تحلیل اثرگذاری رویکردهای ذهنی و عینی در وزن‌دهی معیارها در چارچوب تصمیم‌گیری چندمعیاره

Abstract
Objective: The present study aims to compare the results of prioritizing strategies for mitigating the adverse impacts arising from the construction and operation of the Marun Dam and Hydropower Plant, based on the application of different weighting methods for a set of criteria and sub-criteria.
Method: To this end, the environmental impacts of the Marun Dam were first examined using field observations and previous studies. Subsequently, various alternatives for reducing the dam’s environmental impacts, along with the evaluation criteria and sub-criteria, were proposed by a panel of experts. Two weighting approaches—namely the Analytic Hierarchy Process (AHP) and Shannon’s Entropy method—were then employed to calculate the criteria weights. Next, the strategies for mitigating the adverse effects of the Maroon Dam were prioritized using the Multi-Attributive Border Approximation area Comparison (MABAC) method. This prioritization was carried out twice: once with the criteria weights derived from AHP, and again with those obtained from Shannon’s Entropy method.
Results: The results indicate that under AHP, the sub-criterion “impact on water quality” holds the highest weight (0.37), whereas under Shannon’s Entropy method, the sub-criterion “operation and maintenance costs” ranks first with a weight of 0.22. Moreover, the MABAC analysis reveals that the prioritization of alternatives remains consistent across both weighting methods. Specifically, “Alternative 1—monitoring and controlling water quality” achieved the highest rank in both cases, with scores of 0.18 (AHP) and 0.31 (Entropy).
Conclusion: The findings of this study highlight the importance of selecting an appropriate weighting method to identify the most critical criterion, underscoring its impact on the final prioritization outcomes. This is particularly important for long-term policymaking and sustainable resource management.
 
 
 
English Extended Abstract
Introduction
Prioritizing strategic approaches to mitigate the adverse impacts associated with the construction and operation of civil infrastructure—particularly in water resources management projects—represents a multifaceted and demanding challenge. This complexity arises from the involvement of diverse stakeholders, each with distinct objectives, preferences, and evaluative criteria.
Consequently, the selection of appropriate methodologies for assigning weights to decision-making criteria and sub-criteria, as well as for ranking alternative solutions in accordance with the specific contextual constraints of the problem, plays a pivotal role in ensuring the robustness and credibility of the final decision-making process. Employing systematic, transparent, and context-sensitive prioritization frameworks is therefore essential to reconcile competing interests and to enhance the sustainability and effectiveness of such projects.
 
Method
The present study is designed to perform a comparative evaluation of strategic prioritization approaches aimed at minimizing the environmental consequences resulting from the construction and operation of the Marun Dam and its associated hydropower plant. Given the scale and complexity of such infrastructure projects, particularly in ecologically sensitive regions, the need for systematic and evidence-based decision-making frameworks becomes paramount. To this end, the environmental impacts of the Marun Dam were first identified and analyzed through a combination of field observations and a comprehensive review of prior research and technical reports.
Following this assessment, a panel of interdisciplinary experts—including specialists in hydrology, environmental engineering, and water resource management—collaboratively proposed a set of feasible mitigation strategies. These alternatives were evaluated against a structured hierarchy of criteria and sub-criteria, reflecting ecological, socio-economic, and technical dimensions relevant to the project context.
To quantify the relative importance of each criterion, two distinct weighting methodologies were employed: the Analytic Hierarchy Process (AHP), which incorporates expert judgment through pairwise comparisons, and the Entropy-Shannon method, which derives weights based on the inherent information content and variability of the data. Subsequently, the Multi-Attribute Approximate Border Area Comparison (MABAC) method—a robust multi-criteria decision-making (MCDM) technique—was applied to rank the proposed strategies under each weighting scheme.
By comparing the prioritization outcomes derived from the AHP-based and Entropy-based weighting approaches, this study provides critical insights into how the choice of weighting method can influence strategic decision-making in environmental management. The findings contribute to the development of more transparent, adaptable, and context-sensitive frameworks for prioritizing mitigation strategies in large-scale water infrastructure projects.
 
Results
The results of the present study show that in the AHP method, the sub-criterion of the impact on water quality with a weight of 0.37 has the maximum weight, while in the Entropy-Shannon method, the sub-criterion of maintenance and operation costs with a weight of 0.22 is in the first place.
Also, the results of the MABAC multi-criteria decision-making method show that the prioritization of alternatives based on the input weights obtained from the AHP and Entropy-Shannon methods is the same, so that the first alternative, including monitoring and controlling water quality based on the weights of the criteria, was ranked first in both cases with a score of 0.18 and 0.31, respectively.
 
Conclusion
The results of the present study show the importance of determining the weighting method to select the most important criterion according to the nature of the problem and its impact on the final results of ranking the alternatives, especially for long-term policy-making.
For this reason, the selection of the appropriate method should be made according to the nature of the data and the preferences of the decision-makers in order to increase the accuracy and reliability of the results.