Decision making in complex environments under conditions of high cognitive loading: A personal expert systems approach

Tacker, E.C.; Silvia, Manuel T.

doi:10.1016/0957-4174(91)90109-r

Cited by 9 publications

(6 citation statements)

References 1 publication

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“…It was reported that the crisp AHP method was superior than the fuzzy TOPSIS and fuzzy outranking methods. Several other investigators applied FAHP as a multi criteria decision support for solving complex problems of real world (Tacker and Silvia 1991;Yager 1991Yager , 1992Beynon et al 2001;Mikhailov 2003;Hsieh et al 2004;Kong and Lue 2005;Kulak 2005;Chen and Chen 2005;Jaganathan et al 2007;Lin et al 2007;Wang et al 2008;Fu 2008;Hua et al 2008;Kahraman and Cebi 2009;Chen and Wang 2009;Chen and Lee 2010;Azizi and Fathi 2014 etc. ).…”

Section: Fuzzy Ahp (Fahp)mentioning

confidence: 98%

Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization

Jaiswal

Ghosh

Lohani

et al. 2015

Water Resour Manage

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The article presents an efficient multi-criteria decision support model (MCDSM) to prioritize susceptible areas in a watershed for soil conservation measures based on impact analysis of topography, climate, morphology, soil, land cover, management and conservation factors. The MCDS model has been developed based on fuzzy analytical hierarchical process (FAHP) by computing its weights of nine erosion hazard parameters (EHPs), which include; soil loss from revise universal soil loss equation model, sediment yield, sediment production rate, sediment transport index, slope, drainage density, channel frequency, form factor, and circulatory ratio. The MCDS model has successfully been applied to Benisagar reservoir catchment in Madhya Pradesh (India), comprised of 36 sub-watersheds for prioritizing areas require soil conservation measures. For computing weights of EHPs in FAHPs, triangular, narrow rectangular, medium rectangular and wide rectangular fuzzy membership functions have been used followed by the geometric mean method to determine the final weight matrix. The test of consistency ratio showed wide rectangular function as the most effective one in determining the weights of EHPs with soil loss as the most sensitive and circulatory ratio as the least sensitive parameter. The final priorities of the sub-watersheds have been determined using weight and their corresponding normalized values of EHPs. Based on the clustering of final priorities, the sub-watersheds have been categorized into four groups of priorities, i.e., very high, high, medium, low and very low for soil and water conservation measures. The derived methodology can successfully be used in prioritization of soil conservation measures in a basin for developing catchment area treatment plan.

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Section: Fuzzy Ahp (Fahp)mentioning

confidence: 98%

Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization

Jaiswal

Ghosh

Lohani

et al. 2015

Water Resour Manage

View full text Add to dashboard Cite

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“…In recent years, some methods have been presented for handling multicriteria decision making problems (Bellman & Zadeh, 1970;Beynon, Cosker, & Marshall, 2001;Bordogna & Pasi, 1995;Chen, 1988;Chen & Chen, 2005;Fu, 2008;Filev & Yager (1995, 1999a, 1999b; Fullér & Majlender, 2003;Kulak, 2005;Kwon, Kim, & Lee, 2007;Kwon & Kim, 2004;Lin, Hsu, & Sheen, 2007;Liu, 2006;Tacker & Silvia, 1991;Torra, 1997;Wang & Chen, 2007;Yager (1988Yager ( , 1991Yager ( , 1992Yager ( , 1996Yager ( , 1998Yager ( , 2004aYager ( , 2004b). Some methods have been presented for handling prioritized multicriteria decision making problems (Chen & Chen, 2005;Wang & Chen, 2007;Yager (1991Yager ( , 1992Yager ( , 1998Yager ( , 2004b).…”

Section: Introductionmentioning

confidence: 99%

A generalized model for prioritized multicriteria decision making systems

Chen

Wang

2009

Expert Systems with Applications

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“…MYCIN developers adopted a different view which proved to be effective in medical applications and was also applied in many other domains: they conceptualised uncertainty as a degree of belief in a fact or rule 4 .…”

Section: Mycin Certainty Factorsmentioning

confidence: 99%

“…Consequently, they tend to perform inaccurate corrective actions and answer the problems in a wrong sequence. The cognitive overload they are submitted to withholds them to execute a correct, consistent and quick course of actions (see [2], [3] and [4] for actual examples).…”

Section: Introductionmentioning

confidence: 99%

Clinical communication and telemedicine

Coiera¹

2003

Guide to Health Informatics, 2Ed

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Human operators of monitoring and control systems typically perform knowledge intensive tasks requiring human intervention in diverse and sometimes critical situations. The cognitive overload to which they are submitted may motivate human errors which compromise safety and performance. Expert Systems offer the means to codify and automate the use of the knowledge of the best experts in a given domain, and may provide support to monitoring, diagnosis and control tasks. In this text we will make an overview of what expert systems are, what they may offer and what they don't. We will present their typical architecture and the most common knowledge representation formalisms and reasoning mechanisms, and discuss how they may deal with uncertainty. The lifecycle of an Expert System will also be presented, special attention being devoted to knowledge acquisition activities. An overview of some applications of expert systems to monitoring and control will be given, as well as some alternative approaches. INTRODUCTIONThe control needs of complex and expensive processes have motivated the development of sophisticated computational systems which allow the monitoring and control of complex processes by small crews of specialised human operators. Typically, these operators follow the process evolution through a set of instruments connected to a computer system which interacts with sensors, alarms and actuators. The control system is intended to minimize human intervention and to provide the operators with the maximum information on the controlled process. The operators' task essentially consists in trying to minimize failure risks while maximizing performance. The required degree of expertise depends on the complexity of the process and of the control system, on the importance, to people and equipment safety, of keeping the process in normal operation, and on the losses that an incorrect process control or its interruption may cause.In normal steady-state conditions, the control system takes charge of keeping the system in a given set point, and the operators confine themselves to monitoring the most important variables. Human intervention is required during system start-up and shut-down, and also to regulate the process to given resources and needs.Another, more critical, kind of situation occurs when equipment or process failures, or human errors, originate anomalous conditions. In such circumstances the control system presents a great quantity of information to the operators in a short time, originating in the most diverse sources, with the process conditions quickly changing and alarms escalating. In such a stressed situation, operators must discriminate actual faults from false alarm states, perform malfunction diagnosis, and plan and accomplish corrective actions 1 . This requires them to intensively resort to their knowledge on the process and on the control system. In particularly complex, stressed or time-critical situations, they 1 According to [1], "monitoring activities track process variables intelligent...

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Decision making in complex environments under conditions of high cognitive loading: A personal expert systems approach

Cited by 9 publications

References 1 publication

Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization

Fuzzy AHP Based Multi Crteria Decision Support for Watershed Prioritization

A generalized model for prioritized multicriteria decision making systems

Clinical communication and telemedicine

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