scholarly journals An Integrated Approach of Analytic Hierarchy Process and Triangular Fuzzy Sets for Analyzing the Park-and-Ride Facility Location Problem

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1225 ◽  
Author(s):  
Jairo Ortega ◽  
János Tóth ◽  
Sarbast Moslem ◽  
Tamás Péter ◽  
Szabolcs Duleba
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Sets ◽  
Facility Location ◽  
Public Transport ◽  
Location Problem ◽  
Facility Location Problem ◽  
Analytic Hierarchy ◽  
Park And Ride ◽  
Hierarchy Process

A park and ride (P&R) system is a set of facilities where private vehicle users can transfer to public transport to complete their journey. The main advantage of the system is reducing the congestions problem in the central business district (CBD). Thus, the notion of symmetry is particularly important in multi-criteria decision aid (MCDA) because they are basic characteristics of the binary relationships used in modelling the preferences of decision-makers. The focal point of this study is evaluating the P&R facility system location problem from the experts’ point of view. For this aim, an integrated multicriteria decision-making (MCDM) methodology is proposed to evaluate the location of the facilities of the P&R system. The questionnaire survey was designed and estimated by 10 transport experts in the related field. The famous analytic hierarchy process (AHP) was adopted in a fuzzy environment, where the fuzzy sets have an efficient ability to manage the vague concepts in a specific way; moreover, it can mitigate the evaluator reasoning during decision-making. The hierarchical structure of the problem was established to evaluate a real-life problem in Cuenca city, Ecuador. The outcomes highlighted the “accessibility of public transport” as the most significant issue in the P&R facility location problem. The obtained results provide more flexible facilities than the pure AHP method.

A Fuzzy Weights Representation for Inner Dependence AHP

2011 ◽  
Vol 15 (3) ◽  
pp. 329-335 ◽  
Author(s):  
Shin-ichi Ohnishi ◽  
◽  
Takahiro Yamanoi ◽  
Hideyuki Imai ◽  
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Analytic Hierarchy Process ◽  
Fuzzy Sets ◽  
Analytic Hierarchy ◽  
Comparison Matrix ◽  
Fuzzy Representation ◽  
Individual Criterion ◽  
Fuzzy Weights ◽  
Hierarchy Process

The Analytic Hierarchy Process (AHP) proposed by T. L. Saaty has been widely used in decision making. Inner dependence method AHP is used for cases in which criteria are not independent enough. Using the original AHP or inner dependence AHP may cause results to lose reliability because the comparison matrix is not necessarily sufficiently consistent. In such cases, fuzzy representation for weighting criteria using results from sensitivity analysis is useful. We present weights of normal AHP criteria via fuzzy sets, then calculate modified fuzzy weights of inner dependence methods. We also get overall weights of alternatives based on certain assumptions. Results show the fuzziness of inner dependence AHP if the comparison matrix is not sufficiently consistent and individual criterion do not have enough independence.

On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria

2016 ◽  
Vol 2 (3) ◽  
pp. 54
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Decision Models ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Method ◽  
Analytic Hierarchy ◽  
Comparison Model ◽  
Hierarchy Process

Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10].  The weight vectors from this comparison model are obtained by using extent analysis method.  This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

Study of Influence of Road Construction on Grassland in Inner Mongolia

2013 ◽  
Vol 807-809 ◽  
pp. 1881-1885
Author(s):  
Chun Mei Zhang ◽  
Min Zhao ◽  
Xue Lv
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Analytic Hierarchy Process ◽  
Inner Mongolia ◽  
Road Construction ◽  
Environment Protection ◽  
Analytic Hierarchy ◽  
The Sustainable Development ◽  
Inner Mongolia Grassland ◽  
Hierarchy Process

In this paper, the indexes that are used to assess the influence of road construction on Inner Mongolia grassland have been proposed based on the environment protection perspective. The Analytic hierarchy process was employed to evaluate the importance of different indexes regarding to influence. These indexes would be used to provide information for decision making about road construction in order to achieve the sustainable development of grassland.

JUDGMENT NUMBER REDUCTION: AN ISSUE IN THE ANALYTIC HIERARCHY PROCESS

2010 ◽  
Vol 09 (01) ◽  
pp. 175-189 ◽  
Author(s):  
LONG-TING WU ◽  
XIA CUI ◽  
RU-WEI DAI
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Pairwise Comparison ◽  
Random Variable ◽  
Analytic Hierarchy ◽  
Priority Vector ◽  
Judgment Error ◽  
Scale Error ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

The Analytic Hierarchy Process (AHP) uses pairwise comparison to evaluate alternatives' advantages to a certain criterion. For decision-making problem with many different criteria and alternatives, pairwise comparison causes a prolonged decision-making period and rises fatigue in decision-makers' mentality. A question of practical value is if there exists a way to reduce judgment number and what influence the reduction will have on the overall evaluation of alternative ratings. To answer this question, we introduce scale error and judgment error into AHP judgment matrix. By expanding the scales defined in the AHP, scale error is eliminated. Taking judgment error as random variable, a new estimator to calculate priority vector is presented. In the end, an example is proved to show lowering judgment number will increase the probability of larger errors appearing in priority vector computation.

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