The solution of MRSLP with the use of heuristic algorithms

• Autorzy: Kochańska Joanna , Burduk Anna , Łapczyńska Dagmara , Musiał Kamil

Identyfikatory

DOI

10.24425/bpasts.2023.146407

• Języki publikacji: EN

Abstrakty

EN

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.

Słowa kluczowe

EN

in-plant logistics; milk run; heuristic algorithms; assembly line; genetic algorithm; tabu search; simulated annealing

PL

milk run; algorytmy heurystyczne; linia produkcyjna; algorytm genetyczny; przeszukiwanie tabu; symulowane wyżarzanie; logistyka wewnętrzna przedsiębiorstw

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 1
• Strony: art. no. e146407
• Opis fizyczny: Bibliogr. 55 poz., rys., tab.

Twórcy

autor

Kochańska Joanna

• Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

autor

Burduk Anna

• Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

• https://orcid.org/0000-0003-2181-4380

autor

Łapczyńska Dagmara

• Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

autor

Musiał Kamil

• Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland

Bibliografia

• [1] M. Alnahhal, A. Ridwan, and B. Noche, “In-plant milk run decision problems,” 2014 International Conference on Logistics Operations Management, Morocco, 2014, pp. 85–92, doi: 10.1109/GOL.2014.6887421.
• [2] A. Kovács, “Optimizing the storage assignment in a warehouse served by milkrun logistics,” Int. J. Prod. Econ., vol. 133, no. 1, pp. ([0-9]@)–([0-9]@)18, 2011, doi: 10.1016/j.ijpe.2009.10.028.
• [3] H.S. Kilic, M.B. Durmusoglum, and M. Baskak, “Classification and modeling for in-plant milk-run distribution systems,” Int. J. Adv. Manuf. Technol., vol. 62, pp. 1135–1146, 2012, doi: 10.1007/s00170-011-3875-4.
• [4] M. Baudin, Lean logistics: the nuts and bolts of delivering materials and goods. New York: Productivity Press, 2004.
• [5] S.I. Satoglu and I.E. Sahin, “Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry,” Int. J. Adv. Manuf. Technol., vol. 65, pp. 319–332, 2013, doi: 10.1007/s00170-012-4171-7.
• [6] M. Alnahhal and B. Noche, “Efficient material flow in mixed model assembly lines,” SpringerPlus, vol 2, pp. 415–426, 2013, doi: 10.1186/2193-1801-2-415.
• [7] Y. Bozer and D. Ciemnoczolowski, “Performance evaluation of small-batch container delivery systems used in lean manufacturing – Part 1: system stability and distribution of container starts,” Int. J. Prod. Res., vol 51, no. 2, pp. 555–567, 2013, doi: 10.1080/00207543.2012.656330.
• [8] G. Brar and G. Saini, “Milk run logistics: literature review and directions,” 2011 World Congress on Engineering, UK, 2011, vol. 1, pp. 797–801.
• [9] J. Kotowska, M. Markowski, and A. Burduk, “Optimization of the Supply of Components for Mass Production with the Use of the Ant Colony Algorithm,” in Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, A. Burduk and D. Mazurkiewicz, Eds., Cham: Springer, 2018, vol. 637, pp. 347–357, doi: 10.1007/978-3-319-64465-3_34.
• [10] S.I. Satoglu and I.E. Sahin, “Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry,” Int. J. Adv. Manuf. Technol., vol. 65, pp. 319–332, 2012, doi: 10.1007/s00170-012-4171-7.
• [11] D.A. De Moura and R.C. Botter, “Delivery and pick-up problem transportation – milk run or conventional systems,” Indep. J. Manag. Prod., vol. 7, no. 3, pp. 746–749, 2016, doi: 10.14807/ijmp.v7i3.434.
• [12] D. Patel, M.B. Patel, and J.A. Vadher, “Implementation of milk run material supply system in vehicle routing problem with simultaneous pickup and delivery,” Int. J. Appl. Innov. Eng. Manag., vol. 3, no. 11, pp. 122–123, 2014.
• [13] C. Finnsgård, C. Wänström, L. Medbo, and W.P. Neumann, “Impact of materials exposure on assembly workstation performance,” Int. J. Prod. Res., vol. 49, no. 24, pp. 7253–7274, 2011, doi: 10.1080/00207543.2010.503202.
• [14] R. Hanson and C. Finnsgård, “Impact of unit load size on inplant materials supply efficiency.” Int. J. Prod. Econ., vol. 147, pp. 46–52, 2012, doi: 10.1016/j.ijpe.2012.08.010.
• [15] W.P. Neumann and L. Medbo, “Ergonomic and technical aspects in the redesign of material supply systems: Big boxes vs. narrow bins,” Int. J. Prod. Econ., vol. 40, no. 5, pp. 541–548, 2010, doi: 10.1016/j.ergon.2010.06.004.
• [16] T. Rother, “Per Milkrun und Routenzugsystem Leerfahrten deutlich verringern (Less empty runs with milk run and tugger train systems),” Maschinen Markt, vol. 51/52, 2011.
• [17] U. Teschemacher and G. Reinhart, “Ant Colony Optimization Algorithms to Enable Dynamic Milkrun Logistics,” Procedia CIRP, vol. 63, pp. 762–767, 2017, doi: 10.1016/j.procir.2017.03.125.
• [18] H.S. Kilic and M.B. Durmusoglu, “A mathematical model and a heuristic approach for periodic material delivery in lean production environment,” Int. J. Adv. Manuf. Technol., vol. 69, pp. 977–992, 2013, doi: 10.1007/s00170-013-5082-y.
• [19] M.J. Eskandari, A.R. Aliahmadi and G.H.H. Khalegh, “A robust optimisation approach for the milk run problem with time windows with inventory uncertainty: An auto industry supply chain case study,” Int. J. Rapid Manuf., vol. 1, no. 3, pp. 334–347, 2010, doi: 10.1504/IJRAPIDM.2010.034254.
• [20] P. Kirci„ “An optimization algorithm for a capacitated vehicle routing problem with time windows,” S¯adhan¯a, vol. 41, no. 5, pp. 519–529, 2016, doi: 10.1007/s12046-016-0488-5.
• [21] H. Lee, F. Ferdinand, T. Kim, and C. Ko, “A genetic algorithm based approach to the profitable tour problem with pick-up and delivery,” Ind. Eng. Manag. Syst., vol. 9, no. 2, pp. 80–87, 2010, doi: 10.7232/iems.2010.9.2.080.
• [22] G. Nagy and D. Salhi, “Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries,” Eur. J. Oper. Res., vol. 162, pp. 126–141, 2005, doi: 10.1016/j.ejor.2002.11.003.
• [23] S. Basu and D. Ghosh, “A review of the tabu search literature on traveling salesman problem,” IIMA Working Papers, pp. 1–16, 2008.
• [24] P. Larrañaga et al., “Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators,” Artif. Intell. Rev., vol. 13, no. 2, pp. 129–170, 1999, doi: 10.1023/A:1006529012972.
• [25] G. Clarke and J.W. Wright, “Scheduling of vehicles from a depot to a number of delivery points,” Oper. Res., vol. 12, pp. 568–581, 1964.
• [26] G.B. Dantzig and R.H. Ramser, “The Truck Dispatching Problem,” Manag. Sci., vol. 6, no. 1, pp. 80–91, 1959.
• [27] B.L. Golden, T.L. Magnanti and H.Q. Nguyan, “Implementing vehicle routing algorithms,” Networks, vol. 7, no. 2, pp. 113–148, 1972, doi: 10.1002/net.3230070203.
• [28] B. Eksioglu, A.V. Vural, and A. Reisman, “The vehicle routing problem: a taxonomic review,” Comput. Ind. Eng., vol. 57, pp. 1472–1483, 2009, doi: 10.1016/j.cie.2009.05.009.
• [29] M. Gendreau, G. Laporte and J.-Y. Potvin, “Metaheuristics for the vehicle routing problem,” Technical report, Les Cahiers du GERAD, Canada, 1998.
• [30] B.S. Vaidyanathan, J.O. Matson, D.M. Miller, and J.E. Matsona, “A capacitated vehicle routing problem for just-in-time delivery,” IIE Trans., vol. 31, pp. 1083–1092, 1999.
• [31] Y. Lin, Z. Bian, S. Sun, and T. Xu, “A Two-Stage Simulated Annealing Algorithm for the Many-to-Many Milk-Run Routing Problem with Pipeline Inventory Cost,” Math. Probl. Eng., vol. 2015, p. 428925, 2015, doi: 10.1155/2015/428925.
• [32] Z. Jiang, Y. Huang, and J. Wang, “Routing for the milk-run pickup system in automobile parts supply,” in Proc. 6th CIRP-Sponsored International Conference on Digital Enterprise Technology. Advances in Intelligent and Soft Computing, G.Q. Huang, K.L. Mak, and P.G. Maropoulos, Eds., Berlin, Heidelberg: Springer, 2010, vol 66., pp. 1267–1275, doi: 10.1007/978-3-642-10430-5_97.
• [33] L. Wang, P. Liu, Y. Huang, Y. Wang, and F. Kong, “Research on Milk-Run Routing Problem of Automotive Supply Logistics Based on Genetic Algorithm,” International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012), 2013, doi: 10.1007/978-3-642-38445-5_53.
• [34] D. Ying, X. Wen-Hui, and G. Xian-Long, “Study on Multi-depot Vehicle Scheduling Problem Based on Milk-Run Model,” 2010 International Conference on Optoelectronics and Image Processing (IEEE), China, 2011, doi: 10.1109/ICOIP.2010.12.
• [35] S.J. Sadjadi, T. Jafari, and T. Amini, “A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study),” Int. J. Adv. Manuf. Technol., vol. 44, pp. 194–200. 2009, doi: 10.1007/s00170-008-1648-5.
• [36] L. Yun, G. Xianlong, and S. Chaochun, “Inventory-transportation Integrated Optimization Based on Milk-run Model,” 2010 International Conference on E-Business and E-Government, 2010, pp. 3372–3376.
• [37] D.Y. Eroglu, C. Rafele, A.C. Cagliano, S.S. Murat, and M. Ippolito, “Simultaneous routing and loading method for milk-run using hybrid genetic search algorithm,” XII International Logistics and Supply Chain Congress, Turkey, 2014.
• [38] J. Ma and G. Sun, “Mutation Ant Colony Algorithm of Milk-Run Vehicle Routing Problem with Fastest Completion Time Based on Dynamic Optimization,” Discrete Dyn. Nat. Soc., vol. 2013, p. 418436, 2013, doi: 10.1155/2013/418436.
• [39] K. Kalinowski, D. Krenczyk, I. Paprocka, W.M. Kempa, and C. Grabowik, “Ant colony optimisation for scheduling of flexible job shop with multi-resources requirements,” MATEC Web Conf., vol. 112, p. 06018, 2017, doi: 10.1051/matecconf/201711206018.
• [40] T.H.D. Nguyen and T.M. Dao, “Novel approach to optimize milk-run delivery: A case study,” IEEE International Conference on Industrial Engineering and Engineering Management, pp. 351–355, 2016, doi: 10.1109/IEEM.2015.7385667.
• [41] Z. You and Y. Jiao, “Development and Application of Milk-Run Distribution Systems in the Express Industry Based on Saving Algorithm,” Math. Probl. Eng., vol. 2014, p. 536459, 2014, doi: 10.1155/2014/536459.
• [42] A.G.N. Novaes, O.F. Lima, M.M.M. Luna and E.T. Bez, “Mitigating Supply Chain Tardiness Risks in OEM Milk-Run Operations,” in Dynamics in Logistics. Lecture Notes in Logistics, M. Freitag, H. Kotzab, and J. Pannek, Eds., Cham: Springer, 2017, doi: 10.1007/978-3-319-45117-6_13.
• [43] Y. Lin, T. Xu, and Z. Bian, “A Two-Phase Heuristic Algorithm for the Common Frequency Routing Problem with Vehicle Type Choice in the Milk Run,” Math. Probl. Eng., vol. 2015, p. 404868, 2015, doi: 10.1155/2015/404868.
• [44] H.S. Kilic, M.B. Durmusoglu, and M. Baskak, “Classification and modeling for in-plant milk-run distribution systems,” Int. J. Adv. Manuf. Technol., vol. 62, no. 9–12, pp. 1135–1146, 2012, doi: 10.1007/s00170-011-3875-4.
• [45] S. Rajagopalan and S.S. Heragu, “Advances in discrete material handling system design,” Sadhana, vol. 22, pp. 281–292, 1997, doi: 10.1007/BF02744493.
• [46] D. Sinriech and E. Samakh, “A genetic approach to the pickup/delivery station location problem in segmented flow based material handling systems,” J Manuf Syst, vol. 18, no. 2, pp. 81–99, 1999, doi: 10.1016/S0278-6125(99)80014-4.
• [47] B.S. Vaidyanathan, J.O. Matson, D.M. Miller, and J.E. Matson, “A capacitated vehicle routing problem for just in time delivery,” IIE Trans., vol. 31, no. 11, pp. 1083–1092, 1999, doi: 10.1080/07408179908969909.
• [48] H.S. Hwang, “Heuristic transporter routing model for manufacturing facility design,” Comput. Ind. Eng., vol. 46, no. 2, pp. 243–251, 2004, doi: 10.1016/j.cie.2003.12.021.
• [49] B. Costa, L.S. Dias, J.A. Oliveira, and G. Pereira, “Simulation as a tool for planning a material delivery system to manufacturing lines,” 2008 IEEE International Engineering Management Conference, Portugal, 2008, pp. 1–5, doi: 10.1109/IEMCE.2008.4618006.
• [50] D. Gyulaiac, E. Pfeiffera, T. Sobottka, and J. Váncza, “Milkrun Vehicle Routing Approach for Shop-floor Logistics,” Procedia CIRP, vol. 7, pp. 127–132, 2013, doi: 10.1016/j.procir.2013.05.022.
• [51] R. Domingo, R. Alvarez, M.M. Pena, and R. Calvo, “Materials flow improvement in a lean assembly line: a case study,” Assembly Autom., vol. 27, no. 2, pp. 141–147, 2007, doi: 10.1108/01445150710733379.
• [52] K. Musiał, J. Kotowska, D. Górnicka, and A. Burduk, “Tabu search and greedy algorithm adaptation to logistic task,” in Computer Information Systems and Industrial Management. CISIM 2017. Lecture Notes in Computer Science, K. Saeed, W. Homenda, and R. Chaki, Eds., Cham: Springer, 2017, vol. 10244, pp. 39–49, doi: 10.1007/978-3-319-59105-6_4.
• [53] W. Bożejko and J. Pempera, Optymalizacja dyskretna w informatyce, automatyce i robotyce, Oficyna Wydawnicza Politechniki Wrocławskiej, 2012.
• [54] A. Burduk and K. Musiał, “Genetic algorithm adoption to transport task optimization,” in International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, M. Graña, J. López-Guede, O. Etxaniz, A. Herrero, H. Quintián, and E. Corchado, Eds., Cham: Springer, 2017, vol. 527, pp. 366–375, doi: 10.1007/978-3-319-47364-2_35.
• [55] O. Kramer. Genetic algorithm essentials. New York: Springer, 2017, pp 11–19.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).