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PackageCargo: A decision support tool for the container loading problem with stability
Author(s) -
Juan Martínez-Franco,
Edgar Céspedes-Sabogal,
David ÁlvarezMartínez
Publication year - 2020
Publication title -
softwarex
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 21
ISSN - 2352-7110
DOI - 10.1016/j.softx.2020.100601
Subject(s) - container (type theory) , computer science , usable , stability (learning theory) , modular design , metaheuristic , software , set (abstract data type) , decision support system , data mining , machine learning , algorithm , operating system , programming language , engineering , mechanical engineering , world wide web
This article presents PackageCargo. A modular open-source application developed using the Unity game engine to calculate, visualize, and save efficient packing patterns to instances of the Container Loading Problem (CLP). The packing patterns are obtained through approximate optimization algorithms (metaheuristics). Additionally, the proposed tool allows us to estimate cargo stability metrics through the implementation of mathematical models and verify the results of said models using a simulation environment built with the PhysX library. The goal of this application was to create a usable decision support system suitable for industrial purposes as well as a platform for academic research. It is offering a modifiable framework that can adapt to the necessities of its users, saving them software development time while continuing to extend PackageCargo through community contributions. The resulting application was compared with commercial software solutions. Furthermore, each module was tested using the most successful approaches found in literature as benchmarks. The packing module was compared against the top-performing algorithm published to date, obtaining similar results in similar computational times. The simulation module for cargo stability was benchmarked against high-performance simulation software, validating its accuracy and performance. Accordingly, PackageCargo was found to have a competitive feature set useful in both academic and commercial settings. As future work, it is proposed to combine the different modules to solve more sophisticated variants of the CLP, like the container loading problem constrained to weight distribution profiles.

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