Integrated Algorithmic Strategies for Online Food Delivery Routing: A Multi-Stakeholder Optimization Approach
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| Publicado en: | Processes vol. 13, no. 10 (2025), p. 3211-3234 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/pr13103211 |2 doi | |
| 035 | |a 3265942209 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Seçkin, Ünver | |
| 245 | 1 | |a Integrated Algorithmic Strategies for Online Food Delivery Routing: A Multi-Stakeholder Optimization Approach | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The dynamic and time-sensitive nature of online food delivery, along with real-world factors like sudden changes in order volumes and the availability of couriers, distinguishes it from traditional vehicle routing scenarios. Apart from the many studies in the literature that handle this problem from specific angles, our solution proposes a new approach that provides real-time routing with the awareness of the expectations of multiple stakeholders in the ecosystem. For this purpose, we develop a Mixed Integer Programming (MIP) model that minimizes unmet demand and workforce requirements simultaneously to meet platform and courier expectations while maintaining the timeliness of the operation to meet restaurant and customer expectations. Since the model requires more time to provide good results for even small-size problems, we develop a multi-step algorithmic approach supported by strategies that hold or dissolve a part of the solutions to create opportunities for better results. A framework for agent-based simulation was created to implement the strategies and the algorithmic steps, accurately mimicking the operations and movements of couriers. The effectiveness of this solution was evaluated through experiments based on a real-world case study. The results indicate that our solution can generate high-quality results in a short time across various configurations, which are defined by different demand and supply patterns and varying problem sizes. | |
| 653 | |a Customer services | ||
| 653 | |a Simulation | ||
| 653 | |a Linear programming | ||
| 653 | |a Integer programming | ||
| 653 | |a Customer satisfaction | ||
| 653 | |a Restaurants | ||
| 653 | |a Food | ||
| 653 | |a Optimization | ||
| 653 | |a Profits | ||
| 653 | |a Quality of service | ||
| 653 | |a Algorithms | ||
| 653 | |a Mixed integer | ||
| 653 | |a Stakeholders | ||
| 653 | |a Vehicle routing | ||
| 653 | |a Real time | ||
| 653 | |a Logistics | ||
| 653 | |a Food quality | ||
| 653 | |a Ecosystems | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Gülfem, Tuzkaya | |
| 700 | 1 | |a Serol, Bulkan | |
| 773 | 0 | |t Processes |g vol. 13, no. 10 (2025), p. 3211-3234 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265942209/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265942209/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265942209/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |