Algoritmos inteligentes para el Shared Customer Collaboration Vehicle Routing Problem centrado en el cliente

Abstract

Esta memoria propone un modelo colaborativo de optimizaci´on de rutas para log´ıstica urbana, combinando el Shared Customer Collaboration Vehicle Routing Problem (SCC−VRP) con el Cumulative Capacitated Vehicle Routing Problem (CCVRP). El SCC−VRP permite a transportistas independientes colaborar para atender clientes compartidos, reduciendo costes, mientras que el CCVRP prioriza minimizar los tiempos acumulados de llegada, mejorando la eficiencia del servicio. Se propone un nuevo modelo denominado Transporte Colaborativo con Objetivo de Latencia, integrando ambos enfoques, con dos formulaciones matem´aticas: basada en veh´ıculos (VF) y en la carga (LF). Ambas se resuelven con Programaci´on Lineal Entera Mixta implementadas en Python utilizando el solver Gurobi y se validan mediante pruebas computacionales.

This memory proposes a collaborative route optimization model for urban logistics, combining the Shared Customer Collaboration Vehicle Routing Problem (SCC-VRP) with the Cumulative Capacitated Vehicle Routing Problem (CCVRP). The SCC-VRP allows independent carriers to collaborate in serving shared customers, reducing costs, while the CCVRP prioritizes minimizing cumulative arrival times to improve service efficiency. A new model, called Collaborative Transportation with Latency Objective, is introduced by integrating both approaches, with two mathematical formulations: vehicle-based (VF) and load-based (LF). Both are solved using Mixed-Integer Linear Programming, implemented in Python with the Gurobi solver, and validated through computational experiments.