The selective multiple depot pickup and delivery problem with multiple time windows and paired demand
A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associa...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Operations Research Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214716025000181 |
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| author | Daniël Roelink Giovanni Campuzano Martijn Mes Eduardo Lalla-Ruiz |
| author_facet | Daniël Roelink Giovanni Campuzano Martijn Mes Eduardo Lalla-Ruiz |
| author_sort | Daniël Roelink |
| collection | DOAJ |
| description | A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (i) profit maximization including CO2 costs, (ii) soft time windows, and (iii) soft time windows including CO2 costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO2 costs shows a slight sensitivity of the routing schedules to the CO2 emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible. |
| format | Article |
| id | doaj-art-fb6a0d8a321e4e47a358fc31138adbfe |
| institution | OA Journals |
| issn | 2214-7160 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Operations Research Perspectives |
| spelling | doaj-art-fb6a0d8a321e4e47a358fc31138adbfe2025-08-20T02:37:17ZengElsevierOperations Research Perspectives2214-71602025-06-011410034210.1016/j.orp.2025.100342The selective multiple depot pickup and delivery problem with multiple time windows and paired demandDaniël Roelink0Giovanni Campuzano1Martijn Mes2Eduardo Lalla-Ruiz3Department of High-Tech Business and Entrepreneurship, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The NetherlandsFacultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastian, Lago Panguipulli 1390, Puerto Montt, 5501842, Chile; Department of Maritime and Transport Technology, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, The NetherlandsDepartment of High-Tech Business and Entrepreneurship, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The NetherlandsDepartment of High-Tech Business and Entrepreneurship, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands; Corresponding author.A recurring challenge for transportation companies is the inefficiency of returning (partially) empty vehicles, or backhauling, after delivering orders. To address this issue, companies search on freight exchange platforms for profitable pickup and delivery orders, aiming to reduce the costs associated with empty return trips. The increasing reliance on freight exchange platforms presents both an opportunity and a challenge: while they offer access to profitable loads, effectively selecting the right combination of orders to maximize returns is challenging. This paper addresses this challenge by introducing the Selective Multiple Depot Pickup and Delivery Problem with Multiple Time Windows and Paired Demand (SMDPDPMTWPD). We formulate the SMDPDPMTWP as a Mixed-Integer Linear Program (MILP) to maximize profit and optimize freight selection for return trips. In addition to the main model, three problem extensions are proposed: (i) profit maximization including CO2 costs, (ii) soft time windows, and (iii) soft time windows including CO2 costs. Given the complexity of the problem, we develop an Adaptive Large Neighborhood Search (ALNS) metaheuristic to solve large instances within reasonable computing times and compare it with a Simulated Annealing (SA) heuristic. Results show that ALNS outperforms SA and finds the same optimal solutions as the MILP formulation for small instances. Furthermore, ALNS achieves an average improvement of 308.17% over the initial solutions for the profit maximization variant. The model variant with CO2 costs shows a slight sensitivity of the routing schedules to the CO2 emissions costs, whereas we observe a significant change when allowing soft time windows. Finally, soft time windows significantly increase the profits earned compared to the hard time windows (179.54% on average), due to the additional flexibility created when late arrivals are possible.http://www.sciencedirect.com/science/article/pii/S2214716025000181Vehicle Routing ProblemAdaptive Large Neighborhood SearchSimulated AnnealingBackhaulingFreight selectionFreight exchange |
| spellingShingle | Daniël Roelink Giovanni Campuzano Martijn Mes Eduardo Lalla-Ruiz The selective multiple depot pickup and delivery problem with multiple time windows and paired demand Operations Research Perspectives Vehicle Routing Problem Adaptive Large Neighborhood Search Simulated Annealing Backhauling Freight selection Freight exchange |
| title | The selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| title_full | The selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| title_fullStr | The selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| title_full_unstemmed | The selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| title_short | The selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| title_sort | selective multiple depot pickup and delivery problem with multiple time windows and paired demand |
| topic | Vehicle Routing Problem Adaptive Large Neighborhood Search Simulated Annealing Backhauling Freight selection Freight exchange |
| url | http://www.sciencedirect.com/science/article/pii/S2214716025000181 |
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