COTS evaluation using modified TOPSIS and ANP

Shyur, Huan‐Jyh

doi:10.1016/j.amc.2005.11.006

Cited by 150 publications

(91 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The idea of the integration of ANP and TOPSIS techniques was first proposed by Shyur and Shih (2006) and Shyur (2006). Shyur and Shih (2006) presented the integrated approach for a strategic vendor selection problem.…”

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

“…They then used the modified TOPSIS function, which exploits a newly defined weighted Euclidean distance and aims to rank competing products in terms of their overall performance with multiple criteria. Shyur (2006) also used a very similar approach to a different problem named the commercial-off-the-self (COTS) evaluation and selection problem. Inspired by these two papers, this paper utilizes an integrated ANP and TOPSIS methodology for the centralized return center (CRC) location evaluation problem of a reverse logistics network (RLN) design problem.…”

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

“…These pair-wise comparison matrices are needed to identify the relative impacts of criteria-interdependent relationships. The normalized principle eigenvectors for these matrices are calculated and shown as column components in the interdependence weight matrix of criterion B, where zeros are assigned to the eigenvector weights of the criteria for which a given criterion is given (Shyur, 2006). The final step of the ANP approach is to integrate the interdependence priorities with the priority vectors…”

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

Tuzkaya

Gülsün

2008

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

ABSTRACT:In this paper, the centralized return centers location evaluation problem in a reverse logistics network is investigated. This problem is solved via an integrated analytic network process-fuzzy technique for order preference by similarity to ideal solution approach. Analytic network process allows us to evaluate criteria preferences while considering interdependence between them. On the other hand, technique for order preference by similarity to ideal solution decreases the number of computational steps compared to simple analytic network process evaluation. An important characteristic of the centralized return centers location evaluation problem, vagueness, is adapted to the methodology via the usage of fuzzy numbers in the technique for order preference by similarity to ideal solution approach. Finally, a numerical example is given to demonstrate the usefulness of the methodology. The results indicate that, this integrated multi-criteria decision making methodology is suitable for the decision making problems that needs considering multiple criteria conflicting each other. Also, by using this methodology, the interdependences between the criteria may be considered for these kinds of problems in a flexible and systematic manner.

show abstract

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

Section: Proposed Crc Location Evaluation Methodology: Integrated Anpmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

Tuzkaya

Gülsün

2008

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The work presented in [17] evaluates the components and establishes a ranking in terms of performance and according to multiple criteria. In [7], authors perform a management of dependencies between components using goal-oriented models as the basis for component selection.…”

Section: Related Workmentioning

confidence: 99%

Semantic Matching of Components at Run-Time in Distributed Environments

Criado

Iribarne

Padilla

et al. 2015

On the Move to Meaningful Internet Systems: OTM 2015 Workshops

View full text Add to dashboard Cite

Abstract. Software factories are a key element in Component-Based Software Engineering due to the common space provided for software reuse through repositories of components. These repositories can be developed by third parties in order to be inspected and used by different organizations, and they can also be distributed in different locations. Therefore, there is a need for a trading service that manages all available components. In this paper, we describe a matching process based on syntactic and semantic information of software components. This matching operation is part of a trading service which is in charge of generating configurations of components from architectural definitions. With this aim, the proposed matching allows us to evaluate and score the possible configurations, thus guiding a search process to build the architectural solution which best fulfills an input definition.

show abstract

“…The full AHP-fuzzy AHP solution is only practically usable if the number of criteria and alternatives is sufficiently low so that the number of pairwise comparisons performed by evaluator must remain below a reasonable threshold (Dagdeviren et al, 2009). For example, if there are n criteria which have been assigned the importance weights and m alternatives, then to run a full AHP-Fuzzy AHP solution there are 1 /2 pairwise comparisons remaining to be performed for running a full AHP-fuzzy AHP solution (Shyur, 2006). Simultaneous deployment of FAHP and TOPSIS results in a reduction of pairwise comparisons.…”

Section: Introductionmentioning

confidence: 99%

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

Parsaei¹,

Keramati²,

Zorriassatine³

et al. 2012

ijiec

View full text Add to dashboard Cite

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.

show abstract

COTS evaluation using modified TOPSIS and ANP

Cited by 150 publications

References 11 publications

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

Semantic Matching of Components at Run-Time in Distributed Environments

An order acceptance using FAHP and TOPSIS methods: A case study of Iranian vehicle belt production industry

Contact Info

Product

Resources

About