{"ID":2892091,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15282","arxiv_id":"2507.15282","title":"Predict, Reposition, and Allocate: A Greedy and Flow-Based Architecture for Sustainable Urban Food Delivery","abstract":"The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimization objective. This study proposes a novel eco-friendly food delivery optimization framework that integrates demand prediction, delivery person routing, and order allocation to minimize environmental impact while maintaining service efficiency. Since recommending routes is NP-Hard, the proposed approach utilizes the submodular and monotone properties of the objective function and designs an efficient greedy optimization algorithm. Thereafter, it formulates order allocation problem as a network flow optimization model, which, to the best of our knowledge, has not been explored in the context of food delivery. A three-layered network architecture is designed to match orders with delivery personnel based on capacity constraints and spatial demand. Through this framework, the proposed approach reduces the vehicle count, and creates a sustainable food delivery ecosystem.","short_abstract":"The rapid proliferation of food delivery platforms has reshaped urban mobility but has also contributed significantly to environmental degradation through increased greenhouse gas emissions. Existing optimization mechanisms produce sub-optimal outcomes as they do not consider environmental sustainability their optimiza...","url_abs":"https://arxiv.org/abs/2507.15282","url_pdf":"https://arxiv.org/pdf/2507.15282v1","authors":"[\"Aqsa Ashraf Makhdomi\",\"Iqra Altaf Gillani\"]","published":"2025-07-21T06:31:40Z","proceeding":"cs.DS","tasks":"[\"cs.DS\"]","methods":"[]","has_code":false}