Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method

Omrani, Hashem; Alizadeh, Arash; Emrouznejad, Ali

doi:10.1016/j.jclepro.2018.08.238

Cited by 85 publications

(35 citation statements)

References 43 publications

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“…Compared to AHP, BWM uses less comparison (BWM= 2n − 3, AHP= n(n − 1)/2), yet the consistency is better [48,54]. BWM is used to solve various problems, namely, supplier selection [55], location selection [50,53,56], service quality improvement [57], product design selection [49,58,59], supply-chain management [60], and performance evaluation [52,61]. BWM allows the decision-maker to select the best and the worst criteria to be transformed into the weight of each criterion using linear programming.…”

Section: Step 4: Best-worst Methodsmentioning

confidence: 99%

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

2020

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Improving service logistics is crucial in order to reciprocate customer needs. The paper aims to validate the Logistics Service Provider (LSP) Lifecycle Model for re-designing logistics service in three LSP case studies in Thailand. The lifecycle-stage evaluation was adapted to identify the current status in its lifecycle. Afterward, logistics service strategies were implemented according to the voice of the customer by Quality Function Deployment (QFD). The study combined the Logistics Service Provider (LSP) Lifecycle Model with the application of Industry 4.0 (I4.0) to improve service logistics. Case studies showed the implementation of the service logistics strategies with the feasibility solution of Industry 4.0.Sustainability 2020, 12, 2394 2 of 17 real-time information, and Circular Economy (CE) [21] are now among the basic customer requirements. Furthermore, customer behavior has changed too. Quick and immediate responses are expected. Information Technology (IT) has been increasingly used in the global business to satisfy the customer in terms of supply [22,23]. Today, Industry 4.0 is being used to accomplish the customer target, operation, and improvement in manufacturing [24,25]. Some examples of the advancement of Industry 4.0 are applying to self-learning automation, big data analytics, real-time information, Internet of Things (IoT), and smart sensors [26,27]. This improvement expands to the logistics industry in the form of, for example, on-demand delivery which offers an abrupt response to the customer [28,29]. The trend has changed rapidly around the world and the logistics service is demanded to support it. Therefore, LSPs need to innovate and create new service to address these complicated customer trends [30]. This paper demonstrates the use of the LSP Lifecycle Model to re-design the logistics service using three case-study LSPs in Thailand. It aims to address the research questions of whether the developed LSP Lifecycle Model is valid and if the Industry 4.0 concept is applicable and can be beneficial for LSPs. Firstly, the study identified the stage of the service. Secondly, services were re-designed using QFD. Thirdly, customer requirements were prioritized using the Multiple-Criteria Decision-Making (MCDM) tool. Finally, the Industry 4.0 concept was mapped to address these demands. Literature Review

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Section: Step 4: Best-worst Methodsmentioning

confidence: 99%

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

2020

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“…where ε L = (m ε , n ε , q ε ). Assuming the optimal objective function value ε L * = (k * , k * , k * ), k * < m ε , Equation (6) can be transformed into [46]:…”

Section: Constraint Reliability Linguistic Terms Fuzzy Numbers Linguimentioning

confidence: 99%

A Multi-Criteria Decision Support Framework for Inland Nuclear Power Plant Site Selection under Z-Information: A Case Study in Hunan Province of China

Peng

Wang

et al. 2020

Mathematics

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To promote the development of nuclear power project in inland China, a specialized multi-criteria decision support framework is constructed for the site selection of inland nuclear power plants in this work. The best worst method (BWM), decision-making trial and evaluation laboratory (DEMATEL), and technique for order performance by similarity to ideal solution (TOPSIS) are integrated with Z-numbers, namely Z-BWM, Z-DEMATEL, and Z-TOPSIS. Z-BWM and Z-DEMATEL are combined to produce the weights of criteria, and an extended Z-TOPSIS is utilized to determine the ranking of all alternatives. Finally, a case study is performed in Hunan province to demonstrate the practicability of the proposed decision support framework. The robustness and feasibility of the proposed method are shown by an in-depth analysis of the decision results.that are not prone to tsunamis or typhoons. China has a total of 48 nuclear power units in operation, but they are all in coastal areas. The specific research on the site selection of China's inland NPPs is still insufficient. In this work, a multi-criteria decision support framework is provided to identify the best site for inland NPPs in China, and the contributions are summarized as follows:(1) The existing researches on multi-criteria decision-making (MCDM) problems inevitably involve the uncertainly and unreliability of human preferences. Therefore, Z-number is introduced into the proposed MCDM framework in this work, and all evaluation information is given using Z-number linguistic variables.(2) Best worst method (BWM) and decision-making trial and evaluation laboratory (DEMATEL) are extended to Z-numbers, i.e., Z-BWM and Z-DEMATEL. The hybrid model not only considers the impact of the criterion relations on the criterion weights, but also provides a long-term improvement for inland NPP site selection.(3) The technique for order performance by similarity to ideal solution (TOPSIS) is combined with Z-numbers, namely Z-TOPSIS. It can effectively reduce the influence of outliers, and provide more reliable ranking results than the classic TOPSIS.(4) Currently, few researches focus on the site selection of China's inland NPPs. Based on the reactor siting criteria developed by IAEA, a specialized evaluation system considering the eco-environment, geographic features, and socio-economic factors in China's interior is constructed.The structure of the paper is organized as follows. Section 2 reviews some studies related to NPP site selection. Section 3 introduces some basic concepts of Z-numbers. Section 4 presents the proposed framework for inland NPP site selection. Section 5 provides a case study in Hunan province of China. Section 6 conducts an in-depth analysis concerning selection results. Finally, Section 7 draws conclusions.

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“…The method was applied to the technology battle for biomass conversion in the Netherlands. Omrani et al [38] proposed an integrated approach including Taguchi-neural network BWM and TOPSIS under fuzzy environment. The proposed integrated approach was applied to a case study to find the optimal combination of different power plants.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

et al. 2019

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Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.

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Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method

Cited by 85 publications

References 43 publications

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

A Multi-Criteria Decision Support Framework for Inland Nuclear Power Plant Site Selection under Z-Information: A Case Study in Hunan Province of China

An Integrated Best-Worst and Interval Type-2 Fuzzy TOPSIS Methodology for Green Supplier Selection

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