https://oldena.lpnu.ua/handle/ntb/52538| Title: | Intelligent Support for Resource Distribution in Logistic Networks Using Continuous-Domain Genetic Algorithms |
| Authors: | Wieczorek, Lukasz Ignaciuk, Przemyslaw |
| Affiliation: | Lodz University of Technology |
| Bibliographic description (Ukraine): | Wieczorek L. Intelligent Support for Resource Distribution in Logistic Networks Using Continuous-Domain Genetic Algorithms / Lukasz Wieczorek, Przemyslaw Ignaciuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 424–429. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Wieczorek L. Intelligent Support for Resource Distribution in Logistic Networks Using Continuous-Domain Genetic Algorithms / Lukasz Wieczorek, Przemyslaw Ignaciuk // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 424–429. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | inventory management optimization genetic algorithms uncertain demand |
| Number of pages: | 6 |
| Page range: | 424-429 |
| Start page: | 424 |
| End page: | 429 |
| Abstract: | The paper addresses the issue of improving the goods distribution efficiency in logistic networks subjected to uncertain demand. The class of networks under consideration encompasses two types of entities – controlled nodes and external sources – forming a mesh interconnection structure. In order to find the optimal operating conditions for the a priori unknown, time-varying demand, numerous, computationally involving simulations need to be conducted. In this work, the application of genetic algorithms (GAs) with continuous domain search is proposed to optimize the goods reflow in the network. The objective is to reduce the holding costs while ensuring high customer satisfaction. Using a network state-space model with a centralized inventory management policy, GA automatically adjusts the policy parameters to a given network topology. Extensive tests for different statistical distributions validate the analytical content. |
| URI: | https://ena.lpnu.ua/handle/ntb/52538 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] G. Gereffi and S. Frederick, "The global apparel value chain, trade G. Gereffi and S. Frederick, The global apparel value chain, trade and the crisis: Challenges and opportunities for developing countries. Policy Research Working Papers, no. 5281, 2010. [2] T. Berger and C. B. Frey, “Industrial renewal in the 21st century: Evidence from US cities,” Regional Studies, vol. 50, pp. 1–10, 2015. [3] M. Grazia Speranza, “Trends in transportation and logistics,” European Journal of Operational Research, vol. 264, pp. 830-836, 2018. [4] S. Sagiroglu and D. Sinanc, “Big data: A review,” 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, pp. 42-47, 2013. [5] M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and Big data: A revolution that will transform supply chain design and management,” Journal of Business Logistics, vol. 34, pp. 77-84, 2013. [6] G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: Certain investigations for research and applications,” International Journal of Production Economics, vol. 176, pp. 98-110, 2016. [7] E. Ahmed, I. Yaqoob, I. A. T. Hashem, I. Khan, A. I. A. Ahmed, M. Imran, and A. V. Vasilakos, “The role of big data analytics in Internet of Things,” Computer Networks, vol. 129, pp. 459-471, 2017. [8] V. Potó, J. Á. Somogyi, T. Lovas, and Á. Barsi, "Laser scanned point clouds to support autonomous vehicles," Transportation Research Procedia, vol. 27, pp. 531-537, 2017. [9] K. Xu and P. T. Evers, "Managing single echelon inventories through demand aggregation and the feasibility of a correlation matrix," Computers & Operations Research, vol. 30, pp. 297-308, 2003. [10] P. Ignaciuk and A. Bartoszewicz, “Dead-beat and reaching-law-based sliding-mode control of perishable inventory systems,” Bulletin of the Polish Academy of Sciences-Technical Sciences, vol. 59, pp. 39-49, 2011. [11] P. Ignaciuk and A. Bartoszewicz, “Linear-quadratic optimal control of periodic-review perishable inventory systems,” IEEE Transactions on Control Systems Technology, vol. 20, pp. 1400-1407, 2012. [12] C. A. Garcia, A. Ibeas, and R. Vilanova, "A switched control strategy for inventory control of the supply chain," Journal of Process Control, vol. 23, pp. 868-880, 2013. [13] H. D. Purnomo, H. M. Wee, and Y. Praharsi, "Two inventory review policies on supply chain configuration problem," Computers & Industrial Engineering, vol. 63, pp. 448–455, 2012. [14] P. Ignaciuk, "Discrete inventory control in systems with perishable goods – a time-delay system perspective," IET Control Theory & Applications, vol. 8, pp. 11-21, 2014. [15] C. O. Kim, J. Jun, J. K. Baek, R. L. Smith, and Y. D. Kim, "Adaptive inventory control models for supply chain management," The International Journal of Advanced Manufacturing Technology, vol. 26, pp. 1184–1192, 2005. [16] P. Ignaciuk, "Nonlinear inventory control with discrete sliding modes in systems with uncertain delay," IEEE Transactions on Industrial Informatics, vol. 10, pp. 559-568, 2014. [17] L. Sun and Y. Zhou, "A knowledge-based tree-like representation for inventory routing problem in the distribution system of oil products," Procedia Computer Science, vol. 112, pp. 1683-1691, 2017. [18] J. Poppe, R. J. I. Basten, R. N. Boute, and M. R. Lambrecht, "Numerical study of inventory management under various maintenance policies," Reliability Engineering & System Safety, vol. 168, pp. 262-273, 2017. [19] P. Garcia-Herreros, A. Agarwal, J. M. Wassick, and I. E. Grossmann, "Optimizing inventory policies in process networks under uncertainty," Computers & Chemical Engineering, vol. 92, pp. 256-272, 2016. [20] S. Kulkarni, R. Patil, M. Krishnamoorthy, A. Ernst, and A. Ranade, "A new two-stage heuristic for the recreational vehicle scheduling problem," Computers & Operations Research, vol. 91, pp. 59-78, 2018. [21] P. Ignaciuk and Ł. Wieczorek, "Optimization of mesh-type logistic networks for achieving max service rate under order-up-to inventory policy," Advances in Intelligent Systems and Computing, Springer International Publishing, vol. 657, pp. 118-127, 2018. [22] P. Ignaciuk, “Dynamic modeling and order-up-to inventory management in logistic networks with positive lead time,” 2015 IEEE Int. Conf. Intel. Comp. Com. Proc., Cluj-Napoca, Romania, pp. 507–510, Sep. 2015. [23] D. Simon, Evolutionary Optimization Algorithms. John Wiley & Sons, 2013. |
| References (International): | [1] G. Gereffi and S. Frederick, "The global apparel value chain, trade G. Gereffi and S. Frederick, The global apparel value chain, trade and the crisis: Challenges and opportunities for developing countries. Policy Research Working Papers, no. 5281, 2010. [2] T. Berger and C. B. Frey, "Industrial renewal in the 21st century: Evidence from US cities," Regional Studies, vol. 50, pp. 1–10, 2015. [3] M. Grazia Speranza, "Trends in transportation and logistics," European Journal of Operational Research, vol. 264, pp. 830-836, 2018. [4] S. Sagiroglu and D. Sinanc, "Big data: A review," 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, pp. 42-47, 2013. [5] M. A. Waller and S. E. Fawcett, "Data science, predictive analytics, and Big data: A revolution that will transform supply chain design and management," Journal of Business Logistics, vol. 34, pp. 77-84, 2013. [6] G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, vol. 176, pp. 98-110, 2016. [7] E. Ahmed, I. Yaqoob, I. A. T. Hashem, I. Khan, A. I. A. Ahmed, M. Imran, and A. V. Vasilakos, "The role of big data analytics in Internet of Things," Computer Networks, vol. 129, pp. 459-471, 2017. [8] V. Potó, J. Á. Somogyi, T. Lovas, and Á. Barsi, "Laser scanned point clouds to support autonomous vehicles," Transportation Research Procedia, vol. 27, pp. 531-537, 2017. [9] K. Xu and P. T. Evers, "Managing single echelon inventories through demand aggregation and the feasibility of a correlation matrix," Computers & Operations Research, vol. 30, pp. 297-308, 2003. [10] P. Ignaciuk and A. Bartoszewicz, "Dead-beat and reaching-law-based sliding-mode control of perishable inventory systems," Bulletin of the Polish Academy of Sciences-Technical Sciences, vol. 59, pp. 39-49, 2011. [11] P. Ignaciuk and A. Bartoszewicz, "Linear-quadratic optimal control of periodic-review perishable inventory systems," IEEE Transactions on Control Systems Technology, vol. 20, pp. 1400-1407, 2012. [12] C. A. Garcia, A. Ibeas, and R. Vilanova, "A switched control strategy for inventory control of the supply chain," Journal of Process Control, vol. 23, pp. 868-880, 2013. [13] H. D. Purnomo, H. M. Wee, and Y. Praharsi, "Two inventory review policies on supply chain configuration problem," Computers & Industrial Engineering, vol. 63, pp. 448–455, 2012. [14] P. Ignaciuk, "Discrete inventory control in systems with perishable goods – a time-delay system perspective," IET Control Theory & Applications, vol. 8, pp. 11-21, 2014. [15] C. O. Kim, J. Jun, J. K. Baek, R. L. Smith, and Y. D. Kim, "Adaptive inventory control models for supply chain management," The International Journal of Advanced Manufacturing Technology, vol. 26, pp. 1184–1192, 2005. [16] P. Ignaciuk, "Nonlinear inventory control with discrete sliding modes in systems with uncertain delay," IEEE Transactions on Industrial Informatics, vol. 10, pp. 559-568, 2014. [17] L. Sun and Y. Zhou, "A knowledge-based tree-like representation for inventory routing problem in the distribution system of oil products," Procedia Computer Science, vol. 112, pp. 1683-1691, 2017. [18] J. Poppe, R. J. I. Basten, R. N. Boute, and M. R. Lambrecht, "Numerical study of inventory management under various maintenance policies," Reliability Engineering & System Safety, vol. 168, pp. 262-273, 2017. [19] P. Garcia-Herreros, A. Agarwal, J. M. Wassick, and I. E. Grossmann, "Optimizing inventory policies in process networks under uncertainty," Computers & Chemical Engineering, vol. 92, pp. 256-272, 2016. [20] S. Kulkarni, R. Patil, M. Krishnamoorthy, A. Ernst, and A. Ranade, "A new two-stage heuristic for the recreational vehicle scheduling problem," Computers & Operations Research, vol. 91, pp. 59-78, 2018. [21] P. Ignaciuk and Ł. Wieczorek, "Optimization of mesh-type logistic networks for achieving max service rate under order-up-to inventory policy," Advances in Intelligent Systems and Computing, Springer International Publishing, vol. 657, pp. 118-127, 2018. [22] P. Ignaciuk, "Dynamic modeling and order-up-to inventory management in logistic networks with positive lead time," 2015 IEEE Int. Conf. Intel. Comp. Com. Proc., Cluj-Napoca, Romania, pp. 507–510, Sep. 2015. [23] D. Simon, Evolutionary Optimization Algorithms. John Wiley & Sons, 2013. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Wieczorek_L-Intelligent_Support_for_424-429.pdf | 643.38 kB | Adobe PDF | View/Open | |
| 2018_Wieczorek_L-Intelligent_Support_for_424-429__COVER.png | 561.79 kB | image/png | View/Open |
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