Affiliation:
1. Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK
Abstract
Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach.
Reference55 articles.
1. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews;Page;BMJ,2021
2. An overview of industry 4.0: Definition, components, and government initiatives;Tay;J. Adv. Res. Dyn. Control. Syst.,2018
3. Industry 4.0: A Special Section in IEEE Access;Su;IEEE Access,2017
4. Industry 4.0: Coherent definition framework with technological and organizational interdependencies;Nosalska;J. Manuf. Technol. Manag.,2020
5. A beam search approach to the container loading problem;Araya;Comput. Oper. Res.,2014