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Increasing the Efficiency of Logistics in Warehouse Using the Combination of Simple Optimization Methods

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 661)

Abstract

The paper focuses on increasing the efficiency of logistics process in warehouse. Today’s trend is to use simulation tools. To obtain effective solutions exist various optimization methods, optimization algorithms and heuristics. The presented experiment uses the combination of two simple optimization methods for searching the effective solution in reasonable time. The Random solutions algorithm and the All combinations algorithm are used together. Random solutions algorithm generates random combinations and can help indicate how solutions will vary, by giving a picture of the shape of the entire solution space for a scenario. All combinations algorithm is a method, which runs all constrained combinations. If sufficient time is available, this method guarantees that the optimal result will be found. An estimate of the time to be taken can be obtained in advance. This concrete two algorithms demonstration is a high quality and fast way to achieve effective (optimal) results in a short time. The Witness simulation environment is used for the experiments.

Keywords

Simulation Random solutions algorithm All combinations algorithm Optimization Logistics Warehouse ABC classification Inventory 

Notes

Acknowledgment

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089 and also by the Internal Grant Agency of Tomas Bata University under the project No. IGA/FAI/2017/003.

References

  1. 1.
    Cecil-Wright, J.: How the boardroom can influence warehousing costs. Int. J. Retail Distrib. Manag. 14(3), 67–69 (1986)CrossRefGoogle Scholar
  2. 2.
    Segerstedt, A., Pettersson, A.I.: Measurements of excellence in a supply chain. Int. J. Logist. Syst. Manag. 13(1), 65–80 (2012)CrossRefGoogle Scholar
  3. 3.
    Hausman, W.H., Schwarz, L.B., Graves, S.C.: Optimal storage assignment in automatic warehousing system. Manag. Sci. 22(6), 629–638 (1976)CrossRefzbMATHGoogle Scholar
  4. 4.
    Muppani, V.R., Adil, G.K., Bandyopadhyay, A.: A review of methodologies for class-based storage location assignment in a warehouse. Int. J. Adv. Oper. Manag. 2(3–4), 274–291 (2010)Google Scholar
  5. 5.
    Kovacs, A.: Optimizing the storage assignment in a warehouse served by Milkrun logistics. Int. J. Prod. Econ. 133(1), 312–318 (2011)CrossRefGoogle Scholar
  6. 6.
    Glock, C.H., Grosse, E.H.: Storage policies and order picking strategies in U-shaped order picking systems with a movable base. Int. J. Prod. Res. 50(16), 4344–4357 (2012)CrossRefGoogle Scholar
  7. 7.
    De Koster, R.B.M., Le-Duc, T., Zaerpour, N.: Determining the number of zones in a pick and-sort order picking system. Int. J. Prod. Res. 50(3), 757–771 (2012)CrossRefGoogle Scholar
  8. 8.
    Bragg, S.M.: Inventory Management. Accounting Tools, Colorado (2013)Google Scholar
  9. 9.
    Waters, D.: Inventory Control and Management (Business), 2nd edn. Wiley, New York (2003)Google Scholar
  10. 10.
    Piasecki, D.J.: Inventory Management Explained. Ops Publishing, Pleasant Prairie (2009)Google Scholar
  11. 11.
    Granville, D.: Excellence in Inventory Management: How to Minimise Costs and Maximise Services, 1st edn. Cambridge Academic, Cambridge (2007)Google Scholar
  12. 12.
    Milner, C.: Inventory Management: Advanced Methods for Managing Inventory within Business Systems, 1st edn. Kogan Page, London (2015)Google Scholar
  13. 13.
    Grinsted, S.: The Logistics and Supply Chain Toolkit: Over 90 Tools for Transport, Warehousing and Inventory Management, 1st edn. Kogan Page, London (2013)Google Scholar
  14. 14.
    Muller, M.: Essentials of Inventory Management, 2nd edn. Amacom, New York (2011)Google Scholar
  15. 15.
    Bottani, E., Montanari, R., Rinaldi, M., Vignali, G.: Intelligent algorithms for warehouse management. Intell. Syst. Ref. Libr. 87, 645–667 (2015)CrossRefGoogle Scholar
  16. 16.
    López, J.A., Mendoza, A., Masini, J.: A classic and effective approach to inventory management. Int. J. Ind. Eng. Theory Appl. Pract. 20(56), 372–386 (2013)Google Scholar
  17. 17.
    Xiao, Y., Zhang, R., Kaku, I.: A new approach of inventory classification based on loss profit. Expert Syst. Appl. 38(8), 9382–9391 (2011)CrossRefGoogle Scholar
  18. 18.
    Paweł, P., Marek, F., Paulina, G.: Using ABC classification to determine production sequence in automotive industry. In: Proceedings of 2008 World Automation Congress, WAC 2008, pp. 1–6. IEEE (2008)Google Scholar
  19. 19.
    Smith, A.D.: Inventory management and ABC analysis practices in competitive environments. Int. J. Procure. Manag. 4(4), 433–454 (2011)CrossRefGoogle Scholar
  20. 20.
    Miculescu, M.N., Lut, D.M., Miculescu, C.: Current trends of production cost accounting. In: Katalinic, B. (ed.) Annals of DAAAM for 2011 and Proceedings of the 22nd International DAAAM Symposium, 23–26 November 2011, Vienna, Austria, vol. 22, no. 1, pp. 0941–0942. DAAAM International Vienna, Vienna (2011). ISSN 1726-9679, ISBN 978-3-901509-83-4Google Scholar
  21. 21.
    Pasic, M., Kadric, E.R., Bajric, H.: Relationship between inventory investment and forecasting and inventory control. In: Katalinic, B. (ed.) Annals of DAAAM for 2010 and Proceedings of the 21st International DAAAM Symposium, 20–23 October 2010, Zadar, Croatia, pp. 0511–0512. DAAAM International Vienna, Vienna (2010). ISSN 1726-9679, ISBN 978-3-901509-73-5Google Scholar
  22. 22.
    Knezevic, B.: Usage of simulation in inventory management education. In: Katalinic, B. (ed.) Annals of DAAAM for 2011 and Proceedings of the 22nd International DAAAM Symposium, 23–26 November 2011, Vienna, Austria, vol. 22, no. 1, pp. 1197–1198. DAAAM International Vienna, Vienna (2011). ISSN 1726-9679, ISBN 978-3-901509-83-4Google Scholar
  23. 23.
    Gastermann, B.C., Luftensteiner, F., Stopper, M., Katalinic, B.: Multiple stage production planning in plain manufacturing environments. In: Katalinic, B. (ed.) Annals of DAAAM for 2012 and Proceedings of the 23rd International DAAAM Symposium, pp. 0883–0886. DAAAM International, Vienna, Austria (2012). ISBN 978-3-901509-91-9, ISSN 2304-1382Google Scholar
  24. 24.
    Davendra, D., Zelinka, I., Senkerik, R., Bialic-Davendra, M.: Chaos driven evolutionary algorithm for the Traveling Salesman Problem. In: Davendra, D. (ed.) Traveling Salesman Problem, Theory and Applications. InTech Europe, Rijeka (2010)CrossRefGoogle Scholar
  25. 25.
    Davendra, D.: Evolutionary algorithms and the edge of Chaos. In: Zelinka, I., Celikovsky, S., Richter, H., Chen, G. (eds.) Evolutionary Algorithms and Chaotic Systems. Studies in Computational Intelligence, vol. 267, pp. 145–161. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Davendra, D., Bialic-Davendra, M., Senkerik, R.: Scheduling the lot-streaming flowshop scheduling problem with setup time with the chaos-induced enhanced differential evolution. In: 2013 IEEE Symposium on Differential Evolution (SDE), pp. 119–126 (2013)Google Scholar
  27. 27.
    Deugo, D., Ferguson, D.: Evolution to the Xtreme: evolving evolutionary strategies using a meta-level approach. In: Congress on Evolutionary Computation, CEC 2004, 19–23 June 2004, pp. 31–38 (2004)Google Scholar
  28. 28.
    Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.: Parameter control in evolutionary algorithms. In: Parameter Setting in Evolutionary Algorithms, pp. 19–46. Springer, Heidelberg (2007)Google Scholar
  29. 29.
    Hilborn, R.C.: Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers. Oxford University Press, Oxford (2000)CrossRefzbMATHGoogle Scholar
  30. 30.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, November/December 1995, pp. 1942–1948 (1995)Google Scholar
  31. 31.
    May, R.M.C.: Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton (2001)zbMATHGoogle Scholar
  32. 32.
    Oplatkova, Z.: Metaevolution: Synthesis of Optimization Algorithms by Means of Symbolic Regression and Evolutionary Algorithms. Lambert Academic Publishing, Saarbrücken (2010)Google Scholar
  33. 33.
    Pluhacek, M., Senkerik, R., Zelinka, I.: Multiple choice strategy based PSO algorithm with chaotic decision making, a preliminary study. In: Herrero, Á., Baruque, B., Klett, F., et al. (eds.) International Joint Conference SOCO 2013-CISIS 2013-ICEUTE 2013, vol 239. Advances in Intelligent Systems and Computing, pp. 21–30. Springer International Publishing (2014)Google Scholar
  34. 34.
    Pluhacek, M., Senkerik, R., Zelinka, I., Davendra, D.: Chaos PSO algorithm driven alternately by two different chaotic maps—an initial study. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2444–2449 (2013)Google Scholar
  35. 35.
    Price, K.V.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw-Hill Ltd., London (1999)Google Scholar
  36. 36.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution—A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  37. 37.
    Senkerik, R., Davendra, D., Zelinka, I., Pluhacek, M., Oplatkova, Z.: An investigation on the chaos driven differential evolution: an initial study. In: 5th International Conference on Bioinspired Optimization Methods and their Applications, BIOMA 2012, pp. 185–194 (2012)Google Scholar
  38. 38.
    Senkerik, R., Zelinka, I., Oplatkova, Z.: Optimal control of evolutionary synthesized chaotic system. In: 15th International Conference on Soft Computing—Mendel 2009, pp. 220–227 (2009)Google Scholar
  39. 39.
    Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, Oxford (2003)zbMATHGoogle Scholar
  40. 40.
    Zelinka, I.: SOMA—Self-Organizing Migrating Algorithm. In: New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing, vol. 141, pp. 167–217. Springer, Heidelberg (2004)Google Scholar
  41. 41.
    Zelinka, I., Raidl, A.: Evolutionary synchronization of chaotic systems. In: Zelinka, I., Celikovsky, S., Richter, H., Chen, G. (eds.) Evolutionary Algorithms and Chaotic Systems. Studies in Computational Intelligence, vol. 267, pp. 385–407. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  42. 42.
    Zelinka, I., Davendra, D., Senkerik, R., Jasek, R., Oplatkova, Z.: Analytical programming—a novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms. InTech Europe, Rijeka (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlínZlínCzech Republic

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