Assigning Metaheuristics to a Logistics Problem: A Novel Classification System for Algorithms and Problems

Metaheuristic algorithms applied to NP-hard problems are proven effective techniques in the field of optimization to ensure a good result within an acceptable calculation time. However, finding a suitable technique for optimizing a complex problem is not an easy task since there are hundreds of methods. The majority of metaheuristic research is characterized by developing a new algorithm for a task or modifying or improving an existing technique. The reuse of metaheuristics is small compared to the fact that up to 30+ new procedures can be presented in the scientific community every year. Metaheuristic algorithms have already been grouped in countless ways, but not based on their components and structural elements, which are responsible for the basic optimization performance of the given method. The grouping of the problems to be solved is also not typical in terms of which method can be used to solve them effectively. This paper contributes to filling this gap by introducing a novel classification system for algorithms and problems in terms of variables or based on the type of task. Two main categories were distinguished for both logistic tasks and metaheuristic algorithms: discrete and continuous. Linking the nature of the variables between the task and the algorithm is a significant step forward in choosing an efficient metaheuristic with a high probability for a logistics problem. This can increase the efficiency of solving logistics problems and expand the use of the latest optimization techniques.