Multi-criteria scheduling: an agent-based approach for expert knowledge integration

In order to compete in challenging global markets, Rolls-Royce must have the capability to manage complex processes effectively. As the environment surrounding these processes is constantly changing, the ability to adapt to meet new requirements is essential. The challenge to the business is to quickly adopt an appropriate course of action for each set of circumstances, anticipated or otherwise. An individual decision-maker faced with this situation will normally have several options available. The difficulty is assessing the ramifications of each within a given time frame. To improve the quality of this evaluation, computerized decision support tools can be used. Such tools offer the ability to assess a multitude of options in a short space of time, using a combination of expert knowledge and real data. Having developed an extensive aero-engine service business, Rolls-Royce is required to maintain a highly dependable aftermarket infrastructure. Therefore, the ability to compare how any one particular aftermarket design would perform relative to another is an essential capability. Agent-based systems offer an approach that is both intuitive and interactive, modelling individual entities in the system from the bottom up, capturing low-level interactions that ultimately determine the overall performance of the system. This provides the flexibility and transparency to allow trustworthy analysis and evaluation to take place. To address this business need, an agent-based aftermarket model has been developed. An agent-based system is made up of small software programs built to operate just like a human team. Each agent has a set of capabilities and knowledge, but must work with other agents to achieve the overall goal. Agents can react to changes, adapting and re-planning if a better approach is identified. This paper describes how the model was constructed and the resulting analysis that it facilitates. The model has the ability to replicate the likely service characteristics that would be in place during the full lifecycle of the product. All aspects of the necessary infrastructure are captured, based upon the roles and capabilities of the constituent elements. Agents are used to represent a variety of objects and functions, including airports, airlines, aircraft, overhaul facilities and logistics. Through planning and negotiation, representative decisions are made by these agents to determine when an overhaul should take place and what the workscope should be based upon defined policies. A large selection of configurable parameters can be set by the user to accurately reflect the proposed scenario, providing a powerful what-if analysis tool that can be used to drive the design process, ensuring that product attributes and performance are aligned with the available maintenance infrastructure.

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Development and Analysis of an Enhanced Multi-Expert Knowledge Integration System for Designing Context-Aware Ubiquitous Learning Contents

International Journal of Distance Education Technologies ◽

10.4018/ijdet.2018100103 ◽

2018 ◽

Vol 16 (4) ◽

pp. 31-53 ◽

Cited By ~ 1

Author(s):

Gwo-Haur Hwang ◽

Beyin Chen ◽

Shiau-Huei Huang

Keyword(s):

Knowledge Acquisition ◽

Real World ◽

Domain Knowledge ◽

Knowledge Integration ◽

Expert Knowledge ◽

Context Aware ◽

Ubiquitous Learning ◽

Integration System ◽

Degree Of Acceptance ◽

Multiple Experts

This article describes how in context-aware ubiquitous learning environments, teachers must plan a theme and design learning contents to provide complete knowledge for students. Knowledge acquisition, which is an approach for helping people represent and organize domain knowledge, has been recognized as a potential way of guiding teachers to develop real-world context-related learning contents. However, previous studies failed to address the issue that the learning contents provided by multiple experts or teachers might be redundant or inconsistent; moreover, it is difficult to use the traditional knowledge acquisition method to fully describe the complex real-world contexts and the learning contents. Therefore, in this article, a multi-expert knowledge integration system with an enhanced knowledge representation approach and Delphi method has been developed. From the experimental results, it is found that the teachers involved had a high degree of acceptance of the system. They believe that it can unify the knowledge of many teachers.

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Proximity, knowledge integration and innovation: an agenda for agent-based studies

Journal of Evolutionary Economics ◽

10.1007/s00191-010-0200-6 ◽

2010 ◽

Vol 22 (1) ◽

pp. 19-47 ◽

Cited By ~ 3

Author(s):

Piergiuseppe Morone ◽

Richard Taylor

Keyword(s):

Knowledge Integration ◽

Agent Based

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Fuzzy Filtering of Sensors Signals in Manufacturing Systems with Time Constraints

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2010.3.2488 ◽

2010 ◽

Vol 5 (3) ◽

pp. 362 ◽

Cited By ~ 6

Author(s):

Anis M’halla ◽

Nabil Jerbi ◽

Simon Collart Dutilleul ◽

Etienne Craye ◽

Mohamed Benrejeb

Keyword(s):

Fuzzy Systems ◽

Manufacturing Systems ◽

Knowledge Integration ◽

Expert Knowledge ◽

Time Constraints ◽

Quality And Safety ◽

Job Shops ◽

New Approach ◽

Robustness Property ◽

Filtering Approach

The presented work is dedicated to the supervision of manufacturing job-shops with time constraints. Such systems have a robustness property towards time disturbances. The main contribution of this paper is a fuzzy filtering approach of sensors signals integrating the robustness values. This new approach integrates a classic filtering mechanism of sensors signals and fuzzy logic techniques. The strengths of these both techniques are taken advantage of the avoidance of control freezing and the capability of fuzzy systems to deal with imprecise information by using fuzzy rules. Finally, to demonstrate the effectiveness and accuracy of this new approach, an example is depicted. The results show that the fuzzy approach allows keeping on producing, but in a degraded mode, while providing the guarantees of quality and safety based on expert knowledge integration.

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A protocol for the conceptualisation of an agro-ecosystem to guide data acquisition and analysis and expert knowledge integration

European Journal of Agronomy ◽

10.1016/j.eja.2011.07.004 ◽

2012 ◽

Vol 38 ◽

pp. 104-116 ◽

Cited By ~ 14

Author(s):

Nathalie Lamanda ◽

Sébastien Roux ◽

Sylvestre Delmotte ◽

Anne Merot ◽

Bruno Rapidel ◽

...

Keyword(s):

Data Acquisition ◽

Knowledge Integration ◽

Expert Knowledge

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Agent-Based Environment for Knowledge Integration

Lecture Notes in Computer Science - Computational Science – ICCS 2009 ◽

10.1007/978-3-642-01973-9_98 ◽

2009 ◽

pp. 885-894 ◽

Cited By ~ 2

Author(s):

Anna Zygmunt ◽

Jarosław Koźlak ◽

Leszek Siwik

Keyword(s):

Knowledge Integration ◽

Agent Based

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A multi-source data feature fusion and expert knowledge integration approach on lithium-ion battery anomaly detection

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4051716 ◽

2021 ◽

pp. 1-14

Author(s):

Yudong Wang ◽

Xiwei Bai ◽

Chengbao Liu ◽

Jie Tan

Keyword(s):

Anomaly Detection ◽

Lithium Ion Battery ◽

Network Architecture ◽

Knowledge Integration ◽

Expert Knowledge ◽

Feature Fusion ◽

Lithium Ion ◽

Data Driven ◽

Power Sources ◽

Source Data

Abstract To meet voltage and capability needs, batteries are grouped into packs, as power sources. Abnormal ones in a pack will lead to partial heating and reduced available life, so removing anomalies out during manufacturing is of great significance. The conventional methods to detect abnormal batteries mainly rely on grading systems and manual operations. Current data-driven methods use statistical, machine learning and neural network approaches, building models, then applying them on the unlabeled. However, both cannot make full use of multiple source data, and expert knowledge. Therefore, how to use these multi-source data and knowledges to improve the effect of battery anomaly detection process has become a research focus. We put forward a data-driven multi-source data feature fusion and expert knowledge integration (FFEKI) network architecture which follows encoder-decoder structure with multiple integration units and a corresponding joint loss function. First, we collect multi-source data, and obtain fusion features. Then, we refine filters from expert knowledges. By this way, supervisory knowledges are integrated into our network by integration units. We evaluate our scheme by sets of experiments comparing with most widely used approaches on real manufacturing data. Results show that FFEKI obtains a maximum 100% anomaly detection rate (ADR). Meanwhile, when the number of detection T is greater than the actual number of anomalies in the sample set, our method can achieve full ADR faster. It is concluded that the proposed FFEKI achieves effective performance on power lithium-ion battery anomaly detection.

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