Integrated warehouse assignment and carton configuration optimization using deep clustering-based evolutionary algorithms

Das, Jyotirmoy Nirupam; Tiwari, Manoj Kumar; Sinha, Ashesh Kumar; Khanzode, Vivek

doi:10.1016/j.eswa.2022.118680

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…Cluster analysis is also of great importance in wider research. For example, when examining the connection of the applications and technologies in the context of Industry 4.0. it was used by the authors in [13][14][15], which is interesting in terms of potential further research in the subject area.…”

Section: Introductionmentioning

confidence: 99%

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

Kronova,

Izarikova,

Trebuna

et al. 2024

Applied Sciences

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The article deals with the application of cluster analysis in modeling in-house processes, specifically supply processes. An algorithm is designed on the theoretical basis of cluster analysis and according to the analysis of the supply processes in selected industrial companies. Specifically, the algorithm is based on the hierarchical methods of cluster analysis, and the selected hierarchical clustering method is applicable in modeling storage systems under various production conditions in industrial companies. The methodology of clustering regarding the supply processes is subsequently experimentally verified. Based on the results of the cluster analysis, a system of organization was proposed for the analyzed warehouse in the form of 2D and 3D layout models of the warehouse.

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Section: Introductionmentioning

confidence: 99%

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

Kronova,

Izarikova,

Trebuna

et al. 2024

Applied Sciences

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“…, 2011; Rajput and Singh, 2020). Greener operational practices, as well as packaging consumption monitoring and waste reduction, have also increased and several related solutions have become common (Das et al ., 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, digital technologies and energy-efficient systems have been progressively widespread, such as LED lighting and light sensors, lithium-ion batteries for material handling equipment and fast chargers (Rai et al, 2011;Rajput and Singh, 2020). Greener operational practices, as well as packaging consumption monitoring and waste reduction, have also increased and several related solutions have become common (Das et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Greening warehouses through energy efficiency and environmental impact reduction: a conceptual framework based on a systematic literature review

Perotti,

Colicchia

2023

IJLM

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PurposeThe purpose of this paper is to propose a framework of green strategies as a combination of energy-efficiency measures and solutions towards environmental impact reduction for improving environmental sustainability at logistics sites. Such measures are examined by discussing the related impacts, motivations and barriers that could influence the measures' adoption. Starting from the framework, directions for future research in this field are outlined.Design/methodology/approachThe proposed framework was developed starting from a systematic literature review (SLR) approach on 60 papers published from 2008 to 2022 in international peer-reviewed journals or conference proceedings.FindingsThe framework identifies six main areas of intervention (“green strategies”) towards green warehousing, namely Building, Utilities, Lighting, Material Handling and Automation, Materials and Operational Practices. For each strategy, specific energy-efficiency measures and solutions towards environmental impact reduction are further pinpointed. In most cases, “green-gold” measures emerge as the most appealing, entailing environmental and economic benefits at the same time. Finally, for each measure the relationship with the measures' primary impacts is discussed.Originality/valueFrom an academic viewpoint, the framework fills a major gap in the scientific literature since, for the first time, this study elaborates the concept of green warehousing as a result of energy-efficiency measures and solutions towards environmental impact reduction. A classification of the main areas of intervention (“green strategies”) is proposed by adopting a holistic approach. From a managerial perspective, the paper addresses a compelling need of practitioners – e.g. logistics service providers (LSPs), manufacturers and retailers – for practices and solutions towards greener warehousing processes to increase energy efficiency and decrease the environmental impact of the practitioners' logistics facilities. In this sense, the proposed framework can provide valuable support for logistics managers that are about to approach the challenge of turning the managers' warehouses into greener nodes of the managers' supply chains.

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“…For instance, clustering can be used to group manufactured inventory parts according to different features [134] (Know-what). The obtained clusters can be used as a guideline for warehouse space optimization [135]. Clustering can be used for anomaly detection [136] (Know-what) and process optimization [137] (Know-how), and can be used in conjunction with other techniques to improve the interpretability of results.…”

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confidence: 99%

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

et al. 2023

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While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

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Integrated warehouse assignment and carton configuration optimization using deep clustering-based evolutionary algorithms

Cited by 13 publications

References 36 publications

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

Greening warehouses through energy efficiency and environmental impact reduction: a conceptual framework based on a systematic literature review

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

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