A decision support model for identification and prioritization of key performance indicators in the logistics industry

Küçükaltan, Berk; Irani, Zahir; Aktaş, Emel

doi:10.1016/j.chb.2016.08.045

Cited by 97 publications

(73 citation statements)

References 91 publications

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“…Kucukaltan et al [19] present a decision support model for identification and prioritization of KPIs in the logistics industry. They apply BSC and analytic network process (ANP) methods in combination to evaluate logistics performance indicators.…”

Section: Introductionmentioning

confidence: 99%

A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector

Tüysüz

Şimşek

2017

Complex Intell. Syst.

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One of the important points in performance measurement and evaluation is determining the relative importance of the metrics used. The aim of this study is to present a fuzzy multi-criteria decision-making approach for evaluating the factors used for performance evaluation. Hesitant fuzzy sets-based analytic hierarchy process method, which is an effective and successful approach to deal with uncertainty, is proposed for analyzing the factors affecting the performance of the branches of a cargo company in Turkey and also for prioritizing them according to their level of importance.

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Section: Introductionmentioning

confidence: 99%

A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector

Tüysüz

Şimşek

2017

Complex Intell. Syst.

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“…Potential pollution weights show relative effect of each criterion on water quality. Among multi-criteria decision-making (MCDM) approaches (e.g., AHP, DEA, and TOPSIS), the ANP method is the most appropriate method (Saaty and Vargas's, 2006;Kucukaltan et al, 2016), as it takes into account the criteria's dependencies and the calculation of their relative weights (Lin et al, 2009). For this purpose, the interdependency (correlation) of water quality variables (sub-criteria) was firstly determined based on the experts' opinions and literature review (Table 3).…”

Section: Identification Of Pollution Potentialmentioning

confidence: 99%

A novel approach for selecting sampling points locations to river water quality monitoring in data-scarce regions

Alilou

Nia

Saravi

et al. 2019

Journal of Hydrology

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In order to rationalize a surface water quality monitoring network (WQMN), it is critical to appropriately design surface water quality sampling locations. This is due to high installation, operational, and maintenance costs for each sampling representative of the whole water system conditions. The main objective of this study was to propose an integrated method to determine the most appropriate sampling points in the Khoy watershed northwest of Iran, where financial 2 resources and water quality data are limited. Multi criteria evaluation method including analytic network process (ANP) and Fuzzy logic were incorporated in River Mixing Length (RML) procedure in order to identify exact locations of sampling points. Based on RML procedure, 15 candidate sampling points were identified to suitably select sampling points based on budget deficiency. Relative weights for 12 criteria and 10 sub-criteria related to non-point sources and surficial rocks as well as criteria of topography were then calculated by the ANP method. According to the obtained results, a new total potential pollution score (TPPS) was presented to prioritize 15 candidate sampling points. Then, the values of TPPS were classified and fuzzified to distinguish real differences between scores. Based on current monitoring stations and budget deficiency, the hierarchy value, and Fuzzy rank, six points are proposed as the most appropriate locations for surface water quality monitoring. Furthermore, four points are identified as the second most appropriate for enhancing a robust WQMN in the study area in order for an expansion plan in the future. The results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting exact sampling locations.

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“…In this study, the ANP method was applied as a multi-criteria evaluation to determine relative potential pollution weights for non-point sources. Among multi-criteria decision-making (MCDM) approaches (e.g., AHP, DEA, and TOPSIS), the ANP method is the most appropriate method (Saaty and Vargas's, 2006;Kucukaltan et al, 2016), as it takes into account the criteria's dependencies and the calculation of their relative weights (Lin et al, 2009). Subsequently, event mean concentrations (EMC) of each non-point source are used to precisely calculate and determine relative potential pollution weight for non-point sources, which are located in the contributing area (Table 2).…”

Section: Relative Potential Pollution Weight For Non-point Sourcesmentioning

confidence: 99%

A cost-effective and efficient framework to determine water quality monitoring network locations

Alilou

Nia

Keshtkar

et al. 2018

Science of The Total Environment

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A crucial part in designing a robust water quality monitoring network is the selection of appropriate water quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficient manner. The main contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoy watershed in northwest Iran, where financial resources and water quality data are limited. To achieve this purpose, the river mixing length method (RML) was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata-Markov chain model (CA-Markov) was applied to simulate future change in non-point sources during the period 2026-2036. Finally, by considering anthropogenic activities through land-use mapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling points were identified for the present and the future. It is not expected, however, that the present location of the proposed sampling points will change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations.

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A decision support model for identification and prioritization of key performance indicators in the logistics industry

Cited by 97 publications

References 91 publications

A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector

A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector

A novel approach for selecting sampling points locations to river water quality monitoring in data-scarce regions

A cost-effective and efficient framework to determine water quality monitoring network locations

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