| Grant number: | 22/10303-0 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | October 01, 2022 |
| End date: | March 31, 2026 |
| Field of knowledge: | Engineering - Production Engineering - Operational Research |
| Principal Investigator: | Reinaldo Morabito Neto |
| Grantee: | Kamyla Maria Ferreira |
| Host Institution: | Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil |
| Associated research grant: | 16/01860-1 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM |
| Associated scholarship(s): | 23/07988-3 - Vehicle routing problem with multiple commodities, split-delivery and pollutant emission constraints: formulations and exact methods, BE.EP.PD |
Abstract In this project, we address the Green Vehicle Routing Problem with Two-Dimensional Loading Constraints and Split Delivery, referred to as G2L-SDVRP. This variant incorporates practical constraints since split delivery allows a customer to be served by more than one vehicle when it is advantageous. Furthermore, the two-dimensional loading constraints allow modeling customer demand as rectangular items that are loaded on the rectangular base of vehicles without overlapping and respecting the base dimensions. Finally, the aim is to reduce the amount of CO2 emissions produced by transporting loads to customers, introducing the requirement of sustainability which is of great relevance at the moment. We intend to investigate and develop new mathematical formulations and exact branch-and-cut and branch-price-and-cut methods for G2L-SDVRP and related variants. To solve large instances, that are more consistent with practical problems, we aim to develop a metaheuristic, not yet explored in the literature, for solving G2L-SDVRP. The proposed methods will be implemented and analyzed through extensive computational experiments with literature instances. In this context, we expect to contribute with the proposal of new exact and heuristic solution models and methods for the G2L-SDVRP that are computationally efficient for decision support. (AU) | |
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