Scheduling Under Uncertainty for Industry 4.0 and 5.0

Bakon, Krisztián; Holczinger, Tibor; Süle, Zoltán; Jaskó, Szilárd; Abonyi, János

doi:10.1109/access.2022.3191426

Cited by 16 publications

(10 citation statements)

References 349 publications

(192 reference statements)

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“…Digitalization can start in any company, at any time point in the company's value chain [4]. Industry 4.0 technology enables the development of new production strategies, especially via cyber-physical systems that require highly customized solutions [5]. The increasing levels of digitization impose entirely new requirements on employees within organizations and completely reshape corporate strategies.…”

Section: The Impact Of Digitalization On Companiesmentioning

confidence: 99%

“…(4) Data transfer, storage, and analysis technologies capable of managing data and system interoperability. (5) Artificial intelligence, such as recognizing cause-and-effect relationships in complex, dynamic systems, leading to actionable intelligence. ( 6) Technologies for energy efficiency, renewable energy sources, storage, and autonomy.…”

Section: Directions Of Industry 50mentioning

confidence: 99%

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Examining Digital Transformation Trends in Austrian and Hungarian Companies

2023

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The contemporary discourse focuses on the Fourth Industrial Revolution and its impact on companies. The research is based on a quantitative questionnaire survey of 101 business leaders in Hungary and on 54 in Austria. It was considered important to analyze these two neighboring countries, which have similar histories and cultures but different industrial and economic advantages in terms of labor costs/skills and technology. The aim of our study is to assess how ready companies are for Industry 4.0 and what key factors they take into account when implementing Industry 4.0 initiatives. To complement our quantitative analysis, we conducted four semi-structured interviews with two Hungarian and two Austrian CEOs. Based on both the quantitative and qualitative survey, we conclude that there are relevant differences in digital maturity between the two countries at both macro and micro levels, with Hungarian companies lagging behind Austrian companies in the adoption of Industry 4.0 technologies. Hungarian managers were most concerned with achieving higher profits from the adoption of an Industry 4.0 strategy, while Austrian companies were most concerned with improving their market position. Our analysis also revealed the main areas where Hungarian and Austrian companies show similar performance and improvements in line with Industry 4.0.

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Section: The Impact Of Digitalization On Companiesmentioning

confidence: 99%

Section: Directions Of Industry 50mentioning

confidence: 99%

Examining Digital Transformation Trends in Austrian and Hungarian Companies

2023

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“…Since experts usually define intervals for each model parameter. The interval can represent uncertainty but it can also represent variabilities, such as different raw materials, different separation efficiencies, or different operating modes [21]. The advantage of this method is that we can immediately see the effect of uncertainty on the output streams in the simulator.…”

Section: Monte Carlo Simulation and Expert-based Model Validationmentioning

confidence: 99%

Expert-Based Modular Simulator for Municipal Waste Processing Technology Design

et al. 2022

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One of the significant problems in our society is the handling and processing of the vast amount of waste produced by households and industrial processes. Nowadays, packaging material regulations are constantly changing, which can significantly impact the quality of municipal waste, requiring the continuous development and redesign of waste processing plants. Since only a few uncertain measurements (composition, mass, etc.) are available for this task, analysing and redesigning waste processing technologies is challenging. This research aims to develop a modelling and simulation concept that can integrate all the available information and can also handle the uncertainty of the measurements. The proposed modular modelling framework can serve as a basis for designing and redesigning the technologies needed to process ever-changing municipal waste. The most important steps of the framework are as follows: identifying the typical equipment, these are the elements; building models of the elements; determining the characteristic parameters of the equipment; exploring the possible relationships between the elements. For example, the information needed to define the model parameters can be gathered from measurements, industrial experience, and expert knowledge. In many cases, the data obtained represent ranges. The stationary model framework applies efficiency factors and divides the solids into substreams based on expert knowledge. Furthermore, a modular simulator framework was developed to simulate the technological schemes with various connections. The specifications for all widely used waste industrial equipment (shredders, air separators, sieves, magnetic-, eddy current-, optical-, and ballistic separators) were used to construct the developed simulator. This simulator can open new opportunities for the design of waste sorting technological networks. The model was calibrated based on expertise gained from operating the studied technology. The changes in the material parameters can be considered, and the modular simulator can lead to flexible waste sorting technologies capable of adapting to governmental and environmental regulations changes. The main result of the work is that a methodology for designing a modular simulator, model development, and a validation method has been proposed, which provides the possibility to deal with uncertainty. All this is successfully presented through the analysis of an operating waste separation system.

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“…Scheduling [1,2] is a classical manufacturing problem that consists in organizing the execution of a set of tasks or operations to make the best use of the available resources under some constraints in order to fulfill some objectives. Solving scheduling problems is essential not only in industrial applications [3,4], in particular in Industry 4.0 and 5.0 [5], but also in healthcare [6,7], computing infrastructures [8] or education [9]. Besides its undeniable applicability, scheduling also poses a computational challenge, since many of its problems are NP-Hard [10].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of research devoted to scheduling in general, and to energy-efficient scheduling in particular, assumes a deterministic setting. However, uncertainty pervades real-life problems and its management poses a challenge in industry 5.0 [5]. Hence the relevance of the subfield of fuzzy shop scheduling [55,56], where uncertainty is handled using fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

Gómez

González-Rodríguez

Vela

2023

ICA

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The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption, which combines the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. To solve this NP-Hard problem, we propose a memetic algorithm, a hybrid metaheuristic method that combines global search with local search. Our focus has been on obtaining an efficient method, capable of obtaining similar solutions quality-wise to the state of the art using a reduced amount of time. To assess the performance of our algorithm, we present an extensive experimental analysis that compares it with previous proposals and evaluates the effect on the search of its different components.

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Scheduling Under Uncertainty for Industry 4.0 and 5.0

Cited by 16 publications

References 349 publications

Examining Digital Transformation Trends in Austrian and Hungarian Companies

Examining Digital Transformation Trends in Austrian and Hungarian Companies

Expert-Based Modular Simulator for Municipal Waste Processing Technology Design

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

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