Analytic Hierarchy Process–Simulation Framework for Lighting Maintenance Decision-Making Based on the Clustered Network

2018 ◽  
Vol 32 (1) ◽  
pp. 04017114 ◽  
Author(s):  
Yuan Chen ◽  
Ahmed Bouferguene ◽  
Mohamed Al-Hussein
Author(s):  
G. Marimuthu ◽  
G. Ramesh

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.


2013 ◽  
Vol 807-809 ◽  
pp. 1881-1885
Author(s):  
Chun Mei Zhang ◽  
Min Zhao ◽  
Xue Lv

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.


Author(s):  
LONG-TING WU ◽  
XIA CUI ◽  
RU-WEI DAI

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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