Cross-docking cargo delivery routing for guaranteed minimum period

dc.citation.epage54
dc.citation.issue1
dc.citation.spage38
dc.contributor.affiliationLviv National University of Nature Management
dc.contributor.affiliationNational Transport University
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorOliskevych, Myroslav
dc.contributor.authorDanchuk, Viktor
dc.contributor.authorMastykash, Oleksandr
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2022-06-15T07:17:30Z
dc.date.available2022-06-15T07:17:30Z
dc.date.created2022-03-01
dc.date.issued2022-03-01
dc.description.abstractСтаття присвячена проблемі успішного застосування кросс-докінгу, як технології доставки вантажів за підвищених вимог до термінів, що дозволяє розв’язувати суперечності між забезпеченням гарантованих термінів доставки і ефективності використання наявного парку вантажівок. Процес організації доставки розглядається як упорядкування на транспортній мережі множини дискретних вантажопотоків у вигляді його фаз. Якщо від фази до фази з потоком не відбуваються якісні, і/або кількісні зміни, то такт такого потоку є сталим. Проте вантажопотоки при кросс-докінгу змінюють за переміщення розмір гурту. Вантажі можна переміщати за призначенням довільним гуртом, розміри якого, однак, є обмежені максимальним та мінімальним значенням розмірів гурту. Розроблено двостадійний алгоритм розв’язання задачі. Транспортна мережа представлена у вигляді графа. Зміст задачі пошуку маршрутів є оптимізаційним, оскільки полягає у множинному виборі з початкового графа дуг при наявності обмежень на вхідні і вихідні потоки. Потрібно кожне ребро графа замінити на дугу прямого або зворотного напряму, або видалити це ребро. Критерій оптимальності розв’язку задачі, який застосовано – мінімальна гарантована тривалість доставки вантажів по усій сукупності заданих вантажопотоків. На першій стадії алгоритму виконано пошук найкоротших шляхів у графі, по яких може проходити кожен із заданих вантажопотоків. Перша стадія оптимізації є лінійною задачею цілочислового програмування, розмірність не є надто великою. Початковими даними для другої стадії є матриця вантажопотоків, яка отримана в результаті оптимізації на першій стадії. Зміст другої стадії алгоритму – це розв’язок рівняння балансу дискретних вантажопотоків. Рівняння балансу означає, що усі потоки, які входять у кожну вершину, включно із джерелами вантажопотоків даної вершини, мають середню інтенсивність, яка дорівнює інтенсивності вихідних вантажопотоків з кожної вихідної вершини, включно зі стоками. Завдяки дослідженим залежностям між окремими фазами процесу доставки на прикладі вантажного перевізника на транспортній мережі України, сформульованим обмеженням і крайовим умовам отримано можливість гарантованого точного розв’язання комплексної проблеми. При цьому знайдено найкоротші маршрути, визначено пункти перевантаження, а також часові параметри експлуатації і ступінь завантаження автомобілів. За результатами проведених досліджень отримано трикратне підвищення продуктивності використання парку автопоїздів із зниженням термінів гарантованої тривалості доставки на 30 %
dc.description.abstractThe article is devoted to the problem of effective use of cross-docking as a technology of cargo delivery with increased time requirements, which allows to resolve the contradictions of guaranteed delivery time ensuring and the efficiency of the existing fleet of trucks. The process of delivery organization is considered as the ordering on the transport network of many discrete freight flows in the form of their phases. If qualitative and / or quantitative changes do not occur from phase to phase with the flow, then the tact of such flow is constant. However, crossdocking flows change the size of the band of moving goods. Cargo can be moved as intended by any group size, which, however, is limited by the maximum and minimum values. A two-stage algorithm for solving the problem has been developed. The transport network is represented as a graph. The content of the route search problem is optimization, as it consists of multiple selections from the initial graph of arcs in the presence of restrictions on input and output flows. One needs to replace every each edge of the graph with an arc of the forward or reverse direction, or remove this edge. The criterion for the optimal solution of the problem, which is applied, is the minimum guaranteed duration of delivery of goods throughout the set of specified freight flows. At the first stage of the algorithm, the search for the shortest paths in the graph is performed, along which every given cargo flow can pass. The first stage of optimization is a linear problem of integer programming, the dimension of which is not too large. The initial data of the second stage is freight flows matrix, which is obtained as a result of optimization in the first stage. The content of the second stage of the algorithm is the solution of the equation of the balance of discrete goods flows. The balance equation means that all flows entering each peak including the sources of cargo flows of this peak have an average intensity equal to the intensity of the outgoing cargo flows from each source peak, including runoff. Due to the studied dependencies between the individual phases of the delivery process on the example of a cargo carrier on the transport network of Ukraine, the formulated restrictions and boundary conditions, the possibility of guaranteed accurate solution of a complex problem is obtained. At the same time, the shortest routes were found, reloading points were identified as well as time parameters of operation and the degree of loading of cars. According to the results of the research, a threefold increase in the productivity of the fleet of road trains with a reduction in the guaranteed delivery time by 30 %
dc.format.extent38-54
dc.format.pages17
dc.identifier.citationOliskevych M. Cross-docking cargo delivery routing for guaranteed minimum period / Myroslav Oliskevych, Viktor Danchuk, Oleksandr Mastykash // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 1. — P. 38–54.
dc.identifier.citationenOliskevych M. Cross-docking cargo delivery routing for guaranteed minimum period / Myroslav Oliskevych, Viktor Danchuk, Oleksandr Mastykash // Transport Technologies. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 3. — No 1. — P. 38–54.
dc.identifier.doidoi.org/10.23939/tt2022.01.038
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56932
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofTransport Technologies, 1 (3), 2022
dc.relation.references1. Kiani Mavi, R., Goh, M., Kiani Mavi, N., Jie, F., Brown, K., Biermann, S., & A Khanfar, A. (2020). Crossdocking: a systematic literature review. Sustainability, 12(11), 4789 (1–19). doi: 10.3390/su12114789 (in English)
dc.relation.references2. Santos, F. A., Mateus, G. R., & Da Cunha, A. S. (2013). The pickup and delivery problem with crossdocking. Computers & Operations Research, 40(4), 1085–1093. doi: 10.1016/j.cor.2012.11.021 (in English)
dc.relation.references3. Theophilus, O., Dulebenets, M. A., Pasha, J., Lau, Y. Y., Fathollahi-Fard, A. M., & Mazaheri, A. (2021). Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations. Computers & Industrial Engineering, 156, 107240. doi: 10.1016/j.cie.2021.107240 (in English)
dc.relation.references4. Buijs, P., Vis, I. F., & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational Research, 239(3), 593–608. doi: 10.1016/j.ejor.2014. 03.012 (in English)
dc.relation.references5. Contardo, C., Hemmelmayr, V., & Crainic, T. G. (2012). Lower and upper bounds for the two-echelon capacitated location-routing problem. Computers & operations research, 39(12), 3185–3199. doi: 10.1016/j.cor.2012. 04.003 (in English)
dc.relation.references6. Wen, M., Larsen, J., Clausen, J., Cordeau, J. F., & Laporte, G. (2009). Vehicle routing with cross-docking. Journal of the Operational Research Society, 60(12), 1708–1718. doi: 10.1057/jors.2008.108 (in English)
dc.relation.references7. Nurprihatin, F., Rembulan, G. D., Christianto, K., & Hartono, H. (2021, March). Decision support system for truck scheduling in logistic network through cross-docking strategy. Journal of Physics: Conference Series. 1811(1), 012009. doi: 10.1088/1742-6596/1811/1/012009 (in English)
dc.relation.references8. Huang, H., Savkin, A. V., & Huang, C. (2020). Scheduling of a parcel delivery system consisting of an aerial drone interacting with public transportation vehicles. Sensors, 20(7), 2045. doi: 10.3390/s20072045 (in English)
dc.relation.references9. Han, Y. Q., Li, J. Q., Liu, Z., Liu, C., & Tian, J. (2020). Metaheuristic algorithm for solving the multiobjective vehicle routing problem with time window and drones. International Journal of Advanced Robotic Systems, 17(2), 1729881420920031. doi: 10.1177%2F1729881420920031 (in English)
dc.relation.references10. Buakum, D., & Wisittipanich, W. (2020). Stochastic internal task scheduling in cross docking using chanceconstrained programming. International Journal of Management Science and Engineering Management, 15(4), 258–264. doi: 10.1080/17509653.2020.1764404 (in English)
dc.relation.references11. An Integrated Routing-scheduling Model for a Hybrid UAV-based Delivery System. Retrieved from: https://www.cirrelt.ca/documentstravail/cirrelt-2020-17.pdf (in English)
dc.relation.references12. Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12(1), 1–11. doi: 10.1186/s12544-020-00409-7 (in English)
dc.relation.references13. Oliskevych, M., Kovalyshyn, S., Magats, M., Shevchuk, V., & Sukach, O. (2020). The optimization of trucks fleet schedule in view of their interaction and restrictions of the European agreement of work of crews. Transport Problems, 15(2). 157–170. doi: 10.21307/tp-2020-028 (in English)
dc.relation.references14. Küçükoğlu, İ., & Öztürk, N. (2017). Two-stage optimisation method for material flow and allocation management in cross-docking networks. International Journal of Production Research, 55(2), 410–429. doi: 10.1080/00207543.2016.1184346 (in English)
dc.relation.references15. Chargui, T., Bekrar, A., Reghioui, M., & Trentesaux, D. (2019). Multi-objective sustainable truck scheduling in a rail–road physical internet cross-docking hub considering energy consumption. Sustainability, 11(11), 3127 (1–23). doi: 10.3390/su11113127 (in English)
dc.relation.references16. Gunawan, A., Widjaja, A. T., Gan, B., Yu, V. F., & Jodiawan, P. (2020). Vehicle routing problem for multi-product cross-docking. Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 66–77). Dubai, UAE (in English)
dc.relation.references17. Hochbaum, D. S., & Levin, A. (2006). Cyclical scheduling and multi-shift scheduling: Complexity and approximation algorithms. Discrete Optimization, 3(4), 327–340. doi: 10.1016/j.disopt.2006.02.002 (in English)
dc.relation.references18. Song, X., Jones, D., Asgari, N., & Pigden, T. (2020). Multi-objective vehicle routing and loading with time window constraints: a real-life application. Annals of Operations Research, 291(1), 799–825. doi: 10.1007/s10479-019-03205-2 (in English)
dc.relation.references19. Zhang, C., Song, W., Cao, Z., Zhang, J., Tan, P. S., & Chi, X. (2020). Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in Neural Information Processing Systems, 33, 1621–1632. doi: 10.48550/arXiv.2010.12367 (in English)
dc.relation.referencesen1. Kiani Mavi, R., Goh, M., Kiani Mavi, N., Jie, F., Brown, K., Biermann, S., & A Khanfar, A. (2020). Crossdocking: a systematic literature review. Sustainability, 12(11), 4789 (1–19). doi: 10.3390/su12114789 (in English)
dc.relation.referencesen2. Santos, F. A., Mateus, G. R., & Da Cunha, A. S. (2013). The pickup and delivery problem with crossdocking. Computers & Operations Research, 40(4), 1085–1093. doi: 10.1016/j.cor.2012.11.021 (in English)
dc.relation.referencesen3. Theophilus, O., Dulebenets, M. A., Pasha, J., Lau, Y. Y., Fathollahi-Fard, A. M., & Mazaheri, A. (2021). Truck scheduling optimization at a cold-chain cross-docking terminal with product perishability considerations. Computers & Industrial Engineering, 156, 107240. doi: 10.1016/j.cie.2021.107240 (in English)
dc.relation.referencesen4. Buijs, P., Vis, I. F., & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational Research, 239(3), 593–608. doi: 10.1016/j.ejor.2014. 03.012 (in English)
dc.relation.referencesen5. Contardo, C., Hemmelmayr, V., & Crainic, T. G. (2012). Lower and upper bounds for the two-echelon capacitated location-routing problem. Computers & operations research, 39(12), 3185–3199. doi: 10.1016/j.cor.2012. 04.003 (in English)
dc.relation.referencesen6. Wen, M., Larsen, J., Clausen, J., Cordeau, J. F., & Laporte, G. (2009). Vehicle routing with cross-docking. Journal of the Operational Research Society, 60(12), 1708–1718. doi: 10.1057/jors.2008.108 (in English)
dc.relation.referencesen7. Nurprihatin, F., Rembulan, G. D., Christianto, K., & Hartono, H. (2021, March). Decision support system for truck scheduling in logistic network through cross-docking strategy. Journal of Physics: Conference Series. 1811(1), 012009. doi: 10.1088/1742-6596/1811/1/012009 (in English)
dc.relation.referencesen8. Huang, H., Savkin, A. V., & Huang, C. (2020). Scheduling of a parcel delivery system consisting of an aerial drone interacting with public transportation vehicles. Sensors, 20(7), 2045. doi: 10.3390/s20072045 (in English)
dc.relation.referencesen9. Han, Y. Q., Li, J. Q., Liu, Z., Liu, C., & Tian, J. (2020). Metaheuristic algorithm for solving the multiobjective vehicle routing problem with time window and drones. International Journal of Advanced Robotic Systems, 17(2), 1729881420920031. doi: 10.1177%2F1729881420920031 (in English)
dc.relation.referencesen10. Buakum, D., & Wisittipanich, W. (2020). Stochastic internal task scheduling in cross docking using chanceconstrained programming. International Journal of Management Science and Engineering Management, 15(4), 258–264. doi: 10.1080/17509653.2020.1764404 (in English)
dc.relation.referencesen11. An Integrated Routing-scheduling Model for a Hybrid UAV-based Delivery System. Retrieved from: https://www.cirrelt.ca/documentstravail/cirrelt-2020-17.pdf (in English)
dc.relation.referencesen12. Calabrò, G., Torrisi, V., Inturri, G., & Ignaccolo, M. (2020). Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization. European Transport Research Review, 12(1), 1–11. doi: 10.1186/s12544-020-00409-7 (in English)
dc.relation.referencesen13. Oliskevych, M., Kovalyshyn, S., Magats, M., Shevchuk, V., & Sukach, O. (2020). The optimization of trucks fleet schedule in view of their interaction and restrictions of the European agreement of work of crews. Transport Problems, 15(2). 157–170. doi: 10.21307/tp-2020-028 (in English)
dc.relation.referencesen14. Küçükoğlu, İ., & Öztürk, N. (2017). Two-stage optimisation method for material flow and allocation management in cross-docking networks. International Journal of Production Research, 55(2), 410–429. doi: 10.1080/00207543.2016.1184346 (in English)
dc.relation.referencesen15. Chargui, T., Bekrar, A., Reghioui, M., & Trentesaux, D. (2019). Multi-objective sustainable truck scheduling in a rail–road physical internet cross-docking hub considering energy consumption. Sustainability, 11(11), 3127 (1–23). doi: 10.3390/su11113127 (in English)
dc.relation.referencesen16. Gunawan, A., Widjaja, A. T., Gan, B., Yu, V. F., & Jodiawan, P. (2020). Vehicle routing problem for multi-product cross-docking. Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 66–77). Dubai, UAE (in English)
dc.relation.referencesen17. Hochbaum, D. S., & Levin, A. (2006). Cyclical scheduling and multi-shift scheduling: Complexity and approximation algorithms. Discrete Optimization, 3(4), 327–340. doi: 10.1016/j.disopt.2006.02.002 (in English)
dc.relation.referencesen18. Song, X., Jones, D., Asgari, N., & Pigden, T. (2020). Multi-objective vehicle routing and loading with time window constraints: a real-life application. Annals of Operations Research, 291(1), 799–825. doi: 10.1007/s10479-019-03205-2 (in English)
dc.relation.referencesen19. Zhang, C., Song, W., Cao, Z., Zhang, J., Tan, P. S., & Chi, X. (2020). Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in Neural Information Processing Systems, 33, 1621–1632. doi: 10.48550/arXiv.2010.12367 (in English)
dc.relation.urihttps://www.cirrelt.ca/documentstravail/cirrelt-2020-17.pdf
dc.rights.holder© Національний університет “Львівська політехніка”, 2022
dc.rights.holder© M. Oliskevych, V. Danchuk, O. Mastykash, 2022
dc.subjectоставка вантажів
dc.subjectкросс-докінг
dc.subjectмаршрутизація
dc.subjectдискретна оптимізація
dc.subjectcargo delivery
dc.subjectcross-docking
dc.subjectrouting
dc.subjectdiscrete optimization
dc.titleCross-docking cargo delivery routing for guaranteed minimum period
dc.title.alternativeМаршрутизація доставки вантажів з кросс-докінгом за гарантованих мінімальних термінів
dc.typeArticle

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