Multi-criteria decision making in electrical discharge machining with nickel coated aluminium electrode for titanium alloy using preferential selection index

Huu, Phan Nguyen; Vu, Ngo Ngoc; Shirguppikar, Shailesh; Ly, Nguyen Trong; Tam, Nguyen Chi; Tai, Bui Tien; Thanh, Le Thi Phuong

doi:10.1051/mfreview/2022010

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…Preference Selection Index can be used in determining the relative relationship between attributes if there is a conflict of interest between all attributes [18,19]. The stages of solving multi-criteria decision support problems using the Preference Selection Index, namely [8,9,11]: 1. Determination of the Decision Matrix…”

Section: Preference Selection Index Methodsmentioning

confidence: 99%

“…Preference Selection Index is a method that can be used to solve multi-criteria decision-making problems [7][8][9]. Previous research by Depa Supitri, et al [10], Nguyen Huu Phan, et al [11], Aditya Alfiansyah Saragih, et al [12], Usanto S [13], and Agung Sutrisno & Vikas Kumar [14], concluded that the Preference Selection Index method can provide solutions as support in solving multi-criteria decision-making problems objectively through alternative ranking results.…”

Section: Introductionmentioning

confidence: 99%

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Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Marbun,

Pozilova,

Shahadi

et al. 2024

SAGA

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Search Engine Optimization Specialist is one of the important factors that can help digital agencies improve services to all their customers. This research aims to develop a decision-making system in the recruitment process of Search Engine Optimization Specialists by applying the Preference Selection Index method. The Search Engine Optimization Specialist recruitment decision-making system in this study uses 5 (five) criteria, namely educational background, ability to use SEO tools, work experience as a Search Engine Optimization Specialist, digital marketing skills, and age. The research sample data collection technique related to the recruitment of Search Engine Optimization Specialists used in this research is Literature Study. After the author succeeds in collecting research sample data, at the next stage the author analyzes the application of the Preference Selection Index method in the recruitment of Search Engine Optimization Specialists. The results of this study show that alternative A02 (0.855641604) has the highest value and gets the first ranking position. Meanwhile, the alternatives that get the second to fifth ranking positions are A05 (0,834620739), A03 (0,741086734), A04 (0,716537597), and A01 (0,708920064). So that the most recommended alternative based on the calculation of the Preference Selection Index on the recruitment of Search Engine Optimization Specialists in this study is alternative A02 (0,855641604).

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Section: Preference Selection Index Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Marbun,

Pozilova,

Shahadi

et al. 2024

SAGA

View full text Add to dashboard Cite

show abstract

“…In the literature, a number of research studies investigated the PSI method in many MCDM problems It has been used to assess machine performance (Bilgin Sarı, 2019), propose a waste recovery method for electrical/electronic products (Sari, 2020), select an automated system development method for scholarship recipients (Arifin and Saputro, 2022), make decisions regarding tooth restoration/beautification materials (Yadav, 2022), determine life cycle design solutions for product systems (Attri and Grover, 2015), choose technological parameters for turning (Vara Prasad et al, 2018), select parameters for electrical discharge machining (Phan et al, 2022), determine technological parameters for the grinding process (Hoang Tien et al, 2021) and rank types of materials for engineering (Maniya and Bhatt, 2010). Thus, The PSI method has been used well for making complex choices in a lot of different areas.…”

Section: Preference Selection Indexmentioning

confidence: 99%

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Alrababah,

Alsrehin

2024

Journal of Computer Science

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Decision-making methodologies can differentiate between several types of criterion weights. The subjective weights of decision-makers are prone to be influenced by various factors, including their level of knowledge, experience and competency. This may result in the wrong evaluation of the criteria due to the inherent ambiguity of human judgments, leading to unavoidable assessment errors. Beyond that, while assessing the decision alternatives, the majority of Multiple Criteria Decision Making (MCDM) take the evaluation criteria into consideration separately. However, in actual application, most of the criteria are not mutually exclusive. In the context of online customer reviews, it is essential to prioritize product aspects in order to facilitate the purchasing process for potential consumers. Selecting the appropriate product aspects is a difficult task due to the vast quantity of product reviews. This research develops an MCDM solution through the integration of the Preference Selection Index (PSI) with The approach for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for decision-making. The contribution of this study is to enhance the TOPSIS ranking technique by incorporating PSI objective weights as an alternative to subjective weights. PSI offers the benefit of focusing on the convergence of the criteria involved rather than their divergence. This approach will improve the ranking process of TOPSIS by taking into account the interconnectedness of the criteria, hence facilitating the prioritization of significant aspects of a product based on online reviews. A dataset comprising four electronic products was utilized as a reference for conducting a statistical analysis. Through the examination of the outcomes utilizing the discount cumulative gain metric, it becomes apparent that the combination of the TOPSIS approach alongside PSI weights facilitates the identification of the suitable product aspects that effectively differentiate the one that aligns with consumer expectations.

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“…MCDM is a systematic approach used to handle complex decision-making processes by considering several criteria or factors simultaneously. The main purpose of MCDM is to assist decision makers in evaluating decision alternatives and selecting optimal solutions based on predefined criteria [8]. The MCDM method involves mathematical and statistical analysis to measure the relative value of each alternative, which is then used to rank or select the best solution.…”

Section: Introductionmentioning

confidence: 99%

MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations

Setiawansyah,

Sintaro,

Aldino

2023

jieee

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Customer loan eligibility recommendations are an important step in the risk and financial assessment process. To ensure business continuity and customer satisfaction, it is important for financial institutions to consider several key factors. Problems in determining lending include various aspects that include credit risk that is difficult to assess appropriately, insufficient information about potential borrowers, potential fraud that can harm lenders. Regardless, financial institutions must ensure sound risk management and comply with applicable regulations and compliance standards to minimize potential losses and maintain fairness in decision-making related to lending. Cooperatives act as institutions that provide access to loans with more flexible terms than conventional financial institutions. The loan application process involves evaluating the customer's eligibility, which includes an analysis of financial condition, credit history, and repayment capacity. The contribution of this study provides a recommendation for savings and loan cooperatives in lending to customers using a decision support system model. The purpose of this study is to evaluate and compare customer loan feasibility using a combination of Multi-Criteria Decision Making (MCDM) methods, namely Multi-Attribute Utility Theory (MAUT) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA). This study aims to determine the ability of both methods to provide a comprehensive understanding of loan feasibility by considering a number of relevant criteria. The results of the customer loan eligibility rating recommend rank 1 with a final value of 0.5127 obtained by Customer K, rank 2 with a final value of 0.432 obtained by Customer I, and rank 3 with a final value of 0.3559 obtained by Customer F.

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Multi-criteria decision making in electrical discharge machining with nickel coated aluminium electrode for titanium alloy using preferential selection index

Cited by 6 publications

References 42 publications

Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations

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