scholarly journals AI Overview: Methods and Structures

2021 ◽  
Author(s):  
Erik Dahlquist ◽  
Moksadur Rahman ◽  
Jan Skvaril ◽  
Konstantinos Kyprianidis
Keyword(s):  
Decision Support ◽  
Production Planning ◽  
Process Optimization ◽  
Statistical Models ◽  
Data Cleaning ◽  
Learning Systems ◽  
Process Industry ◽  
Soft Sensors ◽  
On Demand ◽  
Industry Applications

This paper presents an overview of different methods used in what is normally called AI-methods today. The methods have been there for many years, but now have built a platform of methods complementing each other and forming a cluster of tools to be used to build “learning systems”. Physical and statistical models are used together and complemented with data cleaning and sorting. Models are then used for many different applications like output prediction, soft sensors, fault detection, diagnostics, decision support, classifications, process optimization, model predictive control, maintenance on demand and production planning. In this chapter we try to give an overview of a number of methods, and how they can be utilized in process industry applications.

Die synchrone Produktion*/Synchronous manufacturing - An approach for semi-autonomous production planning and control and human-based decision support

2015 ◽  
Vol 105 (04) ◽  
pp. 204-208
Author(s):  
D. Kreimeier ◽  
E. Müller ◽  
F. Morlock ◽  
D. Jentsch ◽  
H. Unger ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Production Planning ◽  
Data Exchange ◽  
Production Control ◽  
Production Planning And Control ◽  
Planning And Control ◽  
Manufacturing Execution Systems ◽  
And Control ◽  
Manufacturing Execution

Kurzfristige sowie ungeplante Änderungen – wie Auftragsschwankungen, Maschinenausfälle oder Krankheitstage der Mitarbeiter – beeinflussen die Produktionsplanung und -steuerung (PPS) von Industriefirmen. Trends wie Globalisierung und erhöhter Marktdruck verstärken diese Probleme. Zur Komplexitätsbewältigung bei der Entscheidungsfindung zur Fertigungssteuerung kommen in der Produktion Werkzeuge der „Digitalen Fabrik“, beispielsweise Simulationsprogramme, oder IT (Informationstechnologie)-Lösungen, wie Manufacturing Execution Systems (MES), zum Einsatz. Eine Verknüpfung dieser Bereiche würde einen echtzeitfähigen Datenaustausch erlauben, der wiederum eine echtzeitfähige Entscheidungsunterstützung bietet. Der Fachbeitrag stellt hierfür einen Lösungsansatz vor.   Sudden and unsystematic changes, such as fluctuations in order flow, machine failures, or employee sick days affect the Production Planning and Control (PPC) activities of industrial companies. Trends like globalization and increased market pressure intensify these problems. To master the complexity of decision-making in production control, tools of the digital factory (e.g. simulation systems) or IT systems (e.g. Manufacturing Execution Systems (MES)) are applied in manufacturing. Combining these areas would enable real-time capable data exchange which, in turn, provides real-time capable decision support. This article presents an approach for solving this problem.

scholarly journals An Intelligent Decision Support System for Production Planning in Garments Industry

2021 ◽  
pp. 378-386
Author(s):  
Rui Ribeiro ◽  
André Pilastri ◽  
Hugo Carvalho ◽  
Arthur Matta ◽  
Pedro José Pereira ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Production Planning ◽  
Support System ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Intelligent Decision Support

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

Human-Computer Cloud and Its Applications in E-Tourism

2021 ◽  
pp. 1143-1176
Author(s):  
Andrew Ponomarev ◽  
Nikolay Shilov
Keyword(s):  
Information Processing ◽  
Decision Support ◽  
Resource Management ◽  
Ad Hoc ◽  
Dynamic Environments ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
On Demand ◽  
Building Information ◽  
Developing Area

The chapter addresses two problems that typically arise during the creation of decision support systems that include humans in the information processing workflow, namely, resource management and complexity of decision support in dynamic environments, where it is impossible (or impractical) to implement all possible information processing workflows that can be useful for a decision-maker. The chapter proposes the concept of human-computer cloud, providing typical cloud features (elasticity, on demand resource provisioning) to the applications that require human input (so-called human-based applications) and, on top of resource management functionality, a facility for building information processing workflows for ad hoc tasks in an automated way. The chapter discusses main concepts lying behind the proposed cloud environment, as well as its architecture and some implementation details. It is also shown how the proposed human-computer cloud environment solves information and decision support demands in the dynamic and actively developing area of e-tourism.

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