Example of Journal of Multi-Criteria Decision Analysis format
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Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format
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Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format Example of Journal of Multi-Criteria Decision Analysis format
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Journal of Multi-Criteria Decision Analysis — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Strategy and Management #140 of 440 up up by 8 ranks
Decision Sciences (all) #19 of 37 down down by 2 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 81 Published Papers | 274 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/06/2020
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Quality:  
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Quality:  
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CiteRatio: 3.9
SJR: 1.136
SNIP: 1.221

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

3.4

26% from 2019

CiteRatio for Journal of Multi-Criteria Decision Analysis from 2016 - 2020
Year Value
2020 3.4
2019 2.7
2018 2.2
2017 2.0
2016 2.5
graph view Graph view
table view Table view

0.462

9% from 2019

SJR for Journal of Multi-Criteria Decision Analysis from 2016 - 2020
Year Value
2020 0.462
2019 0.508
2018 0.492
2017 0.35
2016 0.928
graph view Graph view
table view Table view

1.026

25% from 2019

SNIP for Journal of Multi-Criteria Decision Analysis from 2016 - 2020
Year Value
2020 1.026
2019 1.374
2018 0.825
2017 0.572
2016 0.947
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 26% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 9% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 25% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Multi-Criteria Decision Analysis

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Wiley

Journal of Multi-Criteria Decision Analysis

The Journal of Multi-Criteria Decision Analysis was launched in 1992, and from the outset has aimed to be the repository of choice for papers covering all aspects of MCDA/MCDM. The journal provides an international forum for the presentation and discussion of all aspects of re...... Read More

Strategy and Management

General Decision Sciences

Business, Management and Accounting

i
Last updated on
19 Jun 2020
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ISSN
1099-1360
i
Acceptance Rate
Not Provided
i
Frequency
Not Provided
i
Open Access
Not Provided
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Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
i
Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization
Piotr Czyzżak1, Adrezej Jaszkiewicz1

Abstract:

This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating so... This paper presents a multiple-objective metaheuristic procedure—Pareto simulated annealing. The goal of the procedure is to find in a relatively short time a good approximation of the set of efficient solutions of a multiple-objective combinatorial optimization problem. The procedure uses a sample, of so-called generating solutions. Each solution explores its neighbourhood in a way similar to that of classical simulated annealing. Weights of the objectives, used for their local aggregation, are tuned in each iteration in order to assure a tendency for approaching the efficient solutions set while maintaining a uniform distribution of the generating solutions over this set. A computational experiment shows that the method is a better tool for approximating the efficient set than some previous proposals. © 1998 John Wiley & Sons, Ltd. read more read less

Topics:

Metaheuristic (63%)63% related to the paper, Simulated annealing (62%)62% related to the paper, Parallel metaheuristic (62%)62% related to the paper, Combinatorial optimization (61%)61% related to the paper, Optimization problem (59%)59% related to the paper
749 Citations
On the measurement of preferences in the analytic hierarchy process
Ahti Salo1, Raimo P. Hämäläinen1

Abstract:

In this paper we apply multiattribute value theory as a framework for examining the use of pairwise comparisons in the analytic hierarchy process (AHP). On one hand our analysis indicates that pairwise comparisons should be understood in terms of preference differences between pairs of alternatives. On the other hand it point... In this paper we apply multiattribute value theory as a framework for examining the use of pairwise comparisons in the analytic hierarchy process (AHP). On one hand our analysis indicates that pairwise comparisons should be understood in terms of preference differences between pairs of alternatives. On the other hand it points out undesirable effects caused by the upper bound and the discretization of any given ratio scale. Both these observations apply equally well to the SMART procedure which also uses estimates of weight ratios. Furthermore, we demonstrate that the AHP can be modified so as to produce results similar to those of multiattribute value measurement; we also propose new balanced scales to improve the sensitivity of the AHP ratio scales. Finally we show that the so-called supermatrix technique does not eliminate the rank reversal phenomenon which can be attributed to the normalizations in the AHP. © 1997 John Wiley & Sons, Ltd. read more read less

Topics:

Rank reversals in decision-making (55%)55% related to the paper, Pairwise comparison (54%)54% related to the paper, Analytic hierarchy process (53%)53% related to the paper
404 Citations
MOSA method: a tool for solving multiobjective combinatorial optimization problems
E. L. Ulungu1, Jacques Teghem1, Philippe Fortemps1, Daniel Tuyttens1

Abstract:

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large-scale problems, the simultaneous difficulties of -hard complexity and ... The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large-scale problems, the simultaneous difficulties of -hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods. This paper develops the so-called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi-objective knapsack problems. Copyright © 1999 John Wiley & Sons, Ltd. read more read less

Topics:

Combinatorial optimization (65%)65% related to the paper, Optimization problem (65%)65% related to the paper, Tabu search (61%)61% related to the paper, Knapsack problem (60%)60% related to the paper, Simulated annealing (56%)56% related to the paper
382 Citations
Journal Article DOI: 10.1002/MCDA.4020030204
Multi‐objective combinatorial optimization problems: A survey
E. L. Ulungu1, Jacques Teghem1

Abstract:

In the last 20 years many multi-objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real-world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply ... In the last 20 years many multi-objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real-world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi-objective programming methods. The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi-criteria framework. Some conclusions are drawn and directions for future research are suggested. read more read less

Topics:

Optimization problem (65%)65% related to the paper, Combinatorial optimization (63%)63% related to the paper, Stochastic programming (60%)60% related to the paper, Quadratic assignment problem (57%)57% related to the paper, Covering problems (55%)55% related to the paper
320 Citations
Journal Article DOI: 10.1002/MCDA.1482
An Overview of ELECTRE Methods and their Recent Extensions
José Rui Figueira1, Salvatore Greco2, Bernard Roy3, Roman Słowiński4, Roman Słowiński5

Abstract:

We present main characteristics of ELECTRE (ELimination Et Choix Traduisant la REalite - ELimination and Choice Expressing the REality) family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation on the set of actions—it is constructed in result of concordance... We present main characteristics of ELECTRE (ELimination Et Choix Traduisant la REalite - ELimination and Choice Expressing the REality) family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation on the set of actions—it is constructed in result of concordance and nondiscordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the ELECTRE methods are inserted, we present the main features of these methods. We discuss such characteristic features as the possibility of taking into account positive and negative reasons in the modelling of preferences, without requiring commensurable performance scales; the use of discriminating thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between ‘gains’ and ‘losses’. The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools and several other aspects. This paper is an updated version of a chapter published by the authors under the title ‘ Electre Methods: Main Features and Recent Developments’ in C. Zopounidis and P. Pardalos (Editors): Handbook of Multicriteria Analysis, Springer, Berlin 2010, pp. 51–89. Copyright © 2012 John Wiley & Sons, Ltd. read more read less

Topics:

ELECTRE (69%)69% related to the paper
300 Citations
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13. What is Sherpa RoMEO Archiving Policy for Journal of Multi-Criteria Decision Analysis?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Multi-Criteria Decision Analysis. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Multi-Criteria Decision Analysis?

The 5 most common citation types in order of usage for Journal of Multi-Criteria Decision Analysis are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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