Share:


The application of the genetic algorithm to multi-criteria warehouses location problems on the logistics network

Abstract

This paper presents multi-criteria warehouses location problem in the logistics network. In order to solve this problem the location model was developed. The limitations and optimization criteria of the model were determined. Optimization criteria refer to transportation costs, costs associated with warehouses, e.g.: local taxes, expenditure on starting the warehouse, the constant costs, the labour force costs, the purchase costs of the additional land for the expansion, the transition costs of the raw material via the warehouses. The final location of warehouse facilities was obtained using a genetic algorithm. The genetic algorithm was developed in order to solve the multi-criteria warehouses location problem. This paper describes the stages of the genetic algorithm i.e. the stage of designating the initial population, the crossover and mutation process, the adaptation function. In this paper, the process of calibration of this algorithm was presented. The results of the genetic algorithm were compared with the random results.

Keyword : genetic algorithm, multi-criteria warehouses location problems, optimization, matrix crossover, adaptation function

How to Cite
Izdebski, M., Jacyna-Gołda, I., Wasiak, M., Jachimowski, R., Kłodawski, M., Pyza, D., & Żak, J. (2018). The application of the genetic algorithm to multi-criteria warehouses location problems on the logistics network. Transport, 33(3), 741-750. https://doi.org/10.3846/transport.2018.5165
Published in Issue
Sep 26, 2018
Abstract Views
2
PDF Downloads
8
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Akinc, U.; Khumawala, B. M. 1977. An efficient branch and bound algorithm for the capacitated warehouse location problem, Management Science 23(6): 585–594. https://doi.org/10.1287/mnsc.23.6.585

Ambroziak, T.; Jacyna, M.; Wasiak, M. 2006. The logistic services in a hierarchical distribution system, in K. G. Goulias (Ed.). Transport Science and Technology, 383–394. https://doi.org/10.1108/9780080467542-030

Brandeau, M. L.; Chiu, S. S. 1989. An overview of representative problems in location research, Management Science 35(6): 645–674. https://doi.org/10.1287/mnsc.35.6.645

Demirel, T.; Demirel, N. Ç.; Kahraman, C. 2010. Multi-criteria warehouse location selection using Choquet integral, Expert Systems with Applications 37(5): 3943–3952. https://doi.org/10.1016/j.eswa.2009.11.022

Dey, B.; Bairagi, B.; Sarkar, D.; Sanyal, S. K. 2016. Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria, International Journal of Management Science and Engineering Management 11(4) 262–278. https://doi.org/10.1080/17509653.2015.1086964

Geoffrion, A. M.; Graves, G. W. 2010. Multicommodity distribution system design by benders decomposition, International Series in Operations Research & Management Science 148: 35–61. https://doi.org/10.1007/978-1-4419-6810-4_4

Goldberg, D. E. 2003. Algorytmy genetyczne i ich zastosowania, Warszawa: Wydawnictwa naukowo-techniczne. 408 s. (in Polish).

Gołda, P.; Manerowski, J. 2014. Support of aircraft taxiing operations on the apron, Journal of Kones Powertrain and Transport 21(4): 127–135. https://doi.org/10.5604/12314005.1130457

Izdebski, M.; Jacyna-Gołda I.; Wasiak M. 2016. The application of genetic algorithm for warehouse location in logistic network, Journal of Kones Powertrain and Transport 23(3): 201–208. https://doi.org/10.5604/12314005.1216464

Jacyna, M. 1999. Multicriteria evaluation of traffic flow distribution in a multimodal transport corridor, taking into account logistics base service, Archives of Transport 11(3–4): 43–66.

Jacyna, M.; Wasiak, M. 2015. Multicriteria decision support in designing transport systems, Communications in Computer and Information Science 531: 11–23. https://doi.org/10.1007/978-3-319-24577-5_2

Jacyna-Gołda, I. 2013. Chosen aspects of logistics network design method for production service companies, International Journal of Logistics Systems and Management 15(2/3): 219–238. https://doi.org/10.1504/IJLSM.2013.053768

Jacyna-Gołda, I.; Izdebski, M.; Szczepański, E. 2016. Assessment of the method effectiveness for choosing the location of warehouses in the supply network, Communications in Computer and Information Science 640: 84–97. https://doi.org/10.1007/978-3-319-49646-7_8

Khumawala, B. M. 1973. An efficient heuristic procedure for the uncapacitated warehouse location problem, Naval Research Logistics Quarterly 20(1): 109–121. https://doi.org/10.1002/nav.3800200111

Lewczuk, K. 2015. The concept of genetic programming in organizing internal transport processes, Archives of Transport 34(2): 61–74. https://doi.org/10.5604/08669546.1169213

Merkisz-Guranowska, A.; Pielecha, J. 2014. Passenger cars and heavy duty vehicles exhaust emissions under real driving condition, Archives of Transport 31(3): 47–59. https://doi.org/10.5604/08669546.1146986

Michalewicz, Z. 1996. Algorytmy genetyczne + struktury danych = programy ewolucyjne. Warszawa: Wydawnictwa naukowo-techniczne. 432 s. (in Polish).

Özcan, T.; Çelebi, N.; Esnaf, Ş. 2011. Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem, Expert Systems with Applications 38(8): 9773–9779. https://doi.org/10.1016/j.eswa.2011.02.022

Podviezko, A. 2012. Augmenting multicriteria decision aid methods by graphical and analytical reporting tools, Lecture Notes in Business Information Processing 106: 236–251. https://doi.org/10.1007/978-3-642-29231-6_19

Sharma, R. R. K. 1991. Modelling a fertiliser distribution system, European Journal of Operational Research 51(1): 24–34. https://doi.org/10.1016/0377-2217(91)90142-I

Sharma, R. R. K.; Berry, V. 2007. Developing new formulations and relaxations of single stage capacitated warehouse location problem (SSCWLP): Empirical investigation for assessing relative strengths and computational effort, European Journal of Operational Research 177(2): 803–812. https://doi.org/10.1016/j.ejor.2005.11.028

Szczepański, E.; Jacyna-Gołda, I.; Murawski J. 2014. Genetic algorithms based approach for transhipment HUB location in urban areas, Archives of Transport 31(3): 73–82. https://doi.org/10.5604/08669546.1146989

Wasiak, M.; Jacyna-Gołda, I.; Izdebski, M. 2016. Multi-criteria warehouses location problem in the logistics network, in International Conference on Industrial Logistics (ICIL 2016), 28 September – 1 October 2016, Zakopane, Poland, 352–363.

Zieja, M.; Smoliński, H.; Gołda, P. 2015. Information systems as a tool for supporting the management of aircraft flight safety, Archives of Transport 36(4): 67–76. https://doi.org/10.5604/08669546.1185211