Skip to main content

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

  • Conference paper
  • First Online:
Book cover Cybernetics Approaches in Intelligent Systems (CoMeSySo 2017)

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

Included in the following conference series:

  • 1003 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Cecil-Wright, J.: How the boardroom can influence warehousing costs. Int. J. Retail Distrib. Manag. 14(3), 67–69 (1986)

    Article  Google Scholar 

  2. Segerstedt, A., Pettersson, A.I.: Measurements of excellence in a supply chain. Int. J. Logist. Syst. Manag. 13(1), 65–80 (2012)

    Article  Google Scholar 

  3. Hausman, W.H., Schwarz, L.B., Graves, S.C.: Optimal storage assignment in automatic warehousing system. Manag. Sci. 22(6), 629–638 (1976)

    Article  MATH  Google Scholar 

  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. Kovacs, A.: Optimizing the storage assignment in a warehouse served by Milkrun logistics. Int. J. Prod. Econ. 133(1), 312–318 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  8. Bragg, S.M.: Inventory Management. Accounting Tools, Colorado (2013)

    Google Scholar 

  9. Waters, D.: Inventory Control and Management (Business), 2nd edn. Wiley, New York (2003)

    Google Scholar 

  10. Piasecki, D.J.: Inventory Management Explained. Ops Publishing, Pleasant Prairie (2009)

    Google Scholar 

  11. Granville, D.: Excellence in Inventory Management: How to Minimise Costs and Maximise Services, 1st edn. Cambridge Academic, Cambridge (2007)

    Google Scholar 

  12. Milner, C.: Inventory Management: Advanced Methods for Managing Inventory within Business Systems, 1st edn. Kogan Page, London (2015)

    Google Scholar 

  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. Muller, M.: Essentials of Inventory Management, 2nd edn. Amacom, New York (2011)

    Google Scholar 

  15. Bottani, E., Montanari, R., Rinaldi, M., Vignali, G.: Intelligent algorithms for warehouse management. Intell. Syst. Ref. Libr. 87, 645–667 (2015)

    Article  Google Scholar 

  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. Xiao, Y., Zhang, R., Kaku, I.: A new approach of inventory classification based on loss profit. Expert Syst. Appl. 38(8), 9382–9391 (2011)

    Article  Google Scholar 

  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. Smith, A.D.: Inventory management and ABC analysis practices in competitive environments. Int. J. Procure. Manag. 4(4), 433–454 (2011)

    Article  Google Scholar 

  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 978-3-901509-83-4

    Google Scholar 

  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 978-3-901509-73-5

    Google Scholar 

  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 978-3-901509-83-4

    Google Scholar 

  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 2304-1382

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. Hilborn, R.C.: Chaos and Nonlinear Dynamics: An Introduction for Scientists and Engineers. Oxford University Press, Oxford (2000)

    Book  MATH  Google Scholar 

  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. May, R.M.C.: Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton (2001)

    MATH  Google Scholar 

  32. Oplatkova, Z.: Metaevolution: Synthesis of Optimization Algorithms by Means of Symbolic Regression and Evolutionary Algorithms. Lambert Academic Publishing, Saarbrücken (2010)

    Google Scholar 

  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. 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. 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. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution—A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  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. 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. Sprott, J.C.: Chaos and Time-Series Analysis. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Milan Jemelka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Jemelka, M., Chramcov, B., Kříž, P. (2018). Increasing the Efficiency of Logistics in Warehouse Using the Combination of Simple Optimization Methods. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Cybernetics Approaches in Intelligent Systems. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 661. Springer, Cham. https://doi.org/10.1007/978-3-319-67618-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67618-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67617-3

  • Online ISBN: 978-3-319-67618-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics