Energy-Efficient Last-Mile Logistics Using Resistive Grid Path Planning Methodology (RGPPM)

Carlos Hernández-Mejía (), Delia Torres-Muñoz, Carolina Maldonado-Méndez, Sergio Hernández-Méndez, Everardo Inzunza-González, Carlos Sánchez-López and Enrique Efrén García-Guerrero
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Carlos Hernández-Mejía: ITS de Misantla, Tecnológico Nacional de México, Misantla 93850, Mexico
Delia Torres-Muñoz: Instituto Tecnológico Superior de San Martín Texmelucan, San Martín Texmelucan, Puebla 74120, Mexico
Carolina Maldonado-Méndez: Ingeniería en Computación, Instituto de Agroingeniería, Universidad del Papaloapan, Loma Bonita 68400, Mexico
Sergio Hernández-Méndez: Artificial Intelligence Research Institute, Universidad Veracruzana, Xalapa 91097, Mexico
Everardo Inzunza-González: Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada No. 3917, Ensenada 22860, Mexico
Carlos Sánchez-López: Department of Electronics, Autonomous University of Tlaxcala, Clzda Apizaquito S/N, km. 1.5, Apizaco 90300, Mexico
Enrique Efrén García-Guerrero: Facultad de Ingeniería, Arquitectura y Diseño, Universidad Autónoma de Baja California, Carretera Tijuana-Ensenada No. 3917, Ensenada 22860, Mexico

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO 2 emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics.

Keywords: RGPPM algorithm; path planning methods; georeferenced grid; energy-efficient delivery; multi-agent logistics; carbon emissions reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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