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Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

  • Afshin Mehrsai
  • Hamid-Reza Karimi
  • Bernd Scholz-Reiter
Original Article

Abstract

Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks’ structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central monitoring and controlling, show some limitations for organizing every operation on time or in the right time. Therefore, in the recent decade, a great attention is put on decentralized control and, to some extent, autonomy. This paper tries to investigate the possibility of combining this new research paradigm with existing strategies in production logistics, in order to improve material handling and control task according to material flow criteria. To show this, an exemplary plant after decoupling point out of a logistic network is considered for simulation and analysis. This combines Conwip system with learning autonomous pallets’ concept in a discrete event simulation model. Several decentralized control scenarios are experimented and compared together. Here, the learn methodology is brought to pallets based on fuzzy rules and advantage of closed loop systems.

Keywords

Learning pallets Autonomous control Fuzzy system Conwip 

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References

  1. 1.
    Stump B, Badurdeen F (2009) Integrating lean and other strategies for mass customization manufacturing: a case study. J Intell Manuf 23:109–124. doi: 10.1007/s10845-009-0289-3 CrossRefGoogle Scholar
  2. 2.
    Rupp T, Ristic M (2000) Fine planning for supply chains in semiconductor manufacture. J Mater Process Technol 107:390–397. doi: 10.1016/S0924-0136(00)00724-X CrossRefGoogle Scholar
  3. 3.
    Karageorgos A, Mehandjiev N, Weichhart G, H\(\tilde{\rm A}\)d’mmerle A (2003) Agent-based optimisation of logistics and production planning. Eng Appl Artificial Intell 16:335–348. doi: 10.1016/S0952-1976(03)00076-9 CrossRefGoogle Scholar
  4. 4.
    Scholz-Reiter B, Windt K et al (2004) New concepts of modelling and evaluating autonomous logistic processes. In: Chryssolouris G, Mourtzis D (eds) Manufacturing, modelling, management and control, 1st edn. Elsevier, Oxford, pp 37–46Google Scholar
  5. 5.
    Scholz-Reiter B, Mehrsai A (2009) Integration of lean-agile experiments with autonomy in supply chains. In: Proceedings of the 7th international conference on manufacturing research (ICMR09) 60–66. CD-ROMGoogle Scholar
  6. 6.
    Sanchez R (1998) Uncertainty, flexibility, and economic organization: foundations for an options theory of the firm. University of Western Australia. http://www.druid.dk/conferences/summer1998/conf-papers/sanchez.pdf. Accessed 25 Oct 2010
  7. 7.
    Yang B, Burns N D, Backhouse C (2004) Postponement: a review and an integrated framework. Int J Oper Production Manag (IJOPM) 24:468–487. doi: 10.1108/01443570410532542 CrossRefGoogle Scholar
  8. 8.
    Graves R, Konopka J, Milne R (1995) Literature review of material flow control mechanisms. Prod Plann Control 6:395–403. doi: 10.1080/09537289508930296 CrossRefGoogle Scholar
  9. 9.
    Fernandes N, do Carmo-Silva S (2006) Generic POLCA—a production and materials flow control mechanism for quick response manufacturing. Int J Prod Econ 104:74–84. doi: 10.1016/j.ijpe.2005.07.003 CrossRefGoogle Scholar
  10. 10.
    Newman W, Sridharan V (1995) Linking manufacturing planning and control to the manufacturing environment. Integrated Manuf Syst 6:36–42. doi: 10.1108/09576069510088952 CrossRefGoogle Scholar
  11. 11.
    Scholz-Reiter B, Mehrsai A, Görges M (2009) Handling dynamics in logistics-adoption of dynamic behaviour and reduction of dynamic effects. Asian Int J Sci Technol Prod Manuf Eng (AIJSTPME) 2:99–110Google Scholar
  12. 12.
    Trinh T, Kachitvichyanukul V (2007) Event graph models for generic manufacturing systems with push and pull policies. Comm Comput Inf Sci 5:1–11. doi: 10.1007/978-3-540-77600-0_1 CrossRefGoogle Scholar
  13. 13.
    Papadopoulou T, Mousavi A (2008) Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation. In: Proceeding of the 6th international conference on manufacturing research (ICMR08) Brunel University. http://hdl.handle.net/2438/2677. Accessed 20 Oct 2010
  14. 14.
    Spearman ML, Woodruff DL, Hopp WJ (1990) CONWIP: a pull alternative to kanban. Int J Prod Res 28:879–894CrossRefGoogle Scholar
  15. 15.
    Zhang Z, Gershwin SB (2006) Modeling and analysis of manufacturing systems with multiple-loop structures. DSpace@MIT. http://hdl.handle.net/1721.1/29836. Accessed 25 Oct 2010
  16. 16.
    Sakawa M, Kubota R (2000) Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy due date through genetic algorithms. Eur J Oper Res 120:393–407MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Gupta A, Maranas C (2003) Managing demand uncertainty in supply chain planning. Comput Chem Eng 27:1219–1227CrossRefGoogle Scholar
  18. 18.
    Sevastjanov P, Róg P (2003) Fuzzy modeling of manufacturing and logistic systems. Math Comput Simul 63:569–585zbMATHCrossRefGoogle Scholar
  19. 19.
    Mula J, Poler R, Garcia-Sabater JP, Lario FC (2006) Models for production planning under uncertainty: a review. Int J Prod Econ 103:271–285. doi: 10.1016/j.ijpe.2005.09.001 CrossRefGoogle Scholar
  20. 20.
    Nagy Z, Braatz R (2004) Open-loop and closed-loop robust optimal control of batch processes using distributional and worst-case analysis. J Process Control 14:411–422CrossRefGoogle Scholar
  21. 21.
    Shi J, Zhang G, Sha J (2011) Optimal production planning for a multi-product closed loop system with uncertain demand and return. Comput Oper Res 38:641–650MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Kogan K (2009) Production control under uncertainty: closed-loop versus open-loop approach. IIE Trans 41:905–915CrossRefGoogle Scholar
  23. 23.
    Krishnamurthy A, Suri R, Vernon M (2000) A new approach for analyzing queueing models of material control strategies in manufacturing systems. In: Proceedings 4th int. workshop on queueing networks with finite capacity (QNETs2000). CiteSeerX Beta. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.80.5448&rep=rep1&type=pdf. Accessed 26 Oct 2010
  24. 24.
    Duenyas I, Hopp W (1990) Estimating variance of output from cyclic exponential queueing systems. Queueing Syst 7:337–353MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Levantesi R (2001) Analysis of multiple loop assembly/disassembly networks. Dissertation, University of Politecnico di MilanoGoogle Scholar
  26. 26.
    Resano Lázaro A, Luis Pérez CJ (2008) Analysis of an automobile assembly line as a network of closed loops working in both, stationary and transitory regimes. Int J Prod Res 46:4803–4825zbMATHCrossRefGoogle Scholar
  27. 27.
    Resano Lázaro A, Luis Pérez CJ (2009) Dynamic analysis of an automobile assembly line considering starving and blocking. Robot Comput-Integrated Manuf 25:271–279CrossRefGoogle Scholar
  28. 28.
    Gershwin S, Werner L (2007) An approximate analytical method for evaluating the performance of closed-loop flow systems with unreliable machines and finite buffers. Int J Prod Res 45:3085–3112zbMATHCrossRefGoogle Scholar
  29. 29.
    Helber S, Schimmelpfeng K, Stolletz R (2009) Setting inventory levels of CONWIP ow lines via linear programming. Diskussionspapiere der Wirtschaftswissenschaftlichen Fakult\(\tilde{\rm A}\)d’t der Universit\(\tilde{\rm A}\)d’t Hannover. www.wiwi.uni-hannover.de/Forschung/Diskussionspapiere/dp-436.pdf. Accessed 1 Nov 2010
  30. 30.
    Ip WH et al (2007) CONWIP based control of a lamp assembly production line. J Intell Manuf 18:261–272MathSciNetCrossRefGoogle Scholar
  31. 31.
    O’Dell T (2004) A closed-loop system for the measurement of self-heating in BJTs. Solid-State Electron 48:167–170MathSciNetCrossRefGoogle Scholar
  32. 32.
    Rowley C, Batten B (2009) Dynamic and closed-loop control, in fundamentals and applications of modern flow control. In: Joslin RD, Miller D (eds) 231, Progress in astronautics and aeronautics series. AIAA, Washington, DC, pp 115–148Google Scholar
  33. 33.
    Jansson H, Hjalmarsson H (2002) From open-loop learning to closed-loop control. In: Proceedings of the 41st IEEE conference on decision and control, vol 4, pp 4209–4214. doi: 10.1109/CDC.2002.1185030
  34. 34.
    Kulvicius T et al (2010) Behavioral analysis of differential Hebbian learning in closed-loop systems. Biol Cybern 103:255–271. doi: 10.1007/s00422-010-0396-4 CrossRefGoogle Scholar
  35. 35.
    Dorigo M, Colombetti M (1994) Robot shaping: developing autonomous agents through learning. Artif Intell 71:321–370CrossRefGoogle Scholar
  36. 36.
    Andry P, et al (2001) Learning and communication via imitation: an autonomous robot perspective. IEEE Trans Syst Man Cybern A Syst Hum 31:431–442. doi: 10.1109/3468.952717 CrossRefGoogle Scholar
  37. 37.
    Olalla MF (2000) Information technology and business process redesign. JEL M12; Int’l Adv Econ Res 6:581–589. http://www.wfmc.org/Download-document/IT-in-Business-Process-Reengineering.html. Accessed 1 Nov 2010
  38. 38.
    Hülsmann M, Windt K (2007) Understanding autonomous cooperation and control in logistics: the impact of autonomy on management, information, communication and material flow. Springer, BerlinCrossRefGoogle Scholar
  39. 39.
    Da Silveira G, Borenstein D, Fogliatto F (2001) Mass customization: literature review and research directions. Int J Prod Econ 72:1–13. doi: 10.1016/S0925-5273(00)00079-7 CrossRefGoogle Scholar
  40. 40.
    Fredriksson P, Gadde L (2005) Flexibility and rigidity in customization and build-to-order production. Ind Market Manag 34:695–705CrossRefGoogle Scholar
  41. 41.
    Meyer G, FrÃd’mling K, HolmstrÃüm J (2009) Intelligent products: a survey. Comput Ind 60:137–148CrossRefGoogle Scholar
  42. 42.
    Stone P, Veloso M (2000) Multiagent systems: a survey from a machine learning perspective. Autonom Robot 8:345–383CrossRefGoogle Scholar
  43. 43.
    Scholz-Reiter B, Mehrsai A (2010) Superior performance of Leagile supply networks by application of autonomous control. In: Vallespir B, Alix T (eds) Advances in production management systems. New challenges, new approaches 338. Springer, Boston, pp 333–341. doi: 10.1007/978-3-642-16358-6_42 CrossRefGoogle Scholar
  44. 44.
    Wang H (2005) Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions. Expert Syst 22:78–85zbMATHCrossRefGoogle Scholar
  45. 45.
    Daley D (1965) General customer impatience in the queue GI/G/1. J Appl Probab 2:186–205MathSciNetzbMATHCrossRefGoogle Scholar
  46. 46.
    Zadeh L (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28–44MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Zimmermann H (2001) Fuzzy set theory—and its applications. Springer, The NetherlandsCrossRefGoogle Scholar
  48. 48.
    Petrovic S et al (2008) Fuzzy job shop scheduling with lot-sizing. Annals Oper Res 159:275–292 doi: 10.1007/s10479-007-0287-9 MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Klimke W (2006) Uncertainty modeling using fuzzy arithmetic and sparse grids. University of Stuttgart, StuttgartzbMATHGoogle Scholar
  50. 50.
    Tay N, Linn S (2001) Fuzzy inductive reasoning, expectation formation and the behavior of security prices. J Econ Dyn Control 25:321–361zbMATHCrossRefGoogle Scholar
  51. 51.
    Mehrsai A, Wenning, B.L, Scholz-Reiter B (2011) Analysis of learning pallets in flexible scheduling by closed queue network. In: IEEE international symposium on assembly and manufacturing (ISAM), Tampare, pp 1–8. doi: 10.1109/ISAM.2011.5942357
  52. 52.
    Haridy S, Wu Z (2009) Univariate and multivariate control charts for monitoring dynamic-behavior processes: a case study. J Indust Eng Manag 2:464–498. doi: 10.3926/jiem.2009.v2n3.p464-498 Google Scholar
  53. 53.
    Dong W, Wong F (1987) Fuzzy weighted averages and implementation of the extension principle. Fuzzy Set Syst 21:183–199MathSciNetzbMATHCrossRefGoogle Scholar
  54. 54.
    Mamdani E (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc IEEE 121:1585–1588Google Scholar
  55. 55.
    Ying H et al (2002) Comparison of necessary conditions for typical Takagi–Sugeno and Mamdani fuzzy systems as universal approximators. IEEE Trans Syst Man Cybern A Syst Hum 29:508–514CrossRefGoogle Scholar
  56. 56.
    Pfluger N, Yen J, Langari R (2002) A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation. In: IEEE international conference on fuzzy systems, pp 717–723. doi: 10.1109/FUZZY.1992.258746
  57. 57.
    Scholz-Reiter B, Freitag M, De Beer C, Jagalski T (2007) Analysing the dynamics caused by autonomously controlled logistic objects. In: Proceedings of the 2nd international conference changeable, agile reconfigurable and virtual production (CARV07). CiteSeerX Beta. http://citeseerx.ist.psu.edu/viewdoc/summary?doi:=10.1.1.165.2039. Accessed 1 Nov 2010
  58. 58.
    Lei D (2010) Fuzzy job shop scheduling problem with availability constraints. Comput Ind Eng 58:610–617CrossRefGoogle Scholar
  59. 59.
    Ahmadizar F, Hosseini L (2011) Single-machine scheduling with a position-based learning effect and fuzzy processing times. Int J Adv Manufact Technol. doi: 10.1007/s00170-011-3190-0 Google Scholar
  60. 60.
    Mehrsai A, Teucke M, Scholz-Reiter B (2010) Coordination of push-pull principle logistics network by optimizing material-pull; applying genetic algorithm. In: Proceedings 1st international conference on logistics and maritime systems (LOGMS), Pusan, CD-ROM, pp 2–11Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Afshin Mehrsai
    • 1
  • Hamid-Reza Karimi
    • 2
  • Bernd Scholz-Reiter
    • 1
  1. 1.Department of Planning and Control of Production SystemsUniversity of BremenBremenGermany
  2. 2.Department of Engineering and ScienceUniversity of AgderAgderNorway

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