Daniel Morillo-Torres

Evidence from recent large-scale disruptions indicates that efficiency-centered supply chain designs struggle to sustain operation under persistent and systemic uncertainty. This study introduces the Response and Adaptive Immune-Inspired Supply Chain Immune System (RAIE–SCIS), a continuous-time dynamic framework that extends existing viability and resilience approaches by explicitly modeling inter-temporal adaptation and operational memory within a control-theoretic structure. The framework represents supply chains as multi-layer control systems where structural protection, adaptive regulation, and memory mechanisms jointly shape system response over time. Viability is assessed using time-dependent indicators, including performance trajectories, recovery time, and an adaptation-based viability index. The model is applied to a carbon capture, utilization, and storage (CCUS) supply chain under heterogeneous disruption scenarios. Results show that immune-enabled configurations increase minimum performance levels by 15–30% and reduce recovery times by up to 25% compared to non-adaptive configurations. These improvements are not uniform across scenarios and depend on disturbance structure and recurrence. The analysis reveals that adaptive regulation introduces a trade-off between recovery speed and variability, while memory mechanisms shape recovery dynamics under recurrent disruptions—effects not captured by static or purely reactive models. Their effects become more pronounced when disturbances accumulate or propagate.

Purpose: This case study explores the integration of Design Thinking (DT) and Artificial Intelligence (AI) in education to develop students' soft skills while addressing a real and complex sustainability challenge. The “Cooling the Future” educational experience, implemented at Pontificia Universidad Javeriana Cali, in Colombia, aimed to enhance students' problem-solving abilities, teamwork, and creativity through experiential learning focused on optimizing air conditioning (AC) energy consumption on campus by promoting a culture of rational energy use. Design/methodology/approach: Students from 14 undergraduate academic programs, ranging from first to last year, participated in this interdisciplinary initiative. Within this case study, DT and AI were employed as pedagogical approaches to foster critical thinking, interdisciplinary collaboration, and user-centered innovation. By engaging in a structured, hands-on learning process, students analyzed real-world energy consumption issues and proposed innovative solutions. Findings: The results highlight how experiential learning enhances students' adaptability, decision-making, and problem-solving skills. The use of DT combined with generative AI fostered a creative and stimulating, yet challenging, learning framework. The empathy component became the central axis of the solution proposals addressing the high energy consumption associated with AC. Through this initiative, participants gained environmental awareness and social responsibility, and five innovative solutions were proposed, integrating technological, behavioral, and resource management strategies. Originality/value: This case study bridges education and sustainability by demonstrating how DT and AI can be combined to address environmental challenges while fostering essential soft skills. Unlike previous research that explores these methodologies separately, this work provides evidence of their joint impact in higher education. The results emphasize the potential of integrating pedagogical strategies with technological and behavioral interventions to create a sustainable and skill-enhancing educational framework.

The viability of supply chains is a central challenge in environments marked by frequent disruptions, extreme uncertainty, and rising sustainability requirements. While literature has advanced in integrating resilience and sustainability, predominant methods—mainly robust or stochastic optimization—focus on predefined scenarios and offer only a partial view of adaptive capacity. This emphasis on known–unknowns leaves unresolved how to ensure continuity, efficient recovery, and organizational learning under unexpected or unknown–unknown events. A methodological gap therefore persists in evaluating and designing supply chains that not only withstand disruptions but also retain essential goals, autonomously activate responses, and reorganize with acceptable costs and times. This study introduces the Immune-Structural Adaptive Response (RAIE) methodology, inspired by the human immune system. RAIE provides an evaluation framework combining properties such as early detection, minimal redundancy, adaptive memory, and structural reconfiguration, operationalized through dynamic metrics: goal retention, autonomous activation, adaptation cost, recovery time, and service loss. Applied to Carbon Capture, Utilization, and Storage (CCUS) supply chains, RAIE reduced service-loss area (Rₐᵣₑₐ) by 40–65% and recovery time (TTR) by 30–45%, while keeping adaptation costs below 2% of total expenditures. Unlike traditional stochastic or robust models, RAIE explicitly embeds endogenous responses and post-shock reorganization, producing more viable configurations that balance efficiency and resilience. The results deliver actionable guidance for strategic and tactical decision-making in highly uncertain environments.

La enseñanza de la transferencia de calor en ingeniería presenta desafíos significativos debido a la naturaleza abstracta y la complejidad matemática de los modelos empleados. A menudo, los estudiantes encuentran dificultades para relacionar los conceptos teóricos con aplicaciones prácticas, lo que limita su comprensión y retención. Con el objetivo de mejorar la enseñanza de esta disciplina, se implementó una innovación educativa basada en el uso de la cocina como laboratorio de aprendizaje. Esta metodología busca aprovechar situaciones cotidianas para facilitar la apropiación de los principios fundamentales de la transferencia de calor. La iniciativa se llevó a cabo en la Pontificia Universidad Javeriana Cali, en Colombia, con estudiantes de ingeniería de sexto semestre. Se diseñaron actividades en las que los estudiantes analizaron la conducción, convección y radiación térmica a través de procesos de cocción de alimentos. Se seleccionaron geometrías simples como esferas, cilindros y placas para modelar la transferencia de calor en diferentes condiciones. Durante las sesiones, los estudiantes midieron temperaturas en tiempo real utilizando termómetros y cámaras termográficas, calcularon coeficientes de transferencia de calor y compararon los valores obtenidos con predicciones analíticas y numéricas. Las actividades experimentales requerían electrodomésticos básicos, como hornos y estufas, así como ingredientes comunes, lo que permitió una interacción directa con los fenómenos estudiados. También se contó con el acompañamiento de profesionales de la cocina, quienes capacitaron a los estudiantes en el manejo de los implementos de cocina, técnicas de cocción, cortes, entre otros. Los resultados obtenidos evidenciaron un impacto positivo en el proceso de aprendizaje. Se observó un incremento en la motivación y el interés de los estudiantes, quienes manifestaron que la metodología facilitó la comprensión de los conceptos. Además, afirmaron que el vincular estos fenómenos con los experimentos en la cocina realizados por ellos, incrementó la retención a largo plazo. En conclusión, este estudio muestra cómo la utilización de la cocina como entorno de aprendizaje constituye una estrategia eficaz para la enseñanza de la transferencia de calor, permitiendo una conexión entre la teoría y la práctica en un contexto familiar para los estudiantes.

This study presents a systematic literature review on the mathematical modeling of resilient and viable supply chains, grounded in the PRISMA methodology and applied to a curated corpus of 235 peer-reviewed scientific articles published between 2011 and 2025. The search strategy was implemented across four major academic databases (Scopus and Web of Science) using Boolean operators to capture intersections among the core concepts of supply chains, resilience, viability, and advanced optimization techniques. The screening process involved a double manual assessment of titles, abstracts, and full texts, based on inclusion criteria centered on the presence of formal mathematical models, computational approaches, and thematic relevance. As a result of the selection process, six thematic categories were identified, clustering the literature according to their analytical objectives and methodological approaches: viability-oriented modeling, resilient supply chain optimization, agile and digitally enabled supply chains, logistics optimization and network configuration, uncertainty modeling, and immune system-inspired approaches. These categories were validated through a bibliometric analysis and a thematic map that visually represents the density and centrality of core research topics. Descriptive analysis revealed a significant increase in scientific output starting in 2020, driven by post-pandemic concerns and the accelerated digitalization of logistics operations. At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. The results confirm a paradigm shift toward integrative frameworks that combine robustness, adaptability, and Industry 4.0 technologies, as well as a growing interest in biological metaphors applied to resilient system design. Finally, the review identifies research gaps related to the formal integration of viability under disruptive scenarios, the operationalization of immune-inspired models in logistics environments, and the need for hybrid approaches that jointly address resilience, agility, and sustainability.

The Vehicle-Routing Problem (VRP) represents a critical challenge in logistics, encompassing numerous variations, such as time window considerations, multi-depot systems, two-echelon routing aspects, and Satellite Locations (SL). SLs are intermediate facilities that support cross-docking, storage, and transshipment operations. However, inconsistencies in defining “satellite” have hindered precise research and implementation. This study presents a systematic review of the use of satellites for VRP, employing the PRISMA methodology to ensure a comprehensive and reproducible analysis. The findings indicate that about 50% of the reviewed papers include a path-splitting variant. At the same time, there is a notable gap in addressing random demands and pickup and delivery within cross-docking environments. A major limitation is the lack of a well-known public dataset, as about 50% of the datasets are created or adapted for specific studies. Additionally, the analysis reveals significant gaps in dataset standardization and the integration of dynamic routing under uncertainty. These findings underscore the potential of satellite-based systems to optimize urban logistics and supply chains while pointing to critical avenues for future research.