Swarm Intelligence Supported e-Remanufacturing

  • Bo Xing
  • Wen-Jing Gao
  • Fulufhelo V. Nelwamondo
  • Kimberly Battle
  • Tshilidzi Marwala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)


e-Remanufacturing has nowadays become a superior option for product recovery management system. So far, many different approaches have been followed in order to increase the efficiency of remanufacturing process. Swarm intelligence (SI), a relatively new bio-inspired family of methods, seeks inspiration in the behavior of swarms of insects or other animals. After applied in other fields with success, SI started to gather the interest of researchers working in the field of remanufacturing. In this paper we provide a survey of SI methods that have been used in e-remanufacturing.


swarm intelligence (SI) ant colony optimization (ACO) artificial bee colony (ABC) particle swarm optimization (PSO) artificial immune system (AIS) e-remanufacturing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zwolinski, P., Lopez-Ontiveros, M.-A., Brissaud, D.: Integrated design of remanufacturable products based on product profiles. J. of Cleaner Production 14, 1333–1345 (2006)CrossRefGoogle Scholar
  2. 2.
    Atzori, L., Iera, A., Morabito, G.: The Internet of things: a survey. Computer Networks 54, 2787–2805 (2010)zbMATHCrossRefGoogle Scholar
  3. 3.
    Ding, N.N.: Data gathering and communication for wireless sensor networks using ant colony optimization, MSc Thesis, in Department of System and Computer Engineering, Carleton University (2005)Google Scholar
  4. 4.
    Huo, H., Gao, D., Niu, Y., Gao, S.: ASDP: An Action-Based Service Discovery Protocol Using Ant Colony Algorithm in Wireless Sensor Networks. In: Zhang, H., Olariu, S., Cao, J., Johnson, D.B. (eds.) MSN 2007. LNCS, vol. 4864, pp. 338–349. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Wang, X., Li, Q., Xiong, N., Pan, Y.: Ant Colony Optimization-Based Location-Aware Routing for Wireless Sensor Networks. In: Li, Y., Huynh, D.T., Das, S.K., Du, D.-Z. (eds.) WASA 2008. LNCS, vol. 5258, pp. 109–120. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Iyengar, S.S., et al.: Biologically inspired cooperative routing for wireless mobile sensor networks. IEEE Systems Journal 1(1), 29–37 (2007)CrossRefGoogle Scholar
  7. 7.
    Saleem, M., Farooq, M.: BeeSensor: A Bee-Inspired Power Aware Routing Protocol for Wireless Sensor Networks. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 81–90. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Saleem, M., Caro, G.A.D., Farooq, M.: Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Information Sciences 181, 4597–4624 (2011)CrossRefGoogle Scholar
  9. 9.
    Mármol, F.G., Pérez, G.M.: Providing trust in wireless sensor networks using a bio-inspired technique. Telecommun. Syst. (2010), doi:10.1007/s11235-010-9281-7Google Scholar
  10. 10.
    Kolias, C., Kambourakis, G., Maragoudakis, M.: Swarm intelligence in intrusion detection: a survey. Computers & Security 30, 625–642 (2011)CrossRefGoogle Scholar
  11. 11.
    Konstantaras, I., Skouri, K., Jaber, M.Y.: Lot sizing for a recoverable product with inspection and sorting. Computers & Industrial Engineering 58, 452–462 (2010)CrossRefGoogle Scholar
  12. 12.
    De Backer, M., Haesen, R., Martens, D., Baesens, B.: A Stigmergy Based Approach to Data Mining. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 975–978. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Özbakır, L., et al.: TACO-miner: an ant colony based algorithm for rule extraction from trained neural networks. Expert Systems with Applications 36, 12295–12305 (2009)CrossRefGoogle Scholar
  14. 14.
    Xie, L., Mei, H.: The application of the ant colony decision rule algorithm on distributed data mining. Communications of the IIMA 7(4), 85–94 (2007)Google Scholar
  15. 15.
    Marwala, T.: Computational intelligence for missing data imputation, estimation and management: knowledge optimization techniques. IGI Global Publications, Information Science Reference Imprint, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Grosan, C., Abraham, A., Chis, M.: Swarm intelligence in data mining. SCI, vol. 34, pp. 1–20. Springer, Heidelberg (2006)zbMATHCrossRefGoogle Scholar
  17. 17.
    Shibeshi, A.G.: Ant based logistics self-organizaiton: starting fundamentals and research design. In: Faculty of Technology, Policy and Management (2009)Google Scholar
  18. 18.
    Silva, C.A., et al.: Distributed supply chain management using ant colony optimization. European J. of Operational Research 199, 349–358 (2009)zbMATHCrossRefGoogle Scholar
  19. 19.
    Wang, K.-J., Chen, M.-J.: Cooperative capacity planning and resource allocation by mutual outsourcing using ant algorithm in a decentralized supply chain. Expert Systems with Applications 36, 2831–2842 (2009)CrossRefGoogle Scholar
  20. 20.
    Fang, F., Wong, T.N.: Applying hybrid case-based reasoning in agent-based negotiations for supply chain management. Expert Systems with Applications 37, 8322–8332 (2010)CrossRefGoogle Scholar
  21. 21.
    Robu, V., et al.: A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Systems with Applications 38, 3483–3491 (2011)CrossRefGoogle Scholar
  22. 22.
    Chen, K.-H., Su, C.-T.: Activity assigning of fourth party logistics by particle swarm optimization-based preemptive fuzzy integer goal programming. Expert Systems with Applications 37, 3630–3637 (2010)CrossRefGoogle Scholar
  23. 23.
    Coutee, A.S.: Virtual assembly and disassembly analysis: an exploration into virtual object interactions and haptic feedback, in Mechanical Engineering. Georgia Institute of Technology (2004)Google Scholar
  24. 24.
    Sharma, S., et al.: Generation of optimized robotic assembly sequence using ant colony optimization. In: Proceedings of the 4th IEEE Conference on Automation Science and Engineering. Key Bridge Marriott. IEEE, Washington DC (2008)Google Scholar
  25. 25.
    Vilarinho, P.M., Simaria, A.S.: ANTBAL: an ant colony optimization algorithm for balancing mixed-model assembly lines with parallel workstations. Int. J. of Production Research 44(2), 291–303 (2006)zbMATHCrossRefGoogle Scholar
  26. 26.
    Chica, M., et al.: A new diversity induction mechanism for a multi-objective ant colony algorithm to solve a real-world time and space assembly line balancing problem. Memetic. Comp. (2010), doi:10.1007/s12293-010-0035-6Google Scholar
  27. 27.
    Ilgin, M.A., Gupta, S.M.: Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line. Robotics and Computer-Integrated Manufacturing 27, 318–334 (2011)CrossRefGoogle Scholar
  28. 28.
    Lewis, A., Randall, M., Galehdar, A., Thiel, D., Weis, G.: Using Ant Colony Optimisation to Construct Meander-Line RFID Antennas. In: Lewis, A., Mostaghim, S., Randall, M. (eds.) Biologically-Inspired Optimisation Methods. SCI, vol. 210, pp. 189–217. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  29. 29.
    Krishna, A.G., Rao, K.M.: Optimisation of operations sequence in CAPP using an ant colony algorithm. Int. J. Adv. Manuf. Technol. 29, 159–164 (2006)CrossRefGoogle Scholar
  30. 30.
    Mahdavi, I., et al.: P-ACO approach to assignment problem in FMSs. World Academy of Science, Engineering and Technology 42, 196–203 (2008)Google Scholar
  31. 31.
    Jerald, J., et al.: Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm. Int. J. Adv. Manuf. Technol. 25, 964–971 (2005)CrossRefGoogle Scholar
  32. 32.
    Li, L., Qiao, F., Wu, Q.: ACO-Based Scheduling of Parallel Batch Processing Machines with Incompatible Job Families to Minimize Total Weighted Tardiness. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 219–226. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  33. 33.
    Gupta, S., Koulamas, C., Kyparisis, G.J.: e-Business: a review of research published in Production and Operations Management (1992-2008). Production and Operations Management 18(6), 604–620 (2009)CrossRefGoogle Scholar
  34. 34.
    Li, S.G., Rong, Y.L.: The research of online price quotation for the automobile parts exchange programme. Int. J. Computer Integrated Manufacturing 22(3), 245–256 (2009)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Franken, C.J.: PSO-based coevolutionary game learning. In: Faculty of Engineering, Built-Environment and Information Technology, University of Pretoria, Pretoria (2004)Google Scholar
  36. 36.
    Wu, Y., McCall, J., Corne, D.: Two novel ant colony optimization approaches for Bayesian network structure learning. In: Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2010 ). CCIB. IEEE, Barcelona (2010)Google Scholar
  37. 37.
    Wong, L.-H., Looi, C.-K.: Adaptable learning pathway generation with ant colony optimization. Educational Technology & Society 12(3), 309–326 (2009)Google Scholar
  38. 38.
    Wijaya, S., et al.: Web 2.0-based webstrategies for three different types of organizations. Computers in Human Behavior (2010), doi:10.1016/j.chb.2010.07.041Google Scholar
  39. 39.
    Kouzas, G., Kayafas, E., Loumos, V.: Ant Seeker: An Algorithm for Enhanced Web Search. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations. IFIP, vol. 204, pp. 649–656. Springer, Boston (2006)CrossRefGoogle Scholar
  40. 40.
    Boryczka, U., Polak, I.: Cumulation of Pheromone Values in Web Searching Algorithm. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 515–522. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  41. 41.
    Hu, K.-Y., Chang, T.-S.: An innovative automated storage and retrieval system for B2C e-commerce logistics. Int. J. Adv. Manuf. Technol. (2009), doi:10.1007/s00170-009-2292-4Google Scholar
  42. 42.
    Xing, B., et al.: Ant colony optimization for automated storage and retrieval system. In: Proceedings of The Annual IEEE Congress on Evolutionary Computation (IEEE CEC 2010). CCIB. IEEE, Barcelona (2010)Google Scholar
  43. 43.
    Mak, K.L., Lau, P.S.K.: Order pickings in an AS/RS with multiple I/O stations using an artificial immune system with aging antibodies. Engineering Letters 16(1) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bo Xing
    • 1
  • Wen-Jing Gao
    • 1
  • Fulufhelo V. Nelwamondo
    • 1
  • Kimberly Battle
    • 1
  • Tshilidzi Marwala
    • 1
  1. 1.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations