MCDA Index Tool: an interactive software to develop indices and rankings

Cinelli, Marco; Spada, Matteo; Kim, Wansub; Zhang, Yiwen; Burgherr, Peter

doi:10.1007/s10669-020-09784-x

Cited by 50 publications

(27 citation statements)

References 75 publications

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“…Third, the performance matrix was normalized using one of the eight possible normalization techniques. The normalization techniques include the rank, percentile rank, standardized, min-max, logistic, categorical (-1,0,1), categorical (0.1,0.2,0.4,0.6,0.8,1), and target methods [29]. The outcome of the CI-based procedure can ultimately weigh or rank the SNDA and identify which algorithms are best suited for the classification problem in question.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Fourth, the scores assigned after the normalized process are combined using a particular aggregation function to generate an index for each alternative (i.e., algorithm). These aggregation functions include additive, geometric, harmonic, minimum, and median functions [29]. Fifth, indices assigned to different algorithms are used to assign ranks to these algorithms.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Hence, it can be modeled as a multi-criteria decision analysis (MCDA) problem [27]. MCDA is a formal process that can be employed in decision-making by allowing identification of the alternatives, selection of the evaluation indicators (i.e., criteria), and aggregation of the obtained performance scores under the considered performance evaluation criteria [28,29]. In this context, the composite indicators (CIs) are a family of MCDA methods that can be used to assign scores to various alternatives and rank them appropriately [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

Al-Tarawneh¹

2021

IJFIS

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This paper reports on the use of online data stream classification algorithms to support workload orchestration in vehicular edge computing environments. These algorithms can be used to predict the ability of available computational nodes to successfully handle computational tasks generated from vehicular applications. Several online data stream classification algorithms have been evaluated based on synthetic datasets generated from simulated vehicular edge computing environments. In addition, a multi-criteria decision analysis technique was utilized to rank the different algorithms based on their performance metrics. The evaluation results demonstrate that the considered algorithms can handle online classification operations with various trade-offs and dominance relations with respect to their obtained performance. In addition, the utilized multi-criteria decision analysis technique can efficiently rank various algorithms and identify the most appropriate algorithms to augment workload orchestration. Furthermore, the evaluation results show that the leveraging bagging algorithm, with an extremely fast decision tree base estimator, is able to maintain marked online classification performance and persistent competitive ranking among its counterparts for all datasets. Hence, it can be considered a promising choice to reinforce workload orchestration in vehicular edge computing environments.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Evaluation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Stream Classification Algorithms for Workload Orchestration in Vehicular Edge Computing: A Comparative Evaluation

Al-Tarawneh¹

2021

IJFIS

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“…To overcome this challenge, the model-based approach has been introduced to systematically select attributes that cover the concerns and values of the problem under consideration. Accordingly, a few studies provide model-driven attributes by formulating the selection process of attributes as a choice problem (Cinelli et al 2020;Höfer et al 2020;Otto et al 2018;Rossberg et al 2017;Dale et al 2015;. Regardless of the source of the alternative pool, these studies evaluate candidate attributes based on properties of the desired attribute and apply a systematic process to select the attribute set.…”

Section: Current Approaches Towards the Selection Of Attributesmentioning

confidence: 99%

A prescriptive framework for recommending decision attributes of infrastructure disaster recovery problems

Zamanifar

Hartmann

2021

Environ Syst Decis

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This paper proposes a framework to systematically evaluate and select attributes of decision models used in disaster risk management. In doing so, we formalized the attribute selection process as a sequential screening-utility problem by formulating a prescriptive decision model. The aim is to assist decision-makers in producing a ranked list of attributes and selecting a set among them. We developed an evaluation process consisting of ten criteria in three sequential stages. We used a combination of three decision rules for the evaluation process, alongside mathematically integrated compensatory and non-compensatory techniques as the aggregation methods. We implemented the framework in the context of disaster resilient transportation network to investigate its performance and outcomes. Results show that the framework acted as an inclusive systematic decision aiding mechanism and promoted creative and collaborative decision-making. Preliminary investigations suggest the successful application of the framework in evaluating and selecting a tenable set of attributes. Further analyses are required to discuss the performance of the produced attributes. The properties of the resulting attributes and feedback of the users suggest the quality of outcomes compared to the retrospective attributes that were selected in an unaided selection process. Research and practice can use the framework to conduct a systematic problem-structuring phase of decision analysis and select an equitable set of decision attributes.

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“…They found that FEW systems can be significantly impacted by labor shortages, and that food systems were more resilient compared to water and energy systems. Cinelli et al ( 2021 ) developed a MCDA tool and demonstrated a case study assessing the energy security and sustainability of various energy alternatives. The tool is available online for non-experts to use and includes various visualization and sensitivity analysis features.…”

mentioning

confidence: 99%