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Design of Multi-Stage Manufacturing Networks for Personalized Products Using Metaheuristics

  • D. Mourtzis
  • M. Doukas
  • F. Psarommatis
  • N. Panopoulos
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Manufacturers are nowadays highly affected by the ever-increasing number of product variants, under the product personalization trend. The large number of cooperating manufacturing network partners leads to enormous search spaces of alternative manufacturing network configurations. This obstructs effective decision-making towards configuring efficient network structures, a nonetheless crucial decision for a company. Exact methods guarantee that the identified solution is the optimum, with regards to the objectives set in the specified problem. However, in real life cases the magnitude of the solution space is such that these methods cannot be utilized due to computational constraints. For tackling such NP-hard problems, meta-heuristics can be utilized that provide a trade-off between the quality of solution and the computation time. This research work describes the modeling and solving of a manufacturing network design problem using the meta-heuristic methods of simulated annealing and tabu search. The quality of the results identified by these methods is compared with the results obtained from an intelligent search algorithm and an exhaustive enumerative method, which are implemented into a web-based platform for the design and planning of manufacturing networks. The approach is validated through its application to a real life case study with data acquired from the automotive industry.

Keywords

Simulated annealing Tabu search Manufacturing network design Decision-making Metaheuristics 

Notes

Acknowledgments

The work reported in this paper has been partially supported by the EC-funded project “e-CUSTOM—A web-based collaboration system for mass customization” (260067).

References

  1. 1.
    Fogliatto FS, Da Silveira GJC, Borenstein D (2012) The mass customization decade: an updated review of the literature. Int J Prod Econ 138(1):14–25CrossRefGoogle Scholar
  2. 2.
    Yao J, Liu L (2009) Optimization analysis of supply chain scheduling in mass customization. Int J Prod Econ 117(1):197–211CrossRefGoogle Scholar
  3. 3.
    Papakostas N, Efthymiou K, Georgoulias K, Chryssolouris G (2012) On the configuration and planning of dynamic manufacturing networks. Logistics Res 5(3–4) 105–111, SpringerGoogle Scholar
  4. 4.
    Papageorgiou LG (2009) Supply chain optimisation for the process industries: advances and opportunities. Comput Chem Eng 56(4):1205–1215Google Scholar
  5. 5.
    Mastrolilli M, Blum C (2010) On the use of different types of knowledge in metaheuristics based on constructing solutions. J Eng Appl Artif Intell 23(5):650–659CrossRefGoogle Scholar
  6. 6.
    Mansouri AS (2006) A simulated annealing approach to a bi-criteria sequencing problem in a two-stage supply chain. Comput Ind Eng 50(1–2):105–119CrossRefGoogle Scholar
  7. 7.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Glover F (1989) Tabu search. INFORMS J Comput Summer 1(3):190–206zbMATHCrossRefGoogle Scholar
  9. 9.
    De Boer PT, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19–67MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArborGoogle Scholar
  11. 11.
    Beyr HG, Schwefel HP (2002) Evolution strategies. Nat Comput 1:3–52MathSciNetCrossRefGoogle Scholar
  12. 12.
    Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. Elsevier Publishing, Paris, pp 134–142Google Scholar
  13. 13.
    Kennedy J (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4. pp 1942–1948Google Scholar
  14. 14.
    Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Global Optim 6:109–133MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Maniezzo V, Carbonaro A (1999) An ANT heuristic for the frequency assignment problem. Future Generation Comput Syst 16(8):927–935 Google Scholar
  16. 16.
    Blum C (2005) Beam-ACO: hybridizing ant colony optimization with beam search: an application to open shop scheduling. J Comput Oper Res 32(6):1565–1591CrossRefGoogle Scholar
  17. 17.
    Keskin BB, Ulster H (2007) Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem. Eur J Oper Res 182:663–682zbMATHCrossRefGoogle Scholar
  18. 18.
    Armentano VA, Shiguemoto AL, Løkketangen A (2011) Tabu search with path relinking for an integrated production–distribution problem. Comput Oper Res 38(8):1199–1209MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Melo MT, Nickel S, Saldanha-da-Gama F (2012) A tabu search heuristic for redesigning a multi-echelon supply chain network over a planning horizon. Int J Prod Econ 136(1):218–230CrossRefGoogle Scholar
  20. 20.
    Cerqueti R, Falbo P, Guastaroba G, Pelizzari C (2012) A Tabu search heuristic procedure in markov chain bootstrapping. Eur J Oper Res. Available online 26 Nov 2012Google Scholar
  21. 21.
    Jayaraman V, Ross A (2003) A simulated annealing methodology to distribution network design and management. Eur J Oper Res 144(3):629–645MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Taheri J, Zomaya AY (2005) A simulated annealing approach for mobile location management. In: Proceedings of the 19th IEEE international symposium on parallel and distributed processing, p 194Google Scholar
  23. 23.
    Martins C, Pinto-Varela T, Barbósa-Póvoa AP, Novais AQ (2012) A simulated annealing algorithm for the design and planning of supply chains with economic and environmental objectives. Comput Aided Chem Eng 30:21–25CrossRefGoogle Scholar
  24. 24.
    Subramanian P, Ramkumar N, Narendran TT, Ganesh K (2013) PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem. Appl Soft Comput 13(2):1121–1135CrossRefGoogle Scholar
  25. 25.
    Arostegui MA Jr, Kadipasaoglu SN, Khumawala BM (2006) An empirical comparison of Tabu search, simulated annealing, and genetic algorithms for facilities location problems. Int J Prod Econ 103:742–754CrossRefGoogle Scholar
  26. 26.
    Zhou G, Min H, Gen M (2002) The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Comput Ind Eng 43(1–2):251–261CrossRefGoogle Scholar
  27. 27.
    Chryssolouris G (2006) Manufacturing systems: theory and practice, 2nd edn. Springer, New YorkGoogle Scholar
  28. 28.
    Mourtzis D, Doukas M, Psarommatis F (2013) Environmental impact of centralised and decentralised production networks in the era of personalisation. In: Windt K (ed) Robust manufacturing control. Springer, Berlin, ISBN 978-3-642-30748-5, Chapter 27, DOI: 10.1007/978-3-642-30749-2_26Google Scholar
  29. 29.
    Mourtzis D, Doukas M, Psarommatis F (2012) A multi-criteria evaluation of centralized and decentralized production networks in a highly customer-driven environment. CIRP Ann-Manuf Technol 61(1):427–430CrossRefGoogle Scholar
  30. 30.
    Chryssolouris G, Dicke K, Lee M (1992) On the resources allocation problem. Int J Prod Res 30(12):2773–2795zbMATHCrossRefGoogle Scholar
  31. 31.
    Michalos G, Makris S, Mourtzis D (2012) An intelligent search algorithm-based method to derive assembly line design alternatives. Int J Comput Integr Manuf 25(3):211–229CrossRefGoogle Scholar
  32. 32.
    Michalos G, Makris S, Mourtzis D (2011) A web based tool for dynamic job rotation scheduling using multiple criteria. CIRP Ann-Manuf Technol 60(1):453–456CrossRefGoogle Scholar
  33. 33.
    Milani A, Shanian A, Madoliat R, Nemes J (2005) The effect of normalisation norms in multiple attribute decision making models: a case study in gear material selection. Struct Multi Optim 29:312–318CrossRefGoogle Scholar
  34. 34.
    Mourtzis D, Doukas M, Psarommatis F (2012) Design and planning of decentralised production networks under high product variety demand. In: Proceedings of Procedia CIRP, 45th CIRP conference on manufacturing systems 2012, vol 3. pp 293–298, 2012Google Scholar
  35. 35.
    EPA (2010) URL: www.epa.gov
  36. 36.
    Evans JR (1993) Applied production and operations management, 4th edn. West Publishing Company, St. PaulGoogle Scholar
  37. 37.
    Phadke MS (1989) Quality engineering using robust design, 1st edn. Englewood Cliffs, Prentice HallGoogle Scholar
  38. 38.
    Mourtzis D, Doukas M, Psarommatis F (2012) An intelligent multi criteria method for the design of manufacturing networks in a mass customisation environment. Int J Prod Res Under ReviewGoogle Scholar
  39. 39.
    Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • D. Mourtzis
    • 1
  • M. Doukas
    • 1
  • F. Psarommatis
    • 1
  • N. Panopoulos
    • 1
  1. 1.Department of Mechanical Engineering and Aeronautics, Laboratory for Manufacturing Systems and AutomationUniversity of PatrasRio-PatrasGreece

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