CENDES COLOMBIA is a centre that seeks the integral human development for forced displaced population caused by civil conflict in the country. This centre is meant as a Learning Community where cooperative, participatory and trustworthy relationships arise. This paper efforts have been put up to showcase the analysis of the social networks among its beneficiaries, and its incidence in the intervention process in order to identify possible areas for improvement to strengthen the effectiveness of the emancipation process. The methodology involves document reviews, interviews, and the use of the UCInet software for the social network analysis or SNA formulation. Key features of the CENDES beneficiaries, such as connection strength, degree of intermediation, degree of separation, and degree of centrality are discussed, showing as results that people associated to this process is connected with a low density in the entire network created, but with a greater tendency to clustering, reflecting teamwork. Four of the thirty five people in the network show leadership characteristics, indicating that these individuals have a high degree of empowerment, particularly in “power for” to exercise authority “deserved” for their high sense of cooperation. Overall, the average degree of centrality of the network shows that tends to exist homogeneity in the position of all nodes in the network, presenting no marked hierarchies and facilitating communication processes; it is likely that taking out some node of the network, the network flow is affected. These results can complement processes in the development centre based on the roles and importance of key people within a process

The production and supply chain management of biofuels from organic waste as raw material has been identified as a promising strategy in the field of renewable energies and circular economy initiatives. This industry involves complex tasks such as strategic land use, feedstock purchasing, production plant location, production capacity strategy, and material flows, which can be solved by mathematical modeling. The study proposed a multi-objective mixed-integer linear programming model to design a sustainable supply chain of biofuels with forest residues from its triple function: economic, environmental, and social. The trade-offs between the proposed objectives were determined with computational results. The proposed objectives were profit maximization, CO₂ minimization, and employment generation maximization. Thus, the proposed model serves as a tool for decision-making, allowing the projection of a long-term structure of the biofuel supply chains and contribute to the United Nations Sustainable Development Goals.

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.

An important problem in rural-area supply chains is how to transport the harvested fruit to urban areas. Low- and medium-capacity vehicles are used in Colombia to carry out this activity. Operating them comes with an inherent cost and generates carbon emissions. Normally, minimizing operating costs and minimizing carbon emissions are conflicting objectives to allocate such vehicles efficiently in any of the supply chain echelons. We designed a multi-objective mixed-integer programming model to address this problem and solved it via the ε-constraint method. It includes decisions mainly about quantities of fruit to transport and store, types of vehicles to allocate according to their capacities, CO₂ emission levels of these vehicles, and subcontracting on the collection process. The main results show two schedules for allocating the vehicles, showing minimum and maximum CO₂ emissions. Minimum CO₂ emissions scheme require subcontracting and the maximum CO₂ scheme does not. Then, a Pareto frontier shows that CO₂ emissions level are inversely proportional to total management cost for different scenarios in which fruit supply was modified.

The problems of flexible planning of the design of logistics systems for the collection of food products such as raw milk can result in a decrease in the performance of critical indicators for their performance. This paper proposes a new efficient methodology for robustly designing a first-mile logistics system for storing and refrigerating milk as a perishable product considering decisions related to open facilities and the flow of products, including sustainability indices. The proposed approach is modeled as a bi-objective problem by considering the minimization of greenhouse gas emissions (CO2) produced by milk transportation canteens and the maximization of the system configuration's net present value (NPV). We have analyzed and determined the most robust configuration for the first time and explained the robustness-NPV and robustness-CO2 relationships. The proposed mathematical model is solved by the Epsilon constraints method, and the robustness is calculated considering an extension of the FePIA methodology for multiobjective problems. A novel contribution is a balance in the possible future values generated by the company related to its cash flows and the generation of CO2 emissions when using a motorized transport frequently used in the shipment of raw milk considering a new important aspect such as the volume of product transported and the slope of the path between the production farm and the storage cooling tanks.

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.