Enhancing Technical Proposal Evaluation in Consultant Selection in Department of Water Resources and Irrigation: A Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

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Enhancing Technical Proposal Evaluation in Consultant Selection in Department of Water Resources and Irrigation: A Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Student: Nischal Silwal

Supervisor: Er. Subash Kumar Bhattarai

Submitted Date: April, 2024

Abstract

The complexity, costs and uncertainties inherent in today's construction projects underscore the critical role of consultant selection in project success. While various methods exist, Quality and Cost Based Selection (QCBS) is widely recognized for its comprehensive approach, considering both technical criteria and cost. However, evaluating technical proposals within QCBS can be subjective, despite established criteria. This study focuses on addressing this issue by identifying common criteria and sub-criteria for consultant selection within the Department of Water Resources and Irrigation. Utilizing the Fuzzy Analytic Hierarchy Process (AHP), the study aims to determine the relative importance of these criteria and sub-criteria. By integrating Fuzzy AHP and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS), the study aims to effectively rank consultants. The use of Fuzzy AHP allows for the determination of independent weights for criteria and sub-criteria, accommodating the inherent vagueness and imprecision of qualitative factors. Subsequently, Fuzzy TOPSIS is employed to rank consultant alternatives based on their Closeness Coefficients, providing a comprehensive evaluation framework. Through this hybrid methodology, the study seeks to enhance the consultant selection process, ensuring that chosen consultants align with project needs and standards. By mitigating subjective decision-making uncertainties, the study aims to contribute to improved project outcomes in the Department of Water Resources and Irrigation's construction projects.

Keywords

Consultant Selection, Criteria, Sub-criteria, Weightage Fuzzy AHP, Fuzzy TOPSIS, Closeness Coefficient

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