The impact of Industry 4.0 and AI on economic growth

Vyshnevskyi, Oleksandr; Lyashenko, V. I.; Amosha, Oleksandr

doi:10.29119/1641-3466.2019.140.32

Cited by 11 publications

(2 citation statements)

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“…At the same time, the rapid development of information and communication services within Industry 4.0 has made it possible to significantly change the organization of business and production processes. In particular, the authors of [11] note the positive impact of these technologies on economic results. A number of scientists recognize possible threats to the social development of society and the ambiguity of the impact on the environment [12].…”

Section: Research Of Existing Solutionsto the Problemmentioning

confidence: 99%

Creating the model of balanced business development based on ecosystem approach

Skorobogatova

2022

TAPR

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The object of research is the process of creating effective relationships between the participants of the economic system, which is based on the need to ensure its balanced development in an unstable environment. One of the most problematic places is that at present the main criterion for the effectiveness of the model for organizing relationships between participants is the economic result, other aspects of the rational creation of an ecosystem are not considered, namely: innovative, environmental, social. The study proved that considering a systematic approach allows making effective management decisions at the level of a business ecosystem, considering the results obtained by all its agents. The basis of the modelof balanced business development model is the transition to a circular economy, which allows reducing resource consumption, reducing the negative impact on the environment, and complying with relevant socio-economic standards. On the example of an agricultural complex, a model of an agribusiness ecosystem was created, including participants and internal relationships between them. The agents of the internal environment include resource (service) providers, agricultural manufacturers and customers (buyers), detailing their roles in this system. A methodical approach to assessing the effectiveness of management decisions based on a multi-criteria assessment in four areas is proposed: economic, environmental, social, and innovative. For implementation, one should choose a solution that gives the maximum value of the integral indicator at the system level, considering the participation of each of its agents. The proposed solutions will contribute to more sustainable production and consumption patterns, which will provide opportunities for developed and developing countries to achieve economic growth and sustainable development.

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Section: Research Of Existing Solutionsto the Problemmentioning

confidence: 99%

Creating the model of balanced business development based on ecosystem approach

Skorobogatova

2022

TAPR

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“…Rising in popularity are artificial intelligence methods, with an estimated economic potential of over $15 trillion by 2030. From these methods, reinforcement learning is a promising application to process scheduling in the process industry [10,11]. Reinforcement learning (RL), from the field of machine learning, describes a class of algorithms to be used in simulation-based optimization.…”

Section: Introductionmentioning

confidence: 99%

Production Scheduling Using Deep Reinforcement Learning and Discrete Event Simulation

Hubert,

Meintschel,

Bleidorn

et al. 2023

Chemie Ingenieur Technik

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Scheduling in the process industry is a highly demanding task. Having access to optimal production schedules at short notice, for instance, after spontaneous changes, offers numerous advantages in terms of robustness, economics, and ultimately customer satisfaction, as delays are minimized. In this work, we describe our initial efforts to apply and evaluate deep reinforcement learning (DRL) for optimized scheduling in a typical fill-and-finish batch production plant in the chemical industry. Our pilot study demonstrates how DRL can be implemented using an approach based on discrete event simulation. We discuss the results and benefits of DRL, compare it to mathematical programming approaches, and outline a potential path forward. Our study suggests that the application of DRL in the chemical industry is a promising research direction and that DRL can complement established methods such as process simulation and mathematical programming.

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