Optimization of the First-Mile Pickup Problem: A Real-Life Case Study
Notice bibliographique
Résumé
This study presents an optimization model for a logistics company to solve the first-mile pick-up problem.The first-mile pick-up stage is a vital element in the entire supply chain process, as inefficiencies in this phase can lead to significant delays and increased costs throughout the entire delivery network.The first-mile pick-up problem is the problem of collecting parcels from supplier companies to the target points with minimum cost.Determining optimal routes for parcel pickup vehicles is critical to minimize operational costs while meeting all constraints.Efficient routing ensures that resources such as fuel, driver hours, and vehicle capacity are effectively managed, preventing unnecessary delays and additional expenses.In the first-mile pick-up problem, there are constraints such as determining the order of visits to companies, satisfying time windows, having fixed source-target points for routes, not exceeding vehicle capacities, observing maximum distance limits, and visiting each customer point only once.Effectively addressing these constraints is essential to ensure that the model delivers practical and actionable solutions for real-world scenarios.While similar optimization models exist in the literature, none completely matches all aspects of our problem.This limitation highlights the need for a model that comprehensively addresses the unique challenges presented in first-mile logistics.Existing approaches like the Open Vehicle Routing Problem with Time Windows (OVRPTW) [1], Close-Open Vehicle Routing Problem with Time Windows (COVRPTW) [2], Multi-Depot Open Vehicle Routing Problem with Time Windows (MDOVRPTW) [3,4,5,6], and Multi-Depot Multiple Terminal Hamiltonian Path Problem (MDMTHPP) [7,8] each address different subsets of these constraints.Among these, MDOVRPTW emerges as the closest candidate to our problem requirements, however, this model does not include routing for fixed source-target points and maximum distance constraints.The first-mile pick-up problem is named as the multi-depot open vehicle routing problem with time windows and fixed target points (MDOVRPTW ft) and a mathematical model of the problem is created.The mathematical model of the problem was coded in IBM ILOG CPLEX Optimization Studio and applied to a real-life example.As a real-life example, the location of supplier companies and vehicles connected to a branch depot of a logistics company are considered.A distance matrix was created using the latitude-longitude information of the supplier companies in Open Route Service.This method ensures accurate distance calculations, which are crucial for generating optimal routes that align with real-world conditions.The observed total distance cost according to preferences of the vehicle drivers and the optimal total distance costs obtained from the model are calculated for three consecutive days.By comparing these results, the model's effectiveness in minimizing costs while ensuring practical feasibility is demonstrated.It is concluded that there is an average 49% improvement in the total distance cost for these three days.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».