## 7 Final Thoughts and Further Research  
This paper has presented a mathematical model for food hub locations in the central region of Colombia. The model considers the special characteristics of the supply and distribution of the fresh food supply chain in the region, namely dispersed farmers in the countryside that make choices as to where they sell their products and food hubs that distribute such products to urban centers. To the best of our knowledge, this problem has not been studied in the literature. This problem is of special relevance because public institutions in Colombia, responsible for the design and implementation of the master plans for food supply and food security, are closely following the results of this model. This research uses extensions and adaptations of several competitive hub and facility location formulations to create a realistic model that can be used as a first input for policy making. The results show that the model can be viable and sustainable in the long term under several conditions, as described throughout the paper. This proposed model, of realistic size, was tested with real data. Extensive experiments suggest that the ability of the food concessionaires to attract farmers is the main success factor for the business model. Based on the results presented throughout this paper, managers and decision makers of the central region can devise an articulated set of tax and legal incentives to attract private investment for the creation and/or strengthening of logistics strategies with technical and operational capacities to efficiently deal with the operation of the hubs. At the same time, education and sensibilization campaigns must be carried out with all stakeholders in order to guarantee a smooth operation of the hub network. Future research will be carried out with extensions such as logistic regression models that capture additional decision factors such as prices and social network, to name a few. Multi-objective extensions considering environmental factors are also foreseen. Conceptualization, G.M., D.G.-R., and J.A.J. ; methodology, G.M. and D.G.-R.; software, D.G.-R., N.M., E.M., and A.C.; validation, J.A.J., G.M., and D.G.-R.; formal analysis, G.M. and D.G.-R.; investigation, G.M., D.G.-R., N.M., E.M., and A.C.; resources, G.M. ; data curation, D.G.-R., N.M., E.M., and A.C.; writing--original draft preparation, G.M., J.A.J., and D.G.-R.; writing--review and editing, All; visualization, D.G.-R.; supervision, G.M. ; project administration, G.M. ; funding acquisition, G.M. and J.A.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Direccion de Investigacion de la Universidad de la Sabana, grant number ING-210-2018. The APC was funded by the same grant. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing is not applicable to this article. **Acknowledgments:** Special thanks to the officers of RAP-E-. [PERSON], [PERSON] and [PERSON], senior officers of RAP-E- and [PERSON], senior consultant of RAP-E-, who provided valuable information and supervised the conceptualization of the mathematical model. The authors also want to thank the Universidad de La Sabana for providing the funds and means to undertake this research. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Appendix A**  
This appendix shows the products that belong to each of the analyzed agricultural chains. * [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON] Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with an illustrative example. _Ann. Oper. Res._**2006**, _147_, 109-141. [CrossRef]
* U.S. Department of Agriculture (2017) U.S. Department of Agriculture.