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Abstract: Common Service Centers (CSCs), which are also known as Tele-centers and Rural Kiosks, are important infrastructural options for any country aiming to provide E-Governance services in rural regions. Their main objective is to provide adequate information and services to a country’s rural areas, thereby increasing government-citizen connectivity. Within developing nations, such as India, many CSC allocations are being planned. This study proposes a solution for allocating a CSC for villages in a country according to their E-Governance plan. The Fuzzy C-Means (FCM) algorithm was used for clustering the village dataset and finding a cluster center for CSC allocation, and the Particle Swarm Optimization (PSO) algorithm was used for further optimizing the results obtained from the FCM algorithm based on population. In the context of other studies addressing similar issues, this study highlights the practical implementation of location modeling and analysis. An extensive analysis of the results obtained using a village dataset from India including four prominent states shows that the proposed solution reduces the average traveling costs of villagers by an average of 33 % compared with those of allocating these CSCs randomly in a sorted order and by an average of 11 % relative to centroid allocation using the FCM-based approach only. As compared to traditional approaches like P-Center and P-Median, the proposed scheme is better by 31 % and 14 %, respectively. Therefore, the proposed algorithm yields better results than classical FCM and other types of computing techniques, such as random search & linear programming. This scheme could be useful for government departments managing the allocation of CSCs in various regions. This work should also be useful for researchers optimizing the location allocation schemes used for various applications worldwide.
Keywords: common service centers, tele-centers, e-governance, location allocation, fuzzy C-means clustering, particle swarm optimization
Published in RUNG: 17.02.2021; Views: 2352; Downloads: 0
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Abstract: Some applications of soft set theory and fuzzy soft set theory (as a generalization of the soft set theory) in the decision-making problem were investigated by Maji et al. in [1]. The aim of this manuscript is to apply the concept of (fuzzy) soft set theory in some hypercompositional structure and especially hyper BCK-algebra. Some notations related to our hyperstructure are introduced and some examples are provided.
Keywords: Soft set theory, Hyper BCK-algebra, Fuzzy soft set theory.
Published in RUNG: 08.07.2020; Views: 2395; Downloads: 0
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Keywords: hypergroup, fuzzy set, reduced hypergroup
Published in RUNG: 23.06.2020; Views: 2493; Downloads: 0
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Keywords: ordered semigroup, fuzzy subset, fuzzy quasi ideal
Published in RUNG: 14.05.2020; Views: 2553; Downloads: 0
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Abstract: In the paper [P.K. Maji, R. Biswas, A.R. Roy, Fuzzy soft sets, J. Fuzzy Math. 9 (3) (2001) 589–602], Maji et al. introduced the concept of fuzzy soft sets as a generalization of the standard soft sets and presented an application of fuzzy soft sets in a decision-making problem. The aim of this paper is to apply a fuzzy soft set for dealing with several kinds of theories in hyper BCK-algebras. The notions of fuzzy soft hyper BCK-ideal, fuzzy soft weak hyper BCK-ideal, fuzzy soft s-weak hyper BCK-ideal, and fuzzy soft strong hyper BCK-ideal are introduced, and related properties and relations are investigated.
Keywords: fuzzy soft hyper BCK-ideal, fuzzy soft weak hyper BCK-ideal, fuzzy soft s-weak hyper BCK-ideal, fuzzy soft strong hyper BCK-ideal
Published in RUNG: 24.02.2020; Views: 2743; Downloads: 0
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