R&amp;D Decision Advisor: An interactive approach to normative decision system model construction

Regan, Peter J.; Holtzman, Samuel

doi:10.1016/0377-2217(94)00321-3

Cited by 25 publications

(8 citation statements)

References 6 publications

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“…A widely used model of decision-making, introduced by Holtzman [47], includes three stages: formulation, evaluation and appraisal. The two phases of formulation and appraisal are important stages of any decision-making process [35,47,81,95] but have been overlooked in the consumer decisionmaking literature. In the formulation stage, the decision problem is formulated in the mind of the decision-maker, alternatives are generated and criteria articulated.…”

Section: An Online Purchase Decision-making Process Modelmentioning

confidence: 99%

The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour

Karimi

Papamichail

Holland

2015

Decision Support Systems

186

125

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Section: An Online Purchase Decision-making Process Modelmentioning

confidence: 99%

The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour

Karimi

Papamichail

Holland

2015

Decision Support Systems

186

125

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“…Initial research prototypes and commercial systems have addressed medical, engineering, and business challenges (Holtzman, 1989; Heckerman et al, 1990; Horvitz and Barry, 1995; Regan and Holtzman, 1995).…”

Section: Normative Systems Expert Systems and W B R I D Systemsmentioning

confidence: 98%

Dynamic Risk Management Systems: Hybrid Architecture and Offshore Platform Illustration

Patè‐Cornell

Regan²

1998

Risk Analysis

Self Cite

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This paper describes and illustrates the architecture of computer-based Dynamic Risk Management Systems (DRMS) designed to assist real-time risk management decisions for complex physical systems, for example, engineered systems such as offshore platforms or medical systems such as patient treatment in Intensive Care Units. A key characteristic of the DRMSs that we describe is that they are hybrid, combining the powers of Probabilistic Risk Analysis methods and heuristic Artificial Intelligence techniques. A control module determines whether the situation corresponds to a specific rule or regulation, and is clear enough or urgent enough for an expert system to make an immediate recommendation without fbrther analysis of the risks involved. Alternatively, if time permits and if the uncertainties justify it, a risk and decision analysis module formulates and evaluates options, including that of gathering further information. This feature is particularly critical since, most of the time, the physical system is only partially observable, i.e., the signals observed may not permit unambiguous characterization of its state. The DRMS structure is also dynamic in that, for a given time window (e.g., 1 day or 1 hour), it anticipates the physical system's state (and, when appropriate, performs a risk analysis) accounting for its evolution, its mode of operations, the predicted external loads and problems, and the possible changes in the set of available options. Therefore, we specifically address the issue of dynamic information gathering for decisionmaking purposes. The concepts are illustrated focusing on the risk and decision analysis modules for a particular case of real-time risk management on board offshore oil platforms, namely of two types of gas compressor leaks, one progressive and one catastrophic. We describe briefly the DRMS proof-of-concept produced at Stanford, and the prototype (ARMS) that is being constructed by Bureau Veritas (Paris) based on these concepts. KEY WORDS Risk management; dynamic systems; probability artificial intelligence; offshore platforms; architecture.

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“…In literature several alternate criteria for financial evaluation of investment projects are discussed (Remer et al, 1993;Lefley & Morgan, 1998;Nowak, 2005;Biezma & San Cristobal, 2006). In this section, we will describe some of the most important, such as:…”

Section: Criteria For Determining Projects Financial Feasibilitymentioning

confidence: 99%

“…Over time, various approaches have been proposed for evaluation of projects such as: goal programming (Santhanam & Kyprasis, 1995;Oliveria et al, 2003), zero-one programming (Regan & Holtzman, 1995;Mavrotas et al, 2003), techniques based on the utility function (Graves & Rinquest, 1996;Wong et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Investment project selection by applying COPRAS method and imprecise data

Popović¹,

Stanujkić²,

Stojanovic³

2012

Serb J Management

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Investment projects can have a significant impact on the functioning and development of a company. Therefore, the selection of one or more investment projects from the set of possible is an important and difficult task for decision makers. This paper considers the investment projects selection based on financial analysis criteria and use of imprecise data. In the proposed model, the alternative projects performances are expressed using crisp and interval values, and then the best project from the available is selected by using COPRAS and COPRAS-G methods. A numerical example is given to demonstrate the applicability and effectiveness of the proposed approach.

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R&D Decision Advisor: An interactive approach to normative decision system model construction

Cited by 25 publications

References 6 publications

The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour

The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour

Dynamic Risk Management Systems: Hybrid Architecture and Offshore Platform Illustration

Investment project selection by applying COPRAS method and imprecise data

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