scholarly journals Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations

2020 ◽  
Vol 8 (9) ◽  
pp. 689
Author(s):  
Jennifer Smith ◽  
Fatemeh Yazdanpanah ◽  
Rebecca Thistle ◽  
Mashrura Musharraf ◽  
Brian Veitch
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
New Technologies ◽  
Expert Knowledge ◽  
Rapid Change ◽  
Management Decision ◽  
Case Based Reasoning ◽  
Maritime Operations ◽  
Decision Support Technology

The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.

Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno

2016 ◽  
Vol 15 (05) ◽  
pp. 923-948 ◽  
Author(s):  
Carmen De Maio ◽  
Aurelio Tommasetti ◽  
Orlando Troisi ◽  
Massimiliano Vesci ◽  
Giuseppe Fenza ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Description Logic ◽  
Expert Knowledge ◽  
Business Strategies ◽  
Management Decision ◽  
Simulation Environment ◽  
Knowledge Based

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers’ opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer’s opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno.

RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application

Weed Science ◽  
2015 ◽  
Vol 63 (3) ◽  
pp. 676-689 ◽  
Author(s):  
Myrtille Lacoste ◽  
Stephen Powles
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Weed Management ◽  
Cropping Systems ◽  
Integrated Management ◽  
Management Decision ◽  
Agricultural Community ◽  
End Use ◽  
Term Performance

RIM, or “Ryegrass Integrated Management,” is a model-based software allowing users to conveniently test and compare the long-term performance and profitability of numerous ryegrass control options used in Australian cropping systems. As a user-friendly decision support system that can be used by farmers, advisers, and industry professionals, RIM can aid the delivery of key recommendations among the agricultural community for broadacre cropping systems threatened by herbicide resistance. This paper provides advanced users and future developers with the keys to modify the latest version of RIM in order to facilitate future updates, modifications, and adaptations to other situations. The various components of RIM are mapped and explained, and the key principles underlying the construction of the model are explained. The implementation of RIM into a Microsoft Excel® software format is also documented, with details on how user inputs are coded and parameterized. An overview of the biological, agronomic, and economic components of the model is provided, with emphasis on the ryegrass biological characteristics most critical for its effective management. The extreme variability of these parameters and the subsequent limits of RIM are discussed. The necessary compromises were achieved by emphasizing the primary end-use of the program as a decision support system for farmers and advisors.

A decision support system for rehabilitation of sewer pipes

2001 ◽  
Vol 28 (3) ◽  
pp. 394-401 ◽  
Author(s):  
Tariq Shehab-Eldeen ◽  
Osama Moselhi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Database Management ◽  
New Technologies ◽  
Database Management System ◽  
Sewer Pipes ◽  
Sewer Rehabilitation ◽  
Multi Attribute Utility Theory ◽  
Need For Rehabilitation

The condition of sewer pipes in North America has severely deteriorated, over the last few decades, creating a need for rehabilitation. Sewer rehabilitation methods are numerous and are constantly being developed, benefiting from emerging technologies. The implementation of these methods is driven by the need to improve quality and to reduce cost and project duration. One of the rapidly expanding fields in the sewer rehabilitation industry is trenchless technology. Due to the large number of methods associated with emerging new technologies in this field, selecting the most suitable method can be a challenging task. Selection in this environment, without a computerized tool, may also suffer from the limited knowledge and (or) experience of the decision-maker and could result in overlooking some of the suitable methods that could do the job at less cost. This paper describes a recently developed system for rehabilitation of concrete and clay sewer pipes and focuses primarily on two of its components: (i) the database management system (DBMS) and (ii) the decision support system (DSS). The system can assist municipal engineers and contractors in selecting the most suitable trenchless rehabilitation technique that specifies job conditions and user requirements. An example application is presented to demonstrate the use and capabilities of the developed system.Key words: pipe defects, rehabilitation, sewer pipes, database management systems, decision support systems, multi-attribute utility theory.

Sign in / Sign up

Export Citation Format

Share Document