Efficient algorithms are needed for optimization of objects and systems, because the user would like to be sure that the optimum is global. The paper shows a very well scalable discrete firefly algorithm, developed for solving a supplier selection problem. The built in general reduced gradient and evolutionary algorithms of the Excel solver are also compared solving this problem. The results show that the firefly algorithm solves the problem in the fragment of the running time of the evolutionary algorithm. In the second part of the article, a mathematical model was formulated to solve the fixed destination multidepot multiple tour multiple traveling salesmen problem (mdmMTSP).
In the research projects and industrial projects severe optimization problems can be met, where the number of variables is high, there are a lot of constraints, and they are highly nonlinear and mostly discrete issues, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In most cases in the logistics industry, the most robust constraint is the time. The optimizations are running on a typical office configuration, and the company accepts the suboptimal solution what the optimization method gives within the appropriate time limit. That is, why adaptivity is needed. The adaptivity of the optimization technique includes parameters of fine-tuning. On this way, the most sensitive setting can be found. In this article, some additional adaptive methods for logistic problems have been investigated to increase the effectivity, improve the solution in a strict time condition.