Delivering data: A real-world dataset for last-mile delivery optimizationZenodo
This dataset was collected to support Vehicle Routing Problem (VRP) optimization by providing structured time and distance matrices. A Third-Party Logistics (3PL) company granted access to its order management software, from which data on daily delivery problems involving pharmaceutical distribution...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925004895 |
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| author | Anna Vrani Savvas D. Apostolidis Athanasios Ch. Kapoutsis Elias B. Kosmatopoulos |
| author_facet | Anna Vrani Savvas D. Apostolidis Athanasios Ch. Kapoutsis Elias B. Kosmatopoulos |
| author_sort | Anna Vrani |
| collection | DOAJ |
| description | This dataset was collected to support Vehicle Routing Problem (VRP) optimization by providing structured time and distance matrices. A Third-Party Logistics (3PL) company granted access to its order management software, from which data on daily delivery problems involving pharmaceutical distribution were obtained. The dataset consists of carefully processed distance and time matrices, over a period of nine days. Each day’s problem involved 60-85 delivery stops that needed to be serviced. While the actual delivery routes covered only specific paths taken on the road, the generated matrices provide a complete view of travel distances and times between all locations, information essential for optimizing the routing process. To ensure confidentiality, only the structured matrices are provided, without the original address data. These matrices were generated using an API that computes travel durations based on historical traffic patterns, real-time data, and predictive models.From the API, we derived four distinct matrices: one for distances and three for travel times under different traffic scenarios: optimistic, pessimistic, and most likely. These matrices enable the modelling of realistic travel conditions accounting for the road congestion variability. Data retrieval was performed through automated API queries, ensuring consistency in structure and format. The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. It also supports statistical analysis and simulation, allowing researchers to assess travel time variability and model uncertainty in routing decisions through Monte Carlo simulations.Overall, this dataset offers valuable insights for optimizing delivery operations and addressing real-world logistics challenges. Its structured format, comprehensive traffic-based travel times, and applicability to VRP make it a valuable resource at the intersection of academia and industry. |
| format | Article |
| id | doaj-art-cdc7f178424240faab03de415bb80502 |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-cdc7f178424240faab03de415bb805022025-08-20T02:59:41ZengElsevierData in Brief2352-34092025-08-016111176210.1016/j.dib.2025.111762Delivering data: A real-world dataset for last-mile delivery optimizationZenodoAnna Vrani0Savvas D. Apostolidis1Athanasios Ch. Kapoutsis2Elias B. Kosmatopoulos3Corresponding author.; Information Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceInformation Technologies Institute, The Centre for Research and Technology, Hellas, Thessaloniki 57001, GreeceThis dataset was collected to support Vehicle Routing Problem (VRP) optimization by providing structured time and distance matrices. A Third-Party Logistics (3PL) company granted access to its order management software, from which data on daily delivery problems involving pharmaceutical distribution were obtained. The dataset consists of carefully processed distance and time matrices, over a period of nine days. Each day’s problem involved 60-85 delivery stops that needed to be serviced. While the actual delivery routes covered only specific paths taken on the road, the generated matrices provide a complete view of travel distances and times between all locations, information essential for optimizing the routing process. To ensure confidentiality, only the structured matrices are provided, without the original address data. These matrices were generated using an API that computes travel durations based on historical traffic patterns, real-time data, and predictive models.From the API, we derived four distinct matrices: one for distances and three for travel times under different traffic scenarios: optimistic, pessimistic, and most likely. These matrices enable the modelling of realistic travel conditions accounting for the road congestion variability. Data retrieval was performed through automated API queries, ensuring consistency in structure and format. The collected matrices were processed and structured for direct use in VRP algorithms.The dataset offers substantial reuse potential by serving as a benchmark for evaluating VRP algorithms, enabling the comparison of optimization methods based on real-world logistics problems. It also supports statistical analysis and simulation, allowing researchers to assess travel time variability and model uncertainty in routing decisions through Monte Carlo simulations.Overall, this dataset offers valuable insights for optimizing delivery operations and addressing real-world logistics challenges. Its structured format, comprehensive traffic-based travel times, and applicability to VRP make it a valuable resource at the intersection of academia and industry.http://www.sciencedirect.com/science/article/pii/S2352340925004895VRP benchmarkDistance matrixLogistics optimizationPharmaceutical deliveries |
| spellingShingle | Anna Vrani Savvas D. Apostolidis Athanasios Ch. Kapoutsis Elias B. Kosmatopoulos Delivering data: A real-world dataset for last-mile delivery optimizationZenodo Data in Brief VRP benchmark Distance matrix Logistics optimization Pharmaceutical deliveries |
| title | Delivering data: A real-world dataset for last-mile delivery optimizationZenodo |
| title_full | Delivering data: A real-world dataset for last-mile delivery optimizationZenodo |
| title_fullStr | Delivering data: A real-world dataset for last-mile delivery optimizationZenodo |
| title_full_unstemmed | Delivering data: A real-world dataset for last-mile delivery optimizationZenodo |
| title_short | Delivering data: A real-world dataset for last-mile delivery optimizationZenodo |
| title_sort | delivering data a real world dataset for last mile delivery optimizationzenodo |
| topic | VRP benchmark Distance matrix Logistics optimization Pharmaceutical deliveries |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925004895 |
| work_keys_str_mv | AT annavrani deliveringdataarealworlddatasetforlastmiledeliveryoptimizationzenodo AT savvasdapostolidis deliveringdataarealworlddatasetforlastmiledeliveryoptimizationzenodo AT athanasioschkapoutsis deliveringdataarealworlddatasetforlastmiledeliveryoptimizationzenodo AT eliasbkosmatopoulos deliveringdataarealworlddatasetforlastmiledeliveryoptimizationzenodo |