A Multi-Objective Decision Analysis of Plants for Maintenance Expert System Development

2016 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Olanrewaju M. Adesusi ◽  
Olayide R. Adetunji ◽  
Sam B. Adejuyigbe ◽  
Adebayo F. Adekoya ◽  
Samuel O. Ipadeola
Keyword(s):  
Expert System ◽  
Decision Analysis ◽  
System Development ◽  
Multi Objective

scholarly journals Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Anna Sandak ◽  
Jakub Sandak ◽  
Dominika Janiszewska ◽  
Salim Hiziroglu ◽  
Marta Petrillo ◽  
...  
Keyword(s):  
Quality Control ◽  
Expert System ◽  
Near Infrared ◽  
System Development ◽  
Nir Spectroscopy ◽  
Original System ◽  
Adhesive System ◽  
Type Composition ◽  
Wide Range ◽  
Panel Type

The overall goal of this work was to develop a prototype expert system assisting quality control and traceability of particleboard panels on the production floor. Four different types of particleboards manufactured at the laboratory scale and in industrial plants were evaluated. The material differed in terms of panel type, composition, and adhesive system. NIR spectroscopy was employed as a pioneer tool for the development of a two-level expert system suitable for classification and traceability of investigated samples. A portable, commercially available NIR spectrometer was used for nondestructive measurements of particleboard panels. Twenty-five batches of particleboards, each containing at least three independent replicas, was used for the original system development and assessment of its performance. Four alternative chemometric methods (PLS-DA, kNN, SIMCA, and SVM) were used for spectroscopic data classification. The models were developed for panel recognition at two levels differing in terms of their generality. In the first stage, four among twenty-four tested combinations resulted in 100% correct classification. Discrimination precision with PLS-DA and SVMC was high (>99%), even without any spectra preprocessing. SNV preprocessed spectra and SVMC algorithm were used at the second stage for panel batch classification. Panels manufactured by two producers were 100% correctly classified, industrial panels produced by different manufacturing plants were classified with 98.9% success, and the experimental panels manufactured in the laboratory were classified with 63.7% success. Implementation of NIR spectroscopy for wood-based product traceability and quality control may have a great impact due to the high versatility of the production and wide range of particleboards utilization.

Success factors for manufacturing expert system development

1995 ◽  
Vol 28 (3) ◽  
pp. 545-559 ◽  
Author(s):  
Tor Guimares ◽  
Youngohc Yoon ◽  
Quinton O'Neal
Keyword(s):  
Expert System ◽  
Success Factors ◽  
System Development

Fuel Insert Shuffler: A Case Study of Expert System Development

1988 ◽  
pp. 625-633 ◽  
Author(s):  
Joseph Naser ◽  
Robert Colley ◽  
John Gaiser ◽  
Thomas Brookmire ◽  
Steven Engle
Keyword(s):  
Expert System ◽  
System Development

scholarly journals Site Selection for Joint Logistics over the Shore (JLOTS) Operations Using Multi- Objective Decision Analysis

2016 ◽  
Vol 4 (2) ◽  
pp. 88-95
Author(s):  
Mike Evans ◽  
Mike Lami ◽  
Brendan Madarasz ◽  
Benjamin Smith ◽  
Chris Green
Keyword(s):  
Decision Analysis ◽  
Site Selection ◽  
Decision Support Tool ◽  
Military Operations ◽  
Support Tool ◽  
Multi Objective ◽  
Selection Protocol ◽  
Operational Criteria ◽  
A Site ◽  
Development Center

As the U.S. military faces an increasing need to deploy across a range of military operations and environments, the ability to establish and sustain logistics support remains a major challenge. The Engineer Research and Development Center is currently developing the Planning Logistics Analysis Network System (PLANS), a decision support tool, to facilitate strategic and operational logistics planning. This paper describes a site selection protocol for logistics operations occurring without a suitable port, commonly referred to as Logistics over-the Shore (LOTS) operations. The model uses multi- objective decision analysis techniques to weight different operational criteria to determine the best overall site for logistics over the shore operations. This tool will enhance the time and accuracy in determining an optimal site that meets the decision maker’s specific operational needs.

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