Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree
A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receivin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | EURO Journal on Transportation and Logistics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2192437622000206 |
| _version_ | 1811299714696478720 |
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| author | Andrea Gallo Riccardo Accorsi Renzo Akkerman Riccardo Manzini |
| author_facet | Andrea Gallo Riccardo Accorsi Renzo Akkerman Riccardo Manzini |
| author_sort | Andrea Gallo |
| collection | DOAJ |
| description | A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock. |
| first_indexed | 2024-04-13T06:39:55Z |
| format | Article |
| id | doaj.art-1e976e2204cc4169908e84f2b9e8783b |
| institution | Directory Open Access Journal |
| issn | 2192-4384 |
| language | English |
| last_indexed | 2024-04-13T06:39:55Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EURO Journal on Transportation and Logistics |
| spelling | doaj.art-1e976e2204cc4169908e84f2b9e8783b2022-12-22T02:57:46ZengElsevierEURO Journal on Transportation and Logistics2192-43842022-01-0111100095Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios treeAndrea Gallo0Riccardo Accorsi1Renzo Akkerman2Riccardo Manzini3Department of Industrial Engineering, University of Bologna, ItalyDepartment of Industrial Engineering, University of Bologna, Italy; Corresponding author. Department of Industrial Engineering, Alma Mater Studiorum – University of Bologna, Viale Risorgimento 2, 40136, Bologna, Italy.Operations Research and Logistics Group, Wageningen University, the NetherlandsDepartment of Industrial Engineering, University of Bologna, ItalyA cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock.http://www.sciencedirect.com/science/article/pii/S2192437622000206Cross-dockingOperations schedulingUncertaintyScenario treeGA |
| spellingShingle | Andrea Gallo Riccardo Accorsi Renzo Akkerman Riccardo Manzini Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree EURO Journal on Transportation and Logistics Cross-docking Operations scheduling Uncertainty Scenario tree GA |
| title | Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree |
| title_full | Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree |
| title_fullStr | Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree |
| title_full_unstemmed | Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree |
| title_short | Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree |
| title_sort | scheduling cross docking operations under uncertainty a stochastic genetic algorithm based on scenarios tree |
| topic | Cross-docking Operations scheduling Uncertainty Scenario tree GA |
| url | http://www.sciencedirect.com/science/article/pii/S2192437622000206 |
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