • Petrica C. Pop
    • 1
  • Mara Hajdu-Macelaru
    • 1
  1. 1.Tech Univ Cluj-NapocaNorth Univ Center Baia MareBaia-MareRomania

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