A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process

Ravi Gorripati; Mainak Thakur; Nagesh Kolagani

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A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process

机译：A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process

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A resource management system is likely to succeed if stakeholders get involved in analyzing and choosing from the alternatives. The present work deals with multi-criteria decision models to evaluate rain water harvesting (RWH) structures. Standard practice is to acquire the weights for criteria from stakeholders using analytic hierarchy process (AHP) to predict the RWH structures' performance and rank them. Challenges in this process are that the data collection is laborious and time-consuming, considers limited stakeholders' opinions, and suffers from lower confidence factors. This work proposes a probabilistic approach to AHP using Monte Carlo simulation (MCS) to model uncertainty. The proposal is to collect multiple assessments instead of a single judgment from knowledgeable stakeholders (KSH) with customized questionnaires and to compute the relative importance of criteria using pairwise comparisons. Stochastically similar assessments within the range of these samples are then generated using different distribution functions to compute the performance of the RWH structures. The computed performance correlated well with common stakeholders' (CSH) opinions in the case study involving 10 existing RWH structures with seven different criteria, for all the distributions. The mean relative error with the proposed method is approximately 21 less than the existing point estimate method.

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来源
《Water policy》 |2022年第6期|878-898|共21页
作者
Ravi Gorripati; Mainak Thakur; Nagesh Kolagani;
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作者单位

Indian Institute of Information Technology Sri City, Chittoor, Andhra Pradesh, India;

Centurion University of Technology and Management, Odisha, India;

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正文语种英语
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Analytic hierarchy process (AHP); Monte Carlo simulation (MCS); Multi-criteria decision modeling (MCDA); Participatory modeling (PM);

A framework for optimal rank identification of resource management systems using probabilistic approaches in analytic hierarchy process

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