Machine Learning for industrial applications: A comprehensive literature review

Bertolini, Massimo; Mezzogori, Davide; Neroni, Mattia; Zammori, Francesco

doi:10.1016/j.eswa.2021.114820

Cited by 260 publications

(115 citation statements)

References 160 publications

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“…An interesting direction for future research is the extension of this approach to support the integrated assembly line balancing and feeding problems (Sternatz 2015;Schmid and Limère 2019). Finally, since ML is often considered as one of the main enablers for the evolution of a traditional manufacturing system into a 4.0 system (Culot et al 2020;Bertolini et al 2021), this work is connected to the research on Industry 4.0, which aims at improving the efficiency and flexibility of production processes thanks to the collection, sharing, and analysis of data (Ghobakhloo 2018;Garay-Rondero et al 2019;Oztemel and Gursev 2020). However, this study does not explore the interplay between ML and other Industry 4.0 enabling technologies (Culot et al 2020).…”

Section: Limitations and Future Researchmentioning

confidence: 99%

“…So far, combined optimization-ML approaches have been applied in the fields of energy systems (Fischetti and Fraccaro 2019) and transportation management (Larsen et al 2018;Abbasi et al 2020), but they raise the attention towards the opportunity to support data-driven decision making in many more fields. As regards production systems, the available literature includes several contributions in which ML techniques are applied (Kang et al 2020;Bertolini et al 2021), but never in combination with optimization. However, a number of issues should be better tackled in order to foster further research on combined optimization-ML approaches.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the application of machine learning to the assembly line feeding problem

2021

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As a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

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Section: Limitations and Future Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the application of machine learning to the assembly line feeding problem

2021

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“…Despite a large number of publications in the field of ML and SCM separately, the number of publications that have paid to the applications of ML algorithms in managing a supply chain is not adequate [38]. On the other hand, there is no enough connection between researchers and practitioners in this field.…”

Section: Machine Learning In Supply Chain Managementmentioning

confidence: 99%

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

Tirkolaee

Sadeghi

Mooseloo

et al. 2021

Mathematical Problems in Engineering

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In today’s complex and ever-changing world, concerns about the lack of enough data have been replaced by concerns about too much data for supply chain management (SCM). The volume of data generated from all parts of the supply chain has changed the nature of SCM analysis. By increasing the volume of data, the efficiency and effectiveness of the traditional methods have decreased. Limitations of these methods in analyzing and interpreting a large amount of data have led scholars to generate some methods that have high capability to analyze and interpret big data. Therefore, the main purpose of this paper is to identify the applications of machine learning (ML) in SCM as one of the most well-known artificial intelligence (AI) techniques. By developing a conceptual framework, this paper identifies the contributions of ML techniques in selecting and segmenting suppliers, predicting supply chain risks, and estimating demand and sales, production, inventory management, transportation and distribution, sustainable development (SD), and circular economy (CE). Finally, the implications of the study on the main limitations and challenges are discussed, and then managerial insights and future research directions are given.

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“…Fortunately, the emergence, development, and application of computational thinking [17][18][19][20] and deep learning [21][22][23][24] provide the new theory, approach, and solution to deal with the operational scheduling and decision support of CTHS at all strategic, tactical, and executive levels. In particular, the recurrent neural network (RNN) and convolutional neural network (CNN) have been widely used in various scenarios of deep learning, such as image recognition [25], natural language processing [26], biomedical processing [27], stock market [28], machine health monitoring [29], intersectional traffic [30], and so on.…”

Section: Introductionmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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Machine Learning for industrial applications: A comprehensive literature review

Cited by 260 publications

References 160 publications

Exploring the application of machine learning to the assembly line feeding problem

Exploring the application of machine learning to the assembly line feeding problem

Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

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