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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 an d importance of key people within a process.

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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, CO 2 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 CO 2 emissions. Minimum CO 2 emissions scheme require subcontracting and the maximum CO 2 scheme does not. Then, a Pareto frontier shows that CO 2 emissions level are inversely proportional to total management cost for different scenarios in which fruit supply was modified.