Green supplier selection of electric vehicle charging based on Choquet integral and type-2 fuzzy uncertainty

Wu, Yunna; Xu, Chuanbo; Huang, Yong; Li, Xinying

doi:10.1007/s00500-019-04147-4

Cited by 24 publications

(8 citation statements)

References 51 publications

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“…Twelve studies have proposed a variety of aggregation operator-based methods for evaluating and selecting suppliers in the sustainable supply chain. Wu et al [143] selected the optimal green supplier of electric vehicle charging facility based on the Choquet integral operator and interval type-2 fuzzy uncertainty. Shahryari Nia et al [146] determined the best green supplier for a manufacturing company using interval-valued intuitionistic fuzzy sets and the Choquet integral operator.…”

Section: Aggregation Operator-based Methodsmentioning

confidence: 99%

“…Green manufacturing [17,19,23,31,37,43,49,51,52,66,77,79,85,95,117,124,125,132,133,141,143,151,157,173,175,176,184,189,191,197] 30 Green packing and labeling [19, 21, 27, 30, 32, 51, 52, 58, 72-74, 77, 79, 85, 112, 124, 135, 146, 152, 168, 181, 195] 22 Green certifications [20, 25, 30, 32, 33, 42, 43, 55, 77, 81, 85, 99, 113, 118, 124, 136, 138, 139, 146, 151, 152, 161-163, 174, 175, 189, 191, 195, 204] 30 Green logistics …”

Section: Green Competenciesmentioning

confidence: 99%

“…Technology level [23,27,46,74,81,98,99,110,120,123,124,135,143,148,149,152,169,175,180,189,191,199,204,208]…”

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confidence: 99%

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Green Supplier Evaluation and Selections: A State-of-the-Art Literature Review of Models, Methods, and Applications

Zhang

Liu

et al. 2020

Mathematical Problems in Engineering

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The role of green supplier evaluation and selection (GSES) in supply chain management is increasingly appreciated due to the intensification of competition, raising public consciousness, and environmental issues. To improve GSES, a large number of approaches have been proposed in the past decades. However, few attempts have been made to systematically review and classify the literature in this field. The objective of this study is to afford a comprehensive review of the studies which aim to develop models and methods in helping enterprises to assess and select the right green suppliers. To achieve this goal, a total of 193 journal articles extracted from the Scopus database over the period of 2009 to 2020 were chosen and reviewed. These publications were classified into ten categories based on their adopted GSES models and analyzed concerning the evaluation criteria, criteria weighting methods, and performance evaluation methods. Moreover, a bibliometric analysis was conducted according to the frequency of supplier selection methods, citation number, publication year, journal, country, and application area. This study supports practitioners, managers, and researchers in effectively recognizing and applying the GSES models to enhance organizational competitiveness and provides an insight into its state of the art.

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Section: Aggregation Operator-based Methodsmentioning

confidence: 99%

Section: Green Competenciesmentioning

confidence: 99%

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Green Supplier Evaluation and Selections: A State-of-the-Art Literature Review of Models, Methods, and Applications

Zhang

Liu

et al. 2020

Mathematical Problems in Engineering

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“…Qin et al., 16 utilized the IT2FSs to represent the evaluation information of green suppler provided by the DMs and improved the TOMID method to select the green supplier. Wu et al., 41 proposed a new fuzzy decision‐making framework based on Choquet integral and T2FSs for solving the problem of electric vehicle charging supplier selection. Abdullah and Najib 42 proposed a new fuzzy AHP method based on IT2FSs and utilized the proposed method to evaluate workplace safety.…”

Section: Literature Reviewmentioning

confidence: 99%

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

Zheng

Liu

Wang

2021

Quality & Reliability Eng

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The failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.

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“…In (Spieker et al 2017), multi-objective is solved using a modified genetic algorithm to serve maximum number of EV by placing EVCS. The optimal charging facilities are selected in (Wu et al 2020) considering uncertainties. Uncertain vehicle rent is taken care for maximum profit of suppliers in (Long et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Allocation of EV Public Charging Station in Renewable based Distribution Network using HHO Considering Uncertainties and Traffic Congestion

Pal¹,

Bhattacharya²,

Chakraborty³

2021

Preprint

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Electric vehicle (EV) is the growing vehicular technology for sustainable development to reduce carbon emission and to save fossil fuel. The charging station (CS) is necessary at appropriate locations to facilitate the EV owners to charge their vehicle as well as to keep the distribution system parameters within permissible limits. Besides that, the selection of a charging station is also a significant task for the EV user to reduce battery energy wastage while reaching the EV charging station. This paper presents a realistic solution for the allocation of public fast-charging stations (PFCS) along with solar distributed generation (SDG). A 33 node radial distribution network is superimposed with the corresponding traffic network to allocate PFCSs and SDGs. Two interconnected stages of optimization are used in this work. The first part deals with the optimization of PFCS’s locations and SDG’s locations with sizes, to minimize the energy loss and to improve voltage profile using harris hawk optimization (HHO) and few other soft computing techniques. The second part handles the proper assignment of EVs to the PFCSs with less consumption of the EV’s energy considering the road distances with traffic congestion using linear programming (LP), where the shortest paths are decided by Dijkstra's algorithm. The 2m point estimation method (2m PEM) is employed to handle the uncertainties associated with EVs and SDGs. The robustness of solutions are tested using wilcoxon signed rank test and quade test.

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Green supplier selection of electric vehicle charging based on Choquet integral and type-2 fuzzy uncertainty

Cited by 24 publications

References 51 publications

Green Supplier Evaluation and Selections: A State-of-the-Art Literature Review of Models, Methods, and Applications

Green Supplier Evaluation and Selections: A State-of-the-Art Literature Review of Models, Methods, and Applications

A likelihood‐based ORESTE method for failure mode and effect analysis (FMEA) based risk analysis problem under interval type‐2 fuzzy environment

Allocation of EV Public Charging Station in Renewable based Distribution Network using HHO Considering Uncertainties and Traffic Congestion

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