# Metaheuristics in the Humanitarian Supply Chain
## 6 Main Findings and Conclusions  
In a world where there are more natural and man-made disasters, and massive diseases that require the attention of HSC, it is necessary to consider what types of problems are being addressed, their classification and the types of metaheuristics used to solve them. In this work, 80 articles were selected considering the systematic literature review methodology, aiming to answer two research questions: RQ1 How are the HSC problems that have been solved from metaheuristics since 2015 classified? and RQ2 What is the gap found to accomplish future research in metaheuristics in HSC? After reviewing all the selected articles, it was found that the deterministic and non-deterministic problems are well balanced with 52.56% of the deterministic in contrast to 47.44% of the non-deterministic. The highest number of addressed problems were regarding distribution with 71.79%, followed by facility location with 41.03%, it should be noted that there are research papers that consider both problems together. In contrast, the least resolved problems are inventory with 11.54% and Mass Evacuation with 10.26%. In the analysis, it is also concluded that the post-disaster phase is the most considered one with 51.28% followed by the one that integrates both phases, pre- and post-disaster with 34.62%, and the pre-disaster phase with 14.10% (RQ1). In this regard, there are areas of opportunity to address the problem of inventory and mass evacuation considering the pre-disaster or both integrated phases. Consequently, the most convenient thing is considering problems in a non-deterministic way to deal with the uncertainty that exists in the current context (RQ2). In the case of single or multiple objectives, they are balanced, with 53.85% being multiple objectives, while 46.15% of the problems are single. Regarding the period, single-period problems are further solved, with 76.92% against 23.07% of multiple-period problems (RQ1). This may be due to the complexity that multi-period problems represent, however, solving problems with multiple periods would allow that the proposed models are closer to reality (RQ2). As to MHs, it was found that 71.79% were based on metaphor; of these 23.07% of the total articles are evolutionary, the most common being Genetic Algorithms. In the Swarm Intelligence area, there is 30.76% of the total work, with Particle Swarm Optimization being the one that is the most extensively occupied. Based on physics there are 15.38% of articles with Simulated Annealing. From those not based on metaphors, there are a total of 28.20%, the most widely used being the search algorithm with 15.38%. Of the MHS that solve multiple objectives, there are 38.46% of the articles in which the NSGA-II predominates with 15 solved papers. Using hybrid algorithms there is 15.38% (RQ2). After analyzing the articles, what they have in common is the modeling of the objective function and some solution methods; in contrast, they adapt the restrictions to the type of problem to be solved. When reviewing deeply, some articles that have been written by the same author have adapted his original proposal to solve other more extensive problems or with other products and movements in HSC. The future research could be in two approaches; (1) Test the new MHs that are mentioned in Section 3 in already structured problems concerning the four main problems--facility location, distribution, inventory, mass evacuation--and do serious statistical analysis to see which one converges faster to the solution. When authors are applying new MHs to solve problems related to HSC, researchers should statistically compare different MHs to decide which one is the best for the problem that they are trying to solve. Some works present the comparison of different solution methods through statistic techniques, for example, Page trend test for convergence analysis [122] or [PERSON] et al. [123] who survey current trends in statistical analysis proposals for the comparison of computational intelligence algorithms.