A critical survey on the status of multiple criteria decision making theory and practice

Stewart, Theodor J.

doi:10.1016/0305-0483(92)90003-p

Cited by 523 publications

(268 citation statements)

References 40 publications

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“…Weights w i indicate the relative importance of an improvement in one attribute from its worst level to its best level compared with changes in other attributes. There are many techniques to elicit attribute weights, for a review see Stewart (1992) and Weber and Borcherding (1993). The methods studied in this experiment and the way they are used are described below.…”

Section: Attribute Weightingmentioning

confidence: 99%

On the convergence of multiattribute weighting methods

Pöyhönen¹,

Hämäläinen²

2001

European Journal of Operational Research

244

106

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Five multiattribute weighting methods were compared in an Internet experiment. This is the first experiment where the subjects created the alternatives and attributes themselves. Each subject used five methods to assess attribute weights, one version of the Analytic Hierarchy Process (AHP), DIRECT weighting, Simple Multiattribute Rating Technique (SMART), SWING weighting and TRADEOFF weighting. They can all be used following the principles of multiattribute value theory. Furthermore, each of them asks decision makers to give numerical estimates of weight ratios although the elicitation questions are different. In earlier studies, however, these methods have yielded different weights. Our results suggest that weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers. The other consequences are that the spread of weights and the inconsistency between the preference statements depend on the number of attributes that a decision maker considers simultaneously.

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Section: Attribute Weightingmentioning

confidence: 99%

On the convergence of multiattribute weighting methods

Pöyhönen¹,

Hämäläinen²

2001

European Journal of Operational Research

244

106

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“…A seaweed industrial cluster is proposed as the third alternative. A cluster is 'a geographic concentration of a specific industry, together with its supporting and related industries and service providers, including government and other institutions, such as universities and trade associations' (Porter 1998). Cluster organizations are commonly a partnership between public and private institutions.…”

Section: Proposed Alternative Risk-mitigation Strategiesmentioning

confidence: 99%

Managing risks in the Indonesian seaweed supply chain

Mulyati

Geldermann

2016

Clean Techn Environ Policy

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Seaweed supply chains in Indonesia, especially carrageenan and agar products, are subject to risks arising both inside the participating companies and in their external networks. Uncertainties in yield, quality, price, and infrastructure in one part of the supply chain can affect the whole chain. To ensure a sustainable seaweed industry, an appropriate supply chain risk management (SCRM) is needed. There are four critical steps in SCRM: identifying seaweed supply chains, identifying and categorizing risks, assessing risks, and mitigating risks. To identify seaweed supply chains, we conducted field research, in-depth interviews, and literature studies. The field survey was conducted in the Provinces of South Sulawesi, West Java, East Java, Banten, and West Nusa Tenggara. The seaweed supply chains were modeled by the software Umberto to get a better understanding of material and energy flows between the key members. To identify and categorize the risks, we started with the risks mentioned in the existing literature works, and then applied the Delphi method to analyze the potential risk sources, their causes, and their impacts. To assess risks, we used a semiquantitative approach through the face-to-face interviews to generate a risk map showing the likelihood, and impact of adverse events. Afterward, the risk intensity was categorized based on the value of the responses and classified into five categories: negligible, marginal, critical, most critical, and catastrophic risks. The mitigation strategies considered sustainability (environment, economy, and social) and risks criteria. Multi-criteria decision analysis was used to evaluate these strategies.

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“…As well as climate change mitigation, energy security, affordability, and the health impacts of energy systems are among the examples of such a range of sometimes conflicting policy objectives and decision criteria. In this context, multi-criteria optimization approaches can shed additional insights into technology portfolio strategies (for reviews see, e.g., Stewart, 1992;Wierzbicki et al, 2000;Figueira et al, 2005).…”

Section: Multi-criteria Optimizationmentioning

confidence: 99%

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

Grübler¹,

Fuss²,

McCollum³

et al. 2013

Energy Technology Innovation

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AUTHORS' SUMMARYThe case study reviews a range of methodologies and model-based applications that can assist policy decisions under irreducible technology innovation uncertainties. One approach uses scenario analysis. A scenario study that explored a range of salient future uncertainties identified energy efficiency and conservation as the single most important and also most robust technology option for climate mitigation. Another approach uses portfolio theory to quantify the benefits from diversified technology portfolios in the framework of risk-averse decision-making. Such studies suggest risk aversion leads to higher adoption rates of currently higher-cost energy technology options such as modern biomass, renewables, and also carbon capture and storage (CCS). Diversification not only reduces the mean of risk exposure but also drastically lowers the tails of extreme undesirable outcomes. Portfolio theory and scenario analysis can also be combined, as illustrated by a study of three different energy system objectives (energy security, air pollution, climate change) within a multi-criteria optimization framework. Scenarios in which objectives were met individually, and then all together, under a range of uncertainties again demonstrated the benefits of portfolio diversification with greater emphasis on energy efficiency and "general purpose" energy conversion technologies. These examples discussed in this case study show that formal tools and approaches, including scenario analysis, portfolio theory, and multi-criteria optimization, are increasingly available to move technology portfolio decisions onto a more rational ground.

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A critical survey on the status of multiple criteria decision making theory and practice

Cited by 523 publications

References 40 publications

On the convergence of multiattribute weighting methods

On the convergence of multiattribute weighting methods

Managing risks in the Indonesian seaweed supply chain

Technology Portfolios: Modelling Technological Uncertainty and Innovation Risks

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