Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 823876, 10 pages
http://dx.doi.org/10.1155/2013/823876
Research Article

A Multiobjective Optimization Model in Automotive Supply Chain Networks

1Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Received 5 February 2013; Revised 10 June 2013; Accepted 5 August 2013

Academic Editor: Bijaya Panigrahi

Copyright © 2013 Abdolhossein Sadrnia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Xi, X. Ding, D. Fu, L. Zhou, and K. Liu, “Regional Δ14C patterns and fossil fuel derived CO2 distribution in the Beijing area using annual plants,” Chinese Science Bulletin, vol. 56, no. 16, pp. 1721–1726, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. S. C. Davis, S. W. Diegel, R. G. Boundy, Engineering ORNL, Division TS, and Program VT, Transportation Energy Data Book, Oak Ridge National Laboratory, 29th edition, 2010.
  3. S. Elhedhli and R. Merrick, “Green supply chain network design to reduce carbon emissions,” Transportation Research D, vol. 17, no. 5, pp. 370–379, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. J. F. Cordeau, F. Pasin, and M. M. Solomon, “An integrated model for logistics network design,” Annals of Operations Research, vol. 144, pp. 59–82, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  5. E. H. Sabri and B. M. Beamon, “A multi-objective approach to simultaneous strategic and operational planning in supply chain design,” Omega, vol. 28, no. 5, pp. 581–598, 2000. View at Google Scholar · View at Scopus
  6. P. Georgiadis, D. Vlachos, and E. Iakovou, “A system dynamics modeling framework for the strategic supply chain management of food chains,” Journal of Food Engineering, vol. 70, no. 3, pp. 351–364, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Ge, J. B. Yang, N. Proudlove, and M. Spring, “System dynamics modelling for supply chain management: a case study on a supermarket chain in the UK,” International Transactions in Operational Research, vol. 11, no. 5, pp. 495–509, 2004. View at Publisher · View at Google Scholar
  8. F. Du and G. W. Evans, “A bi-objective reverse logistics network analysis for post-sale service,” Computers and Operations Research, vol. 35, no. 8, pp. 2617–2634, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. B. M. Beamon and C. Fernandes, “Supply-chain network configuration for product recovery,” Production Planning and Control, vol. 15, no. 3, pp. 270–281, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Chopra, “Designing the distribution network in a supply chain,” Transportation Research E, vol. 39, no. 2, pp. 123–140, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. D. Peidro, J. Mula, R. Poler, and F. C. Lario, “Quantitative models for supply chain planning under uncertainty,” International Journal of Advanced Manufacturing Technology, vol. 43, no. 3-4, pp. 400–420, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. N. S. Kim, M. Janic, and B. van Wee, “Trade-off between carbon dioxide emissions and logistics costs based on multiobjective optimization,” Transportation Research Record, no. 2139, pp. 107–116, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Schultmann, M. Zumkeller, and O. Rentz, “Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry,” European Journal of Operational Research, vol. 171, no. 3, pp. 1033–1050, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. R. J. Merrick and J. H. Bookbinder, “Environmental assessment of shipment release policies,” International Journal of Physical Distribution and Logistics Management, vol. 40, no. 10, pp. 748–762, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. E. L. Plambeck, “Reducing greenhouse gas emissions through operations and supply chain management,” Energy Economics, vol. 34, supplement 1, pp. S64–S74, 2012. View at Publisher · View at Google Scholar
  16. C. Reich-Weiser and D. A. Dornfeld, “A discussion of greenhouse gas emission tradeoffs and water scarcity within the supply chain,” Journal of Manufacturing Systems, vol. 28, no. 1, pp. 23–27, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Melkote and M. S. Daskin, “Capacitated facility location/network design problems,” European Journal of Operational Research, vol. 129, no. 3, pp. 481–495, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  18. T. Aslam, P. Hedenstierna, A. H. C. Ng, L. Wang, and K. Deb, “Multi-objective optimisation in manufacturing supply chain systems design: a comprehensive survey and new directions,” in Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 35–70, Springer, London, UK, 2011. View at Google Scholar
  19. R. Sun, X. Wang, and G. Zhao, “An ant colony optimization approach to multi-objective supply chain model,” in Proceedings of the 2nd IEEE International Conference on Secure System Integration and Reliability Improvement (SSIRI '08), pp. 193–194, Yokohama, Japan, July 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. R. K. Pati, P. Vrat, and P. Kumar, “A goal programming model for paper recycling system,” Omega, vol. 36, no. 3, pp. 405–417, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Bouzembrak, H. Allaoui, G. Goncalves, and H. Bouchriha, “A multi-objective green supply chain network design,” in Proceedings of the 4th International Conference on Logistics (LOGISTIQUA '2011), pp. 357–361, Hammamet, Tunisia, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Amiri, “Designing a distribution network in a supply chain system: formulation and efficient solution procedure,” European Journal of Operational Research, vol. 171, no. 2, pp. 567–576, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. V. V. Kumar, F. T. S. Chan, N. Mishra, and V. Kumar, “Environmental integrated closed loop logistics model: an artificial bee colony approach,” in Proceedings of the 8th International Conference on Supply Chain Management and Information Systems (SCMIS '10), pp. 1–7, October 2010. View at Scopus
  24. M. S. Pishvaee, K. Kianfar, and B. Karimi, “Reverse logistics network design using simulated annealing,” International Journal of Advanced Manufacturing Technology, vol. 47, no. 1–4, pp. 269–281, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Dorigo and G. Di Caro, “Ant colony optimization: a new meta-heuristic,” in Proceedings of the Congress on Evolutionary Computation (CEC '99), vol. 2, Washington, DC, USA, 1999. View at Publisher · View at Google Scholar
  27. M. Dorigo and T. Stützle, “The ant colony optimization metaheuristic: algorithms, applications, and advances,” in Handbook of Metaheuristics, pp. 250–285, 2003. View at Google Scholar
  28. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Wash, USA, December 1995. View at Scopus
  29. M. Gen and R. Cheng, Genetic Algorithms and Engineering Design, John Wiley & Sons, New York, NY, USA, 2007.
  30. S. Das and B. K. Panigrahi :, “Multi-objective evolutionary algorithms,” in Encyclopedia of Artificial Intelligence, vol. 3, pp. 1145–1151, 2009. View at Google Scholar
  31. S. Mondal, A. Bhattacharya, and S. H. N. Dey, “Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration,” International Journal of Electrical Power and Energy Systems, vol. 44, no. 1, pp. 282–292, 2013. View at Publisher · View at Google Scholar
  32. F. Altiparmak, M. Gen, L. Lin, and T. Paksoy, “A genetic algorithm approach for multi-objective optimization of supply chain networks,” Computers and Industrial Engineering, vol. 51, no. 1, pp. 196–215, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. M. S. Pishvaee, R. Z. Farahani, and W. Dullaert, “A memetic algorithm for bi-objective integrated forward/reverse logistics network design,” Computers and Operations Research, vol. 37, no. 6, pp. 1100–1112, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Alçada-Almeida, J. Coutinho-Rodrigues, and J. Current, “A multiobjective modeling approach to locating incinerators,” Socio-Economic Planning Sciences, vol. 43, no. 2, pp. 111–120, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. T. Paksoya, E. Ozceylana, and G. W. Weberb, “A multi objective model for optimization of a green supply chain network,” Global Journal of Technology and Optimization, vol. 2, pp. 84–96, 2011. View at Google Scholar
  36. F. Wang, X. Lai, and N. Shi, “A multi-objective optimization for green supply chain network design,” Decision Support Systems, vol. 51, no. 2, pp. 262–269, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. R. Dekker, J. Bloemhof, and I. Mallidis, “Operations research for green logistics—an overview of aspects, issues, contributions and challenges,” European Journal of Operational Research, vol. 219, no. 3, pp. 671–679, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232–2248, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “Filter modeling using gravitational search algorithm,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 117–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. M. Khajehzadeh, M. R. Taha, A. El-Shafie, and M. Eslami, “A modified gravitational search algorithm for slope stability analysis,” Engineering Applications of Artificial Intelligence, vol. 25, no. 8, pp. 1589–1597, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Ojha, K. Deep, A. Nagar, M. Pant, and J. C. Bansal, “Optimizing supply chain management using gravitational search algorithm and multi agent system,” in Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS '11) December 20–22, 2011, vol. 130 of Advances in Intelligent and Soft Computing, pp. 481–491, Springer, Berlin, Germany, 2012. View at Google Scholar
  42. C. Li and J. Zhou, “Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm,” Energy Conversion and Management, vol. 52, no. 1, pp. 374–381, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. H. R. Hassanzadeh and M. Rouhani, “A multi-objective gravitational search algorithm,” in Proceedings of the 2nd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN '10), pp. 7–12, Liverpool, UK, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. T. Ganesan, I. Elamvazuthi, K. Z. K. Shaari, and P. Vasant, “Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production,” Applied Energy, vol. 103, pp. 368–374, 2013. View at Publisher · View at Google Scholar
  45. M. Arsuaga-Ros, M. A. Vega-Rodrguez, and F. Prieto-Castrillo, “Meta-schedulers for grid computing based on multi-objective swarm algorithms,” Applied Soft Computing Journal, vol. 13, no. 4, pp. 1567–1582, 2013. View at Publisher · View at Google Scholar
  46. T. Back, D. B. Fogel, and Z. Michalewicz, Handbook of Evolutionary Computation, IOP Publishers, Bristol, UK, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  47. L. Cagnina, S. Esquivel, and C. A. C. Coello, “A particle swarm optimizer for multi-objective optimization,” Journal of Computer Science and Technology, vol. 5, pp. 204–210, 2005. View at Google Scholar