Solving Container Loading Problem with Differential Evolution Algorithm - (:unkn) unknown (Institute of Electrical and Electronics Engineers Inc., 2020)
With supply chain today developing an international dimension, it is becoming increasingly important to reduce the costs of container shipping as well as other transportation costs associated with it. It is barely of any doubt that one of the potential ways of saving on these costs is to come up with acceptable solutions to the problems of container loading and thus make better use of container volumes. For this reason, NP hard container loading problems have attracted the attention of many a researcher. This problem can briefly be defined as placing the products, packaged in a range of boxes having different sizes, in the containers in accordance with a specific optimization criterion [1]. In this study, a suggestion of solution is offered for the container loading problem by the use of a method of known as Differential Evolution Algorithm. Differential Evolution Algorithm (DE algorithm) is one of the population-based intuitive optimization techniques, which yield positive results with the problems especially when continuous data is at stake, which is based on genetic algorithm in respect to their working procedure. In this study, the performance of algorithm has also been tested for the standard 2D container loading problem given in the literature and the results are discussed comparatively with reference to the previous works in the literature. © 2020 IEEE.
Keywords: artificial intelligence; container loading; differential evolution; optimization; soft programming