The supply chain has become a key element of increasing the productivity and competitiveness of companies. To achieve this, it is essential to implement a strategy based on the use of technologies, which depends on knowledge of the scope and impact of logistics technologies. Therefore, this article aims to identify the main technologies supporting logistics management and supply chain processes to establish their functionality, scope, and impacts. For this, conventional technologies and technologies framed by the concept of Industry 4.0 that allow the implementation of Logistics 4.0 in companies are analyzed. As a result of searching databases such as Scopus, Web of Science, and Science Direct, we provide an analysis of 18 technologies focusing on their definition, scope, and the logistics processes involved. This study concludes that technologies in logistics management allow for a reduction in total costs, improve collaboration with suppliers and customers, increase the visibility and traceability of products and information, and support decision-making for all agents in the supply chain, including the final consumer.

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Most entrepreneurship studies have an urban focus, and it is studied mainly from the perspective of opportunity exploitation. Rural entrepreneurship presents different characteristics, and it requires analysis from a resource-based view since this kind of entrepreneurial behavior takes place in rural communities under resource constraints. The sustainable livelihood perspective represents a relevant framework in rural entrepreneurship, considering resources and capacities to face poverty in rural areas. Therefore, this study presents a literature review to identify current and emerging issues in rural entrepreneurship from a sustainable livelihood framework. The literature review identifies that the main concepts involved in rural entrepreneurship and sustainable livelihood are women, poverty alleviation, youth, social entrepreneurship, and institutions. Likewise, social capital and human capital prevail as the most relevant capitals in the analyzed documents. The study offers research opportunities in emerging issues related to social entrepreneurship, governance and institutions, livelihood growth, and eco-entrepreneurship for extending the boundaries of rural entrepreneurship from the sustainable livelihood framework.

Autor a quien debe ser dirigida la correspondencia ResumenEste artículo tiene como objetivo desarrollar un algoritmo genético para minimizar la distancia recorrida en almacenes y centros de distribución donde se aplica el problema de conformación de lotes para la preparación de pedidos. Para esto, se propone una nueva representación de soluciones, en la cual cada gen de un cromosoma representa una orden de cliente a recuperar, facilitando la aplicación de operadores de cruzamiento y mutación. A través de experimentos computacionales se establece que el algoritmo genético genera ahorros significativos en distancia recorrida y número de lotes respecto a una regla básica de conformación de lotes, especialmente en escenarios donde se exige conformar un mayor número de lotes. Se concluye que el algoritmo genético brinda soluciones eficientes en un tiempo computacional razonable, por lo cual se recomienda su implementación en ambientes operativos de almacenes y centros de distribución. Palabras clave: preparación de pedidos; conformación de lotes; algoritmos genéticos; gestión de almacenes; metaheurísticos AbstractThis article aims to develop a genetic algorithm to minimize the distance traveled in warehouses and distribution centers where the order-batching problem applies for order picking systems. For this, a new representation of solutions is proposed, in which each gene of a chromosome represents a customer order to be retrieved, easing the application of crossover and mutation operators. Through computational experiments, it is shown that the genetic algorithm generates significant savings in distance traveled and number of batches compared to a basic rule of order batch formation, especially in scenarios where a greater number of batches is required. We conclude that the genetic algorithm provides efficient solutions in a reasonable computational time, thus its implementation is highly recommended in operative environments of warehouses and distribution centers.

This article aims to propose and implement an aggregated production planning model to provide optimal strategies in the medium term for a textile company, for which a linear programming model is proposed to minimise total costs associated with labour and inventory levels. The model proposed takes into account characteristics associated with fabric contraction, wastes in the process, the efficiency of new employees, and training requirements. The model is implemented and solved in GAMS, supported on an MSExcel interface, to find the optimal solution, which is to apply a hybrid strategy to the production plan, and also some strategies for improving the production process are generated.

Esta investigación tiene como objetivo desarrollar un modelo de planificación de producción agregada para generar estrategias de producción óptimas en el mediano plazo para empresas del sector textil. Para esto se desarrolla un modelo de planificación de producción agregada denominado PLAG, que minimiza los costos de mano de obra, costos de gestión de inventarios, y costos de subcontratación de producción. A diferencia de otros modelos de la literatura, el modelo PLAG tiene en cuenta características del sector textil relacionadas con la contracción de tela, pérdidas por manipulación del producto en el proceso, eficiencia de empleados nuevos, tiempos de entrenamiento y capacitación, y subcontratación de procesos de manufactura, lo cual hace que sea un modelo completo y eficaz para las empresas del sector textil. El modelo propuesto se programa y ejecuta en GAMS, apoyándose de una interface en MSExcel, con lo cual se generan estrategias de capacidad de producción para el mejoramiento del proceso productivo y la optimización del plan de producción.Palabras clave: Optimización, costos de producción, planificación agregada, textiles.

ResumenSe desarrolla y valida un metaheurístico para la conformación de lotes de acomodo del mínimo tiempo posible considerando k equipos de manejo de materiales homogéneos. El método debe servir también para la ubicación del depósito (depot, punto de inicio y finalización de la preparación de pedidos), y para determinar el tamaño de la lista de productos a acomodar, entre otros factores. La validación experimental fue realizada usando diseño estadístico de parcelas dividas. Como resultado de la experimentación se obtuvo que el metaheurístico denominado Búsqueda Inteligente en la Vecindad (INS) produjo ahorros de tiempo entre 24 y 50 min/lote respecto a la regla "el Primero que llega es el primero en ser servido (FCFS). Se muestra que el metaheurístico propuesto no solo contribuye al avance en el tema logístico sino también a la eficiencia operacional de la gestión de almacenes. Palabras clave: acomodo, almacén, conformación de lotes, logística, metaheurístico. Minimum Time Order Batching in the Put away Operation Considering k Homogeneous Equipment using metaheuristics AbstractThis paper presents the development and validation of a metaheuristic for minimum time order batching in put away operations considering k material handling equipment. The method also serves to locate the depot and to determine the put away list size, among other factors. Experimental validation was performed by a statistical design named split plot. As a result of the experiment it was obtained that metaheuristic INS (Intelligent Neighborhood Search) generated time savings between 24 and 50 minutes per batch compared with the classical rule FCFS (First Come, First Served). It is demonstrated that the proposed metaheuristic not only contributes to logistic but also to the operational efficiency of warehouse management.

Put-away operations typically consist of moving products from depots to allocated storage locations using either operators or Material Handling Equipment (MHE), accounting for important operative costs in warehouses and impacting operations efficiency. Therefore, this paper aims to formulate and solve a Put-away Routing Problem (PRP) in distribution centres (DCs). This PRP formulation represents a novel approach due to the consideration of a fleet of homogeneous Material Handling Equipment (MHE), heterogeneous products linked to a put-away list size, depot location and multi-parallel aisles in a distribution centre. It should be noted that the slotting problem, rather than the PRP, has usually been studied in the literature, whereas the PRP is addressed in this paper. The PRP is solved using a discrete particle swarm optimization (PSO) algorithm that is compared to tabu search approaches (Classical Tabu Search (CTS), Tabu Search (TS) 2-Opt) and an empirical rule. As a result, it was found that a discrete PSO generates the best solutions, as the time savings range from 2 to 13% relative to CTS and TS 2-Opt for different combinations of factor levels evaluated in the experimentation.