Supply chain optimization in automotive industry: A comparative analysis of evolutionary and swarming heuristics

In every manufacturing, assembly or forwarder systems there are problems determined by a wide range and number of parameters. The increased number of variables and parameters leads to the increased number of required computational time for the exact solution. In this situation, heuristic and metaheuristic algorithms are useful tools to find the optimal or near optimal solutions of the problem. These methods are often combined parallel or sequencial and choosing the best algorithm is a quite complex question. There are two very important factors in computing: computational time and accuracy. In addition, there are secondary aspects, such as robustness or alternative solutions. Within the scope of this paper authors compare one of the best-known algorithms; the genetic algorithm with a relatively new swarming algorithm; the black hole algorithm. The efficiency of both algorithms will be demonstrated with a supply chain optimization problem in automotive industry, where more design tasks of logistic processes will be solved, like location and assignment of resources.