Optimizing production and its logistics flows is now an essential part of the production strategy of companies, whether when designing a production hall or a goods transshipment point. At the same time, it is also used to make existing solutions more efficient in order to maintain competitiveness. Through effective optimization, it is possible to achieve an increase in the profits of companies, which they can later transform into the purchase of more advanced machines and technologies, which subsequently leads to increased profitability again. This article compares 2 types of optimization algorithms in logistics flow. Experimental Manager and the Genetic Algorithm are used as optimization tools. By correctly setting the input parameters for these methods, valuable optimization outputs can be obtained within a shorter time interval (time savings of app. 40%). The results indicate that the Genetic Algorithm has an advantage over the standard method of the Experimental Manager in the speed of achieving the chosen goals.
Rigó et al. (Thu,) studied this question.