An intelligent decision support system for production planning based on machine learning

Rodríguez, Germán; González-Cava, José M.; Pérez, José Andrés Moreno

doi:10.1007/s10845-019-01510-y

Cited by 84 publications

(23 citation statements)

References 43 publications

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“…However, most authors identify their efficiency and effectiveness as the most important indicators of expert systems. Efficiency is understood as a level of achievement of the purpose in the set conditions, connected with results of decision-making, received on an output, that is efficiency of correlation of inputs (resources) and outputs [7]. Efficiency means the use of the smallest amount of resources, however, on the other hand, efficiency can be verified by feedback from users of the expert system.…”

Section: Resultsmentioning

confidence: 99%

The expert evaluation systems in drug development

et al. 2021

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This study investigates the unique properties and criteria of use intellectual systems in drug delivery. The traditional methods of pharmaceutical drug development are limited in the field of usability. In connection with the above mentioned, the task of development and creation of the intellectual system allowing to choose active pharmaceutical ingredients, auxiliary substances, which will be based on the expert system and information base of knowledge in the field of pharmaceutical production and it should be of current scientific and practical relevance. The most commonly used type of intelligent systems today are expert systems, which are computer systems capable of partially replacing a highly qualified specialist in his or her field of competence through the knowledge previously obtained from him or her. That is why expert systems are accepted to be considered together with knowledge bases, which are models of specialists’ behavior in a certain field, and for decision-making procedures of logical conclusion are applied. The field of application of expert systems is absolutely unlimited. A vivid example is their use in diagnostic tasks of modern medicine.

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Section: Resultsmentioning

confidence: 99%

The expert evaluation systems in drug development

et al. 2021

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“…Importantly, they present areas for investment in hardware, software, data management, and workforce education in the coming years to realize the potential of AE systems for materials research and development. Similar opportunities and challenges for closed-loop automation exist outside the field of materials science as well, such as drug discovery, 67 healthcare, 68 supply chain management, 69 architecture, 70 and chemistry. 71,72 Although an exhaustive review falls outside the scope of this perspective, we urge the interested reader to consult recent review articles and perspectives that explore the intersection of materials science with one or more of the topics in information science outlined in this perspective.…”

Section: Highlighting Recent Progress and Reviewsmentioning

confidence: 92%

Section: Highlighting Recent Progress and Reviewsmentioning

confidence: 92%

The materials tetrahedron has a “digital twin”

et al. 2022

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For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework—adapted from the concept of a “digital twin”—which offers a nexus joining materials science and information science. We believe this high-level framework, the materials–information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward. Impact statement This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials–information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)—factors that recognize how systems impact and interact with other systems—in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead. Graphic abstract

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“…These papers describe simplified SC systems as a Markov decision process and use reinforcement learning to learn a policy for effectively operating the supply chain. In contrast to the dynamic system representations, [21] considers a decision support system for managing static SC systems based on fuzzy logic and regression trees.…”

Section: Review Of Approachesmentioning

confidence: 99%

Development of an Integrated Data-Driven Process to Handle Uncertainties in Multi-Variant Production and Logistics: A Survey

Dürr

Lamprecht

Kauffmann³

et al. 2021

Advances in Automotive Production Technology – Theory and Application

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A key differentiator in customer satisfaction in the automotive industry is offering a choice of high-dimensional possibilities to customize an individual vehicle. In the luxury segment this equals more than one billion variants. Innovative data-driven processes are necessary for the planning and handling of vehicles in production and distribution in order to guarantee indispensable factors such as stability, flexibility and transparency across the entire supply chain and to deliver the right vehicle to the right place at the promised time. Highly complex business environments, multi-variant products, trends with effects on distribution networks and future mobility concepts confront manufacturers with new challenges. This paper provides a survey of currently used methods and technologies to handle the previously mentioned challenges in the area of the customer order management of an automotive manufacturer. A specific field of research is the concept of planned orders which are derived from early anticipated customer requirements and their utilization throughout the entire planning and order management process. In addition to the generic approaches for achieving integrated planning of sales and production programs as well as the resulting material requirements, artificial intelligence methods are investigated with regard to the concept of the planned orders. For this purpose, various existing approaches to anticipate planned orders and to effectively manage the supply chain are examined. In conclusion, this paper provides a survey of state of the art methods regarding artificial intelligence linked to current research on agile production systems. Despite existing uncertainty in the upcoming years the implementation of stable data-driven processes is crucial. In this context, the possibility to prepare an automotive manufacturer for the upcoming challenges in digitalization and globalization is evaluated.

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An intelligent decision support system for production planning based on machine learning

Cited by 84 publications

References 43 publications

The expert evaluation systems in drug development

The expert evaluation systems in drug development

The materials tetrahedron has a “digital twin”

Development of an Integrated Data-Driven Process to Handle Uncertainties in Multi-Variant Production and Logistics: A Survey

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