A Novel Location-Inventory-Routing Problem in a Two-Stage Red Meat Supply Chain with Logistic Decisions: Evidence from an Emerging

This study focuses on a specific method of meat production that involves carcass purchase and meat production by packing facilities with a novel two-stage model that simultaneously considers location-routing and inventory-production operating decisions. The considered problem aims to reduce variable and fixed transportation and production costs, inventory holding cost and the cost of opening cold storage facilities. The proposed model encompasses a two-stage model consisting of a single-echelon and a three-echelon many-to-many network with deterministic demand. The proposed model is a mixed-integer linear programming (MILP) model which was tested with the general algebraic modelling system (GAMS) software for a real-world case study in Iran. A sensitivity analysis was performed to examine the effect of retailers' holding capacity and supply capacity at carcass suppliers. In this research, the number of products transferred at each level, the number of products held, the quantity of red meat produced, the required cold storage facilities and the required vehicles were optimally specified. The outcomes indicated a two percent (2%) decrease in cost per kg of red meat. Eventually, the outcomes of the first and second sensitivity analysis indicated that reduced retailers' holding capacity and supply capacity at carcass suppliers leads to higher total costs. This research proposes a novel multi-period location-inventory-routing problem for the red meat supply chain in an emerging economy with a heterogeneous vehicle fleet and logistics decisions. The proposed model is presented in two stages and four-echelon including carcass suppliers, packing facilities, cold storage facilities and retailers.N/

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.The agricultural sector is the fundamental axis that moves the world economy, it allows the generation of agricultural and livestock products to supply small and large cities. In underdeveloped countries, the participation of industry and academia is necessary to strengthen production systems, this based on the injection of technology, as well as the transfer and appropriation of knowledge in the sector. An approach used to strengthen the sector is the study of agricultural supply chains (agro-chains) based on mathematical modeling, that allows data processing and facilitates strategic, tactical or operational decision-making. We conducted a review of the literature on the application of mathematical models in the study of agricultural chains during the last 20 years. The study concludes that there is a fairly great interest by the academic-scientific community to strengthen the agricultural sector in different countries such as the United States, Brazil, India and the Netherlands, among others. Stochastic simulation models are used in 36% of the consulted works, allowing complex problems involving uncertainty in data behavior to be addressed. Also, in 70% of the works consulted, heuristic models are used to solve design and distribution problems in agro-chains, and the remaining 30% require the use of metaheuristics because they require solving problems with multiple responses given the complexity of the data. Mathematical modeling has become a very useful tool for solving latent problems in agro-chains, it facilitates data processing and complex decision-making, mainly during chain design, product supply and control of costs, delivery times and environmental impacts, among other important variables

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers