Evaluation of barriers and solutions to renewable energy acceleration in Malawi, Africa, using AHP and fuzzy TOPSIS approach

Chisale, Sylvester William; Lee, Han Soo

doi:10.1016/j.esd.2023.101272

Cited by 19 publications

(10 citation statements)

References 37 publications

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“…However, a significant limitation of the AHP method is its inability to handle the uncertainties or vaguenesses typical in group decision-making processes. To overcome these challenges, the integration of fuzzy logic with AHP, referred to as FAHP, has been recommended S4 [43]. This combination provides a more accurate tool for assessing the issues and incorporating ambiguous or incomplete data.…”

Section: 𝐹 = 𝑚𝑖𝑛{𝑙} (∏ {𝑚}) Max {𝑢}mentioning

confidence: 99%

Artificial Intelligence Integrated Energy Education Framework. A Holistic Approach

Stecyk,

Miciuła

2024

Preprint

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This scientific article outlines the Artificial Intelligence Integrated Energy Education Framework (AI-IEEF), a transformative model designed to revolutionize energy education and management practices. The framework is organized into five distinct layers: Organizational (AI-enhanced administration systems), Financial (dynamic AI financial models), Technology (advanced simulation and modeling), Methodology (AI in curriculum development and personalized learning), and Social (AI for community engagement and impact). An expert panel used the fuzzy Delphi method to achieve consensus on twenty key factors within these layers, establishing a solid foundation for analysis. Following this, the Fuzzy Analytic Hierarchy Process (AHP) was employed to calculate precise weights for each layer and their respective factors, providing a quantitative assessment of their relative importance. These weight calculations are crucial, as they guide resource allocation and strategic decision-making to ensure the framework is optimized for the evolving needs of energy management. Furthermore, the article introduces five tailored variants of the AI-IEEF, each addressing specific aspects of energy education and offering a comprehensive approach to navigating the challenges in the field. The five variants include the Smart Campus Energy Education, Global Energy Policy Analysis, Renewable Energy Research and Development, Energy Workforce Development, and Community Engagement and Outreach variants. Each provides a distinct approach to energy education, from transforming campuses into living labs for hands-on learning to fostering international collaborations that explore the global implications of energy policy. These variants emphasize practical skills, policy analysis, and community-focused solutions, ensuring that students are well-prepared to contribute effectively to the energy sector.

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Section: 𝐹 = 𝑚𝑖𝑛{𝑙} (∏ {𝑚}) Max {𝑢}mentioning

confidence: 99%

Artificial Intelligence Integrated Energy Education Framework. A Holistic Approach

Stecyk,

Miciuła

2024

Preprint

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“…Awodi et al [40] present a fuzzy TOPSIS-based risk assessment model for nuclear decommissioning, contributing to effective risk management in the intricate field of nu-clear energy. Meanwhile, Chisale and Lee [41] address renewable energy challenges in Malawi, utilizing the AHP and fuzzy TOPSIS to evaluate barriers and propose solutions.…”

Section: Uncertain Data Modelingmentioning

confidence: 99%

Linear Programming-Based Fuzzy Alternative Ranking Order Method Accounting for Two-Step Normalization for Comprehensive Evaluation of Digital Economy Development in Provincial Regions

Xiang,

Farid,

Riaz

2024

Axioms

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As digital technologies continue to reshape economic landscapes, the comprehensive evaluation of digital economy (DE) development in provincial regions becomes a critical endeavor. This article proposes a novel approach, integrating the linear programming method, fuzzy logic, and the alternative ranking order method accounting for two-step normalization (AROMAN), to assess the multifaceted facets of DE growth. The primary contribution of the AROMAN is the coupling of vector and linear normalization techniques in order to produce accurate data structures that are subsequently utilized in calculations. The proposed methodology accommodates the inherent uncertainties and complexities associated with the evaluation process, offering a robust framework for decision-makers. The linear programming aspect optimizes the weightings assigned to different evaluation criteria, ensuring a dynamic and context-specific assessment. By incorporating fuzzy logic, the model captures the vagueness and imprecision inherent in qualitative assessments, providing a more realistic representation of the DE’s multifaceted nature. The AROMAN further refines the ranking process, considering the interdependencies among the criteria and enhancing the accuracy of the evaluation. In order to ascertain the efficacy of the suggested methodology, a case study is undertaken pertaining to provincial areas, showcasing its implementation in the evaluation and a comparison of DE progress in various geographical settings. The outcomes illustrate the capacity of the model to produce perceptive and implementable insights for policymakers, thereby enabling them to make well-informed decisions and implement focused interventions that promote the expansion of the DE. Moreover, managerial implications, theoretical limitations, and a comparative analysis are also given of the proposed method.

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“…Fuzzy TOPSIS merupakan variasi dari metode TOPSIS yang diterapkan dalam konteks MCDM. Pendekatan metode F-TOPSIS menerapkan konsep himpunan fuzzy untuk menangani ketidakpastian dan ambiguitas dalam proses pengambilan keputusan [16]. Dibawah ini dijelaskan tahapantahapan penerapan F-TOPSIS.…”

Section: Metode Fuzzy Topsis (F-topsis)unclassified

Penerapan Metode F-AHP dan F-TOPSIS Dalam Proses Seleksi Karyawan Untuk Bidang Teknologi Informasi

Murnawan,

Lestari,

Samihardjo

2024

teknika

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Penelitian ini mengeksplorasi proses seleksi karyawan di departemen Teknologi Informasi (TI) sebuah perusahaan tekstil di Bandung, Indonesia. Fokusnya adalah pada pengambilan keputusan berbasis kelompok menggunakan metode multi criteria decision making (MCDM) dengan pendekatan fuzzy. Tantangan dalam mengevaluasi calon karyawan diatasi melalui integrasi metode Fuzzy Analytical Hierarchy Process (F-AHP) dan Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). Tiga pengambil keputusan dilibatkan dalam menentukan kriteria dan sub-kriteria melalui wawancara dan kuesioner. Kriteria utama terdiri dari kualifikasi umum (KU), kualifikasi individu (KI), dan kualifikasi teknis (KT), dengan 20 sub-kriteria yang mendukung. F-AHP digunakan untuk menetapkan bobot, kemudian F-TOPSIS digunakan untuk merangking lima calon karyawan potensial (CD1 hingga CD5). Hasil penilaian menunjukkan bahwa CD3 merupakan pilihan terbaik dengan kinerja tertinggi dalam KT, KU, dan KI. CD1 dan CD2 memiliki keunggulan masing-masing, sedangkan CD4 dan CD5 menunjukkan kelemahan. Rekomendasi untuk merekrut CD3 disertai dengan CD1 dan CD2 sebagai opsi wawancara tambahan. Kontribusi utama penelitian ini adalah penerapan metode F-AHP dan F-TOPSIS, memberikan pendekatan sistematis dan obyektif dalam seleksi karyawan. Penelitian ini tidak hanya memberikan landasan kuat untuk pengambilan keputusan rekrutmen, tetapi juga solusi praktis untuk memilih karyawan sesuai dengan kebutuhan perusahaan, meningkatkan efektivitas dan efisiensi proses seleksi di era bisnis yang terus berkembang.

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Evaluation of barriers and solutions to renewable energy acceleration in Malawi, Africa, using AHP and fuzzy TOPSIS approach

Cited by 19 publications

References 37 publications

Artificial Intelligence Integrated Energy Education Framework. A Holistic Approach

Artificial Intelligence Integrated Energy Education Framework. A Holistic Approach

Linear Programming-Based Fuzzy Alternative Ranking Order Method Accounting for Two-Step Normalization for Comprehensive Evaluation of Digital Economy Development in Provincial Regions

Penerapan Metode F-AHP dan F-TOPSIS Dalam Proses Seleksi Karyawan Untuk Bidang Teknologi Informasi

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