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Prof. Dr. Massimo Bertolini
“Enzo Ferrari” Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy

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Research Keywords & Expertise

0 Lean Manufacturing
0 Project Management
0 Production planning and control in Make-To-Order and Engineer-To-Order manufacturing environments
0 Auto-Id technologies for Logistic and supply chain management
0 Artificial Intelligence for Logistics and supply chain management

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Review article
Published: 04 March 2021 in Expert Systems with Applications
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Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.

ACS Style

Massimo Bertolini; Davide Mezzogori; Mattia Neroni; Francesco Zammori. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications 2021, 175, 114820 .

AMA Style

Massimo Bertolini, Davide Mezzogori, Mattia Neroni, Francesco Zammori. Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications. 2021; 175 ():114820.

Chicago/Turabian Style

Massimo Bertolini; Davide Mezzogori; Mattia Neroni; Francesco Zammori. 2021. "Machine Learning for industrial applications: A comprehensive literature review." Expert Systems with Applications 175, no. : 114820.

Journal article
Published: 07 June 2020 in Expert Systems with Applications
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When manufacturing a product, companies must consider the specifications of its design and choose the manufacturing technology that matches them the best in terms of product quality, production time and costs. Since all these parameters can be represented by several different and conflicting indicators, the problem of technology selection can be defined as a multi-criteria decision-making (MCDM) problem. Although several mathematical models have been developed to solve similar problems, recent literature still presents a lack of specific applications of renowned decision-making techniques to the technology matching problem in the manufacturing sector. This study attempts to fill this gap by proposing a manufacturing-oriented model of the Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS), one of the most solid and robust MCDM methods. The solution we present, which is designed for general manufacturing processes, has been applied to the specific case of a producer of food and beverage plants and equipment that is interested in reengineering one of its products. Due to the complexity of the food and beverage industry, the case study is useful for supporting the definition of the general model and validating its applicability. Further, the results of the specific application prove the effectiveness of our model.

ACS Style

Massimo Bertolini; Giovanni Esposito; Giovanni Romagnoli. A TOPSIS-based approach for the best match between manufacturing technologies and product specifications. Expert Systems with Applications 2020, 159, 113610 .

AMA Style

Massimo Bertolini, Giovanni Esposito, Giovanni Romagnoli. A TOPSIS-based approach for the best match between manufacturing technologies and product specifications. Expert Systems with Applications. 2020; 159 ():113610.

Chicago/Turabian Style

Massimo Bertolini; Giovanni Esposito; Giovanni Romagnoli. 2020. "A TOPSIS-based approach for the best match between manufacturing technologies and product specifications." Expert Systems with Applications 159, no. : 113610.

Journal article
Published: 29 March 2020 in Computers & Industrial Engineering
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The present paper is aimed at showing, through a simulative approach, that the adoption of a suitable dispatching rule allows to improve the single-loop CONstant Work In Process (CONWIP) control mechanism within Make To Order (MTO) production systems, balancing the workload among the workstations, reaching a performance level that outperforms standard CONWIP and leans towards that of the corresponding m-CONWIP system. The benefits that may derive from the adoption of a single-loop CONWIP for the design/management of production systems are obvious, being true that (i) m-CONWIP systems are complex to design and optimize, (ii) the single-loop CONWIP systems can be designed with simpler approaches and that (iii) single-loop CONWIP systems remain easier to manage than the m-CONWIP systems. Thus, the well-known Work In Next Queue (WINQ) rule has been adjusted and used within a single-loop CONWIP model, to guarantee that items are favored within those routings for which higher capacity is available in the succeeding work centers. Its performance has been then compared respectively to that of the standard, FIFO-based, CONWIP system and that of an extremely efficient m-CONWIP system. It is known that the workload balancing capability of pull systems not only depends on the configuration of the system itself. It is also subjected to the variability in the order arrival pattern and of the processing times. These parameters have been therefore opportunely considered. Also, the number of available cards represents, along with the loading rule, the most important control parameter. It can be easily determined both in the standard and the WINQ-based CONWIP, whereas it represents a significant issue within the m-CONWIP systems. Thus, to optimize the number of cards within the m-CONWIP model a Genetic Algorithm (GA) has been opportunely configured.

ACS Style

M. Bertolini; M. Braglia; M. Frosolini; L. Marrazzini. Work In Next Queue CONWIP. Computers & Industrial Engineering 2020, 143, 106437 .

AMA Style

M. Bertolini, M. Braglia, M. Frosolini, L. Marrazzini. Work In Next Queue CONWIP. Computers & Industrial Engineering. 2020; 143 ():106437.

Chicago/Turabian Style

M. Bertolini; M. Braglia; M. Frosolini; L. Marrazzini. 2020. "Work In Next Queue CONWIP." Computers & Industrial Engineering 143, no. : 106437.