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Intelligent Algorithms for Warehouse Management

  • Eleonora Bottani
  • Roberto Montanari
  • Marta Rinaldi
  • Giuseppe Vignali
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 87)

Abstract

Warehouses are important links in the supply chain; here, products are temporarily stored and retrieved subsequently from storage locations to fulfill customer’ orders. The order picking activity is one of the most time-consuming processes of a warehouse and is estimated to contribute for more than 55 % of the total cost of warehouse operations. Accordingly, scientists, as well as logistics managers, consider order picking as one of the most promising area for productivity improvements. This chapter is intended to provide the reader with an overview of different intelligent tools applicable to the issue of picking optimization. Specifically, by this chapter, we show how different types of intelligent algorithms can be used to optimize order picking operations in a warehouse, by decreasing the travel distance (and thus time) of pickers. The set of intelligent algorithms analyzed include: genetic algorithms, artificial neural networks, simulated annealing, ant colony optimization and particles swarm optimization models. For each intelligent algorithm, we start with a brief theoretical overview. Then, based on the available literature, we show how the algorithm can be implemented for the optimization of order picking operations. The expected pros and cons of each algorithm are also discussed.

Keywords

Order picking Warehouse optimization Items allocation Intelligent algorithms 

References

  1. Aburto, L., Weber, R.: Improved supply chain management based on hybrid demand forecasts. Appl. Soft Comput. 7, 136–144 (2007)CrossRefGoogle Scholar
  2. Atmaca, E., Ozturk, A.: Defining order picking policy: a storage assignment model and a simulated annealing solution in AS/RS systems. Appl. Math. Model. 37(7), 5069–5079 (2013)CrossRefMathSciNetGoogle Scholar
  3. Beasley, D., Bull, D.R., Martin, R.R.: An overview of genetic algorithms: parts 1 and 2. Univ. Comput. 15, 2–4 (1993)Google Scholar
  4. Bottani, E., Cecconi, M., Vignali, G., Montanari, R.: Optimisation of storage allocation in order picking operations through a genetic algorithm. Int. J. Logistics Res. Appl. 15(2), 127–146 (2012)CrossRefGoogle Scholar
  5. Bottani, E., Montanari, R., Volpi, A.: The impact of RFID and EPC network on the bullwhip effect in the Italian FMCG supply chain. Int. J. Prod. Econ. 124(2), 426–432 (2010)CrossRefGoogle Scholar
  6. Bottani, E., Rizzi, A.: Economical assessment of the impact of RFID technology and EPC system on the fast moving consumer goods supply chain. Int. J. Prod. Econ. 112(2), 548–569 (2008)CrossRefGoogle Scholar
  7. Boysen, N., Stephan, K.: The deterministic product location problem under a pick-by-order policy. Discrete Appl. Math. 161(18), 2862–2875 (2013)CrossRefMATHMathSciNetGoogle Scholar
  8. Chen, F., Wang, H., Qi, C., Xie, Y.: An ant colony optimization routing algorithm for two order pickers with congestion consideration. Comput. Ind. Eng. 66(1), 77–85 (2013)CrossRefGoogle Scholar
  9. Chen, F., Wang, H., Xie, Y., Qi, C.: An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse. J. Intell. Manuf. (In press). doi:  10.1007/s10845-014-0871-1
  10. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of ECAL91—European Conference on Artificial Life, pp. 134–142. Paris (France), (1991). http://faculty.washington.edu/paymana/swarm/colorni92-ecal.pdf Accessed Sept 2014
  11. Coyle, J.J., Bardi, E.J., Langley, C.J.: The Management of Business Logistics. West Publishing Company, Mason (1996)Google Scholar
  12. Dallari, F., Marchet, G., Melacini, M.: Design of order picking system. Int. J. Adv. Manuf. Technol. 42(1–2), 1–12 (2009)CrossRefGoogle Scholar
  13. de Koster, R., Le-Duc, T., Jan Roodbergen, K.: Design and control of warehouse order picking: a literature review. Eur. J. Oper. Res. 182, 481–501 (2007)CrossRefMATHGoogle Scholar
  14. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 1–13 (1996)CrossRefGoogle Scholar
  15. ESTECO.: MOSA—multi objective simulated annealing. Technical Report 2003-003 (2003).Google Scholar
  16. Garetti, M., Taisch, M.: Neural networks in production planning and control. Prod. Plan. Control 10(4), 324–339 (1999)CrossRefGoogle Scholar
  17. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  18. Grosse, E.H., Glock, C.H., Ballester-Ripoll, R.: A simulated annealing approach for the joint order batching and order picker routing problem with weight restrictions. Int. J. Oper. Quant. Manage. 2(20), 65–83 (2014)Google Scholar
  19. Hall, R.W.: Distance approximation for routing manual pickers in a warehouse. IIE Trans. 25, 77–87 (1993)CrossRefGoogle Scholar
  20. Ho, Y.-C., Chien, S.-P.: A comparison of two zone-visitation sequencing strategies in a distribution centre. Comput. Ind. Eng. 50(4), 426–439 (2006)CrossRefGoogle Scholar
  21. Ho, Y.-C., Tseng, Y.-Y.: A study on order-batching methods of order-picking in a distribution centre with two cross-aisles. Int. J. Prod. Res. 44(17), 3391–3417 (2006)CrossRefMATHGoogle Scholar
  22. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, USA (1975)Google Scholar
  23. Hong, S., Johnson, A.L., Peters, B.A.: Batch picking in narrow-aisle order picking systems with consideration for picker blocking. Eur. J. Oper. Res. 221(3), 557–570 (2012)CrossRefMATHGoogle Scholar
  24. Hsu, C.-M., Chen, K.-Y., Chen, M.-C.: Batching orders in warehouses by minimizing travel distance with genetic algorithms. Comput. Ind. 56(2), 169–178 (2005)Google Scholar
  25. Jarvis, J.M., McDowell, E.D.: Optimal product layout in an order picking warehouse. IIE Trans. 23(1), 93–102 (1991)CrossRefGoogle Scholar
  26. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks 4, 1942–1948 (1995). doi: 10.1109/ICNN.1995.488968 Google Scholar
  27. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)CrossRefMATHMathSciNetGoogle Scholar
  28. Kuo, R.J., Tseng, W.L., Tien, F.C., Warren Liao, T.: Application of an artificial immune system-based fuzzy neural network to a RFID-based positioning system. Comput. Ind. Eng. 63, 943–956 (2012)CrossRefGoogle Scholar
  29. Kuo, R.J., Shieh, M.C., Zhang, J.W., Chen, K.Y.: The application of an artificial immune system-based back-propagation neural network with feature selection to an RFID positioning system. Robot. Comput. Integr. Manuf. 29, 431–438 (2013)CrossRefGoogle Scholar
  30. Kuo, R.J., Hung, S.Y., Cheng, W.C.: Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system. Inf. Sci. 262, 78–98 (2014)CrossRefGoogle Scholar
  31. Luxhøj, J.T., Riis, J.O., Stensballe, B.: A hybrid econometric-neural network modeling approach for sales forecasting. Int. J. Prod. Econ. 43, 175–192 (1996)CrossRefGoogle Scholar
  32. Matusiak, M., De Koster, R., Kroon, L., Saarinen, J.: A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse. Eur. J. Oper. Res. 236(3), 968–977 (2014)CrossRefMATHGoogle Scholar
  33. McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)CrossRefMATHMathSciNetGoogle Scholar
  34. Muppani, V.R., Adil, G.K.: Efficient formation of storage classes for warehouse storage location assignment: a simulated annealing approach. Omega 36(4), 609–618 (2008)CrossRefGoogle Scholar
  35. Oke, A., Long, M.: An analysis of the downstream logistics operations of a South African FMCG producer. Int. J. Prod. Econ. 108(1–2), 176–182 (2007)CrossRefGoogle Scholar
  36. Onut, S., Tuzkaya, U.R., Dogac, B.: A particle swarm optimization algorithm for the multiple-level warehouse layout design problem. Comput. Ind. Eng. 54(4), 783–799 (2008)CrossRefGoogle Scholar
  37. Öztürkoğlu, Ö., Gue, K.R., Meller, R.D.: A constructive aisle design model for unit-load warehouses with multiple pickup and deposit points. Euro. J. Oper. Res. 236(1), 382–394 (2014)Google Scholar
  38. Parikh, P.J., Meller, R.D.: Estimating picker blocking in wide-aisle order picking systems. IIE Trans. 41(3), 232–246 (2009)CrossRefGoogle Scholar
  39. Petersen, C.G.: The impact of routing and storage policies on warehouse efficiency. Int. J. Oper. Prod. Manage. 19(10), 1053–1064 (1999)CrossRefGoogle Scholar
  40. Petersen, C.G., Aase, G.: A comparison of picking, storage, and routing policies in manual order picking. Int. J. Prod. Econ. 92, 11–19 (2004)CrossRefGoogle Scholar
  41. Pourakbar, M., Sleptchenko, A., Dekker, R.: The floating stock policy in fast moving consumer goods supply chains. Transp. Res. Part E: Logistics Transp. Rev. 45(1), 39–49 (2009)CrossRefGoogle Scholar
  42. Roodbergen, K.J., De Koster, R.: Routing methods for warehouses with multiple cross aisles. Int. J. Prod. Res. 39(9), 1865–1883 (2001)CrossRefMATHGoogle Scholar
  43. Rouwenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.J., Mantel, R.J., Zijm, W.H.M.: Warehouse design and control: framework and literature review. Eur. J. Oper. Res. 122, 515–533 (2000)CrossRefMATHGoogle Scholar
  44. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)CrossRefGoogle Scholar
  45. Schalkoff, R.J.: Artificial neural networks. McGraw-Hill, New York (1997)MATHGoogle Scholar
  46. Shervais, S., Shannon, T.T., Lendaris, G.G.: Intelligent supply chain management using adaptive critic learning. IEEE Trans. Syst. Man Cybern—Part A: Syst. Humans 33(2), 235–244 (2003)Google Scholar
  47. Silva, C.A., Sousa, J.M.C., Runkler, T.A.: Rescheduling and optimization of logistic processes using GA and ACO. Eng. Appl. Artif. Intell. 21(3), 343–352 (2008)CrossRefGoogle Scholar
  48. Su, C.T.: Intelligent Control Mechanism of part picking operations of automated warehouse. In: Proceedings of the International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies pp. 256–261, Taipei, 22–27 May 1995 (1995). doi:  10.1109/IACET.1995.527572
  49. Tompkins, J.A., White, J.A., Bozer, Y.A., Frazelle, E.H., Tanchoco, J.M.A., Trevino, J.: Facilities Planning, 2nd edn. Wiley, New York (1996)Google Scholar
  50. Wang, Y., Zheng, J., Wang, S.: Evolutionary algorithm inspired particle swarm optimization (EA-PSO) for warehouse allocation problem. In: Proceedings of the 3rd International Conference on Computer Research and Development (ICCRD2011), Shanghai (China), 11–13 March 2011, (2011). doi:  10.1109/ICCRD.2011.5764079
  51. Xing, B., Gao, W.-J., Nelwamondo, F.V., Battle, K., Marwala, T.: Ant colony optimization for automated storage and retrieval system. In: IEEE Congress on Evolutionary Computation (CEC), Barcelona (Spain), 18–23 July 2010, (2010a). doi:  10.1109/CEC.2010.5586237
  52. Xing, B., Gao, W.-J., Battle, K., Marwala, T., Nelwamondo, F.V.: Intelligent travel route planning for bridge crane type of material handling equipment in cellular manufacturing. In: Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC), Istanbul (Turkey), 10–13 Oct. 2010, (2010b). doi:  10.1109/ICSMC.2010.5641894
  53. Xinmin, Z., Xiangzhuo, K., Xiaoguang, H., 2008. Modeling and optimizing fixed shelf order-picking for AS/RS based on least time. In: Proceedings of the IEEE International Conference on Automation and Logistics, Qingdao (China), 1–3 Sept 2008. doi:  10.1109/ICAL.2008.4636249
  54. Yao, M.-J., Chu, W.-M.: A genetic algorithm for determining optimal replenishment cycles to minimize maximum warehouse space requirements. Omega 36(4), 619–631 (2008)CrossRefGoogle Scholar
  55. Yu, M.: Enhancing warehouse performance by efficient order picking. Ph.D. thesis—Rotterdam School of Management, Erasmus University (2005). http://repub.eur.nl/pub/13691/EPS2008139LIS9058921673YU.pdf Accessed Sept 2014
  56. Zhang, X., Ma, T., Han, X.: Optimizing fixed shelf order-picking for AS/RS based on immune particle swarm optimization algorithm. In: Proceedings of the IEEE International Conference on Automation and Logistics (ICAL 2007), Jinan (China), 18–21 Aug 2007. doi:  10.1109/ICAL.2007.4339062
  57. Zhang, G.Q., Lai, K.K.: Combining path relinking and genetic algorithms for the multiple-level warehouse layout problem. Eur. J. Oper. Res. 169(2), 413–425 (2006)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Eleonora Bottani
    • 1
  • Roberto Montanari
    • 1
  • Marta Rinaldi
    • 2
  • Giuseppe Vignali
    • 1
  1. 1.Department of Industrial EngineeringUniversity of ParmaParmaItaly
  2. 2.Interdepartmental Centre for Packaging (CIPACK), C/O Department of Industrial EngineeringUniversity of ParmaParmaItaly

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