Mexico is located within the so-called Fire Belt which makes it susceptible to earthquakes. In fact, two-thirds of the Mexican territory have a significant seismic risk. On the other hand, the country’s location in the tropical zone makes it susceptible to hurricanes which are generated in both the Pacific and Atlantic Oceans. Due to these situations, each year many communities are affected by diverse natural disasters in Mexico and efficient logistic systems are required to provide prompt support. This work is aimed at providing an efficient metaheuristic to determine the most appropriate location for support centers in the State of Veracruz, which is one of the most affected regions in Mexico. The metaheuristic is based on the K-Means Clustering (KMC) algorithm which is extended to integrate (a) the associated capacity restrictions of the support centers, (b) a micro Genetic Algorithm μGA to estimate a search interval for the most suitable number of support centers, (c) variable number of assigned elements to centers in order to add flexibility to the assignation task, and (d) random-based decision model to further improve the final assignments. These extensions on the KMC algorithm led to the GRASP-Capacitated K-Means Clustering (GRASP-CKMC) algorithm which was able to provide very suitable solutions for the establishment of 260 support centers for 3837 communities at risk in Veracruz, Mexico. Validation of the GRASP-CKMC algorithm was performed with well-known test instances and metaheuristics. The validation supported its suitability as alternative to standard metaheuristics such as Capacitated K-Means (CKM), Genetic Algorithms (GA), and Variable Neighborhood Search (VNS).

Objetivo: Estudiar el sistema de producción agrícola de traspatio con un enfoque logístico, asumiendo que el conocimiento de los eslabones que componen la cadena de suministro contribuirá a desarrollar estrategias productivas y operacionales que vinculen a los productores con cadenas agroalimentarias cortas. Diseño/metodología/aproximación: Se presenta el caso de estudio de las localidades de alta marginación y presencia indígena de los municipios de Chontla y Tempoal, Veracruz. Se analizó la cadena de productos agrícolas de traspatio a partir de un modelo genérico identificando los productos del caso de estudio y realizando una proyección económica anualizada. La información se recolectó con muestreo de oportunidad mediante encuestas a productores de traspatio, intermediarios, comercializadores minoristas y mayoristas, agentes municipales y autoridades locales. Resultados: La cadena de suministro de productos agrícolas de traspatio está definida por cinco eslabones: producción de materias primas, procesamiento, manufactura, presentación del producto y consumidor final; el esquema de comercialización es semiestructurado entre tradicional y de circuitos cortos. Se identificaron cuatro áreas de oportunidad: procesamiento no estandarizado, condiciones inapropiadas de almacenamiento, ausencia de registros y deficientes canales de distribución y comercialización. Limitaciones del estudio/implicaciones: La proyección económica indicó que 35.8 % de los productos se destina al autoconsumo, 21.2 % se comercializa en mercados locales y municipales y el 43 % de la producción no se comercializa. Hallazgos/conclusiones: Para disminuir la cantidad de producto no comercializado y fortalecer la economía local, se propone ubicar estratégicamente centros de acopio para vincular a los productores en cadenas agroalimentarias cortas.

In order to define chain strategies for a backyard agricultural production system, this study identified agricultural products produced by high development priority communities with indigenous presence located in the Chontla and Tempoal municipalities in Veracruz, Mexico. The production system was integrated into a generic model decomposing the supply chain into hierarchical components. Data were collected through a face-to-face semistructured questionnaire based on statistical sampling of rural households, backyard producers, intermediaries, retailers, and wholesalers, as well as municipal authorities. The research was carried out from January 2017 to June 2018 and a computational program was designed to analyze the data. As a result, products were identified and their production destinations were quantified. Moreover, it revealed a backyard agricultural supply chain of five echelons with opportunities for improvement in areas such as unstructured agricultural processes and practices, inappropriate product storage and handling, and the lack of production records, and up to three intermediaries that sequentially drove up product cost in regional consumption centers. In this study, 20.9% of total production was destined for sale, 34.8% for self-consumption, and 44.2% was noncommercialized. Nopal, creole pumpkin, coriander, plum, passion fruit, and jobo were products with greater economic value for noncommercialized production. An improvement strategy would be to build inclusive agro-food chains through consolidated centers of backyard agricultural products.

Background: The Bullwhip and Ripple effects are systemic phenomena that disrupt supply chain performance. However, research often neglects their connection to resilience. This article presents a hybrid literature review examining how both effects are addressed about supply chain resilience, focusing on methodological and conceptual trends. Methods: The review combines thematic analysis of studies from Web of Science and ScienceDirect (2000–2023) with bibliometric trend modeling using Long Short-Term Memory neural networks to detect nonlinear patterns and disciplinary dynamics. Results: While 64.7% of the reviewed works explicitly link the Bullwhip Effect or Ripple Effect to resilience, only 11.7% of those focused on the Bullwhip Effect offer models with clear practical use. A structural break in 2019 marks a notable rise in research connecting these effects to resilience. Nonlinear modeling dominates (88.23%) through network theory and system dynamics. Social, Engineering and Business Sciences drive Bullwhip-related studies, while Economics, Computer Science, and Social Sciences lead Ripple-related research. Business, Energy, and Social Sciences strongly influence the integration of the Ripple Effect into supply chains. A modeling typology is proposed, and neural network techniques uncover key bibliometric patterns. Conclusions: The review highlights limited practical application and calls for more adaptive, integrative research approaches.