Evaluation of services using a fuzzy analytic hierarchy process

Mikhailov, Ludmil; Tsvetinov, Petco E.

doi:10.1016/j.asoc.2004.04.001

Cited by 366 publications

(173 citation statements)

References 23 publications

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“…Fuzzification methods have been used in many studies (Feng 1995;Deng 1999;Mikhailov, Tsvetinov 2004;Erensal et al 2006;Wang et al 2008). In this study, they were used for the clustering of factors affecting forest fires (important factors that have been identified by the Apriori algorithm).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Jafarzadeh¹,

Mahdavi²,

Jafarzadeh³

2017

J. For. Sci.

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Jafarzadeh A.A., Mahdavi A., Jafarzadeh H. (2017): Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. J. For. Sci., In this study we evaluated forest fire risk in the west of Iran using the Apriori algorithm and fuzzy c-means (FCM) clustering. We used twelve different input parameters to model fire risk in Ilam Province. Our results with minimum support and minimum confidence show strong relationships between wildfire occurrence and eight variables (distance from settlement, population density, distance from road, slope, standing dead oak trees, temperature, land cover and distance from farm land). In this study, we defined three clusters for each variable: low, middle and high. The data regarding the factors affecting forest fire risk were distributed in these three clusters with different degrees of membership and the final map of all factors was classified by FCM clustering. Each layer was then created in a geographic information system. Finally, wildfire risks in the area obtained from overlaying these layers were classified into five categories, from very low to very high according to the degree of danger.

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Section: Methodsmentioning

confidence: 99%

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Jafarzadeh¹,

Mahdavi²,

Jafarzadeh³

2017

J. For. Sci.

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“…Many classical and extended decision rules designed for pure or mixed MDMU (multi-criteria decision-making under uncertainty) have been already developed, e.g. [Aghdaie et al, 2013;Ben Amor et al, 2007;Dominiak, 2006;Durbach, 2014;Eiselt, Marianov, 2014;GasparsWieloch, 2014d;2015c;2015d;2017;Ginevičius, Zubrecovas, 2009;Goodwin, Wright, 2001;Hopfe et al, 2013;Janjic et al, 2013;Korhonen, 2001;Lee, 2012;Liu et al, 2011;Lo, Michnik, 2010;Michnik, 2013;Mikhaidov, Tsvetinov, 2004;Montibeller et al, 2006;Ram et al, 2010;Ramik et al, 2008;Ravindran, 2008;Silva, 2016;Stewart, 2005;Suo et al, 2012;Troutt, Pettypool, 1989;Tsaur et al, 2002;Urli, Nadeau, 2004;Wojewnik, Szapiro, 2010;Xu, 2000;Yu, 2002]. Some of them can be applied when the DM intends to perform the selected alternative only once (one-shot decisions).…”

Section: How To Combine Criteria With Scenarios In Uncertain Multi-crmentioning

confidence: 99%

A Decision Rule for Uncertain Multi-Criteria Pure Decision Making and Independent Criteria

Gaspars-Wieloch

2017

Optimum

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SummaryThe paper is concerned with multi-criteria decision-making under uncertainty with scenario planning. This topic has been explored by many researchers since almost all real-world decision problems contain multiple conflicting criteria and a deterministic evaluation of criteria is often impossible. We propose a procedure for uncertain multi-objective optimization which can be applied when seeking a pure strategy. A pure strategy, as opposed to a mixed strategy, allows the decision-maker to select and perform only one accessible alternative. The new approach takes into account the decision-maker's preference structure (importance of particular goals) and nature (pessimistic, moderate or optimistic attitude towards a given problem). It is designed for one-shot decisions made under uncertainty with unknown probabilities (frequencies), see decision-making under complete uncertainty or decision-making under strategic uncertainty. The novel approach can be used in the case of totally independent payoff matrices for particular targets.

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“…When a new product is being developed, it is not normally possible to elicit explicit data because of the implicit nature of early-stage product conceptualisation (Yan et al 2006). Since Zadeh (1965) introduced fuzzy set theory, and Bellman and Zadeh (1970) described the decision-making method in fuzzy environments, an increasing number of manufacturing studies have dealt with fuzzy-logic-based decisionmaking models for new product development (Bu¨yu¨-ko¨zkan and Feyzio glu 2004, Mikhailov and Tsvetinov 2004, Feyzio glu and Bu¨yu¨ko¨zkan 2008, Zhang and Chu 2009, Chiang and Che 2010 and cooperative games where the knowledge about the worth of coalitions is described by fuzzy intervals (Nishizaki and Sakawa 2000, Mares 2001, Tsurumi et al 2001, Espin et al 2007, Al-Ahmari 2008, Jing and Lu 2010, Mallozzi et al 2011. According to Zadeh (1975), it is very difficult for conventional quantification to reasonably express complex situations and it is necessary to use linguistic variables whose values are words or sentences in a natural or artificial language.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy e-negotiation support system for inter-firm collaborative product development

Zandi

Tavana

2012

International Journal of Computer Integrated Manufacturing

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Significant advances in manufacturing technology and the rapid intensification of the Internet and electronic commerce diffusion have given rise to competitive differentiation and rapid adaptability to competitive change. New product development is a complex and collaborative process that requires negotiation and joint decisionmaking. We propose a fuzzy electronic negotiation (e-negotiation) support system based on cooperative multicriteria game theory. The proposed system comprises three major processes: initialisation, e-negotiation and joint agreement. The Internet is used to facilitate the e-negotiation process and to minimise the response time in the decision-making process. The fuzzy sets are used to overcome issues related to the imprecise or vague judgments and incomplete information in the negotiation process. The proposed system enables the manufacturing parties involved in the negotiation process to determine the optimal coalition form for new product development and choose a common strategy to improve the payoffs of the members of the coalition group. Finally, a case study is used to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms in the mobile telecommunications industry.

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Evaluation of services using a fuzzy analytic hierarchy process

Cited by 366 publications

References 23 publications

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering

A Decision Rule for Uncertain Multi-Criteria Pure Decision Making and Independent Criteria

A fuzzy e-negotiation support system for inter-firm collaborative product development

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