Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria

Alao, Moshood Akanni; Ayodele, T.R.; Ogunjuyigbe, A.S.O.; Popoola, A. P. I.

doi:10.1016/j.energy.2020.117675

Cited by 124 publications

(36 citation statements)

References 35 publications

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“…Alao et al [47] presented a study on evaluating technologies used in the waste-toenergy process. They defined this problem as a multi-criteria decision-making problem.…”

Section: Recent Studies On Objective Weighting Methodsmentioning

confidence: 99%

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

et al. 2021

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The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

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“…Alao et al [47] presented a study on evaluating technologies used in the waste-toenergy process. They defined this problem as a multi-criteria decision-making problem.…”

Section: Recent Studies On Objective Weighting Methodsmentioning

confidence: 99%

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The newest trend regarding the use of MCDM methods is to combine two or more methods to recoup the weaknesses of a single method [ 63 – 73 ]. AHP and TOPSIS have become a commonly integrated MCDM method [ 74 – 76 ].…”

Section: Methodsmentioning

confidence: 99%

“…The problem in identifying the ideal solution with regard to the reference range for the COVID-19 serological/protein biomarkers has not been detected and recognised [34]. Thus, The newest trend regarding the use of MCDM methods is to combine two or more methods to recoup the weaknesses of a single method [63][64][65][66][67][68][69][70][71][72][73]. AHP and TOPSIS have become a commonly integrated MCDM method [74][75][76].…”

Section: Adopted Mcdm Techniquesmentioning

confidence: 99%

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

et al. 2021

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As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CPtransfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

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“…Nowadays the priority matter is considered very challenges for different kind of medical perspectives [ [48] , [49] , [50] ]. Recently, the newest trend regarding to MCDM methods use is to combine two or more methods to recoup the weaknesses in a single method [ 51 ]. Entropy and TOPSIS have become a commonly integrated MCDM method [ 52 , 51 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, the newest trend regarding to MCDM methods use is to combine two or more methods to recoup the weaknesses in a single method [ 51 ]. Entropy and TOPSIS have become a commonly integrated MCDM method [ 52 , 51 ]. For these explanation the utilisation of weights and target information to acquire the relative distances, the ability to offer complete ranking results, trade-off smoothing by managing nonlinear relationships, the straightforwardness at which it tends to be easy programmable method, and the reasonableness to be joined with stochastic analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

Albahri

Hamid

Albahri

et al. 2021

Artificial Intelligence in Medicine

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Highlights Develop a new framework that can handle the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. The most important laboratory criteria were selected and implemented based on two feature selection approaches (i.e. data-driven and knowledge-driven). A new decision matrix was developed based on a crossover of (i) multi-laboratory characteristics criteria and (ii) lists of infected patients for patient prioritisation using integration Entropy-TOPSIS methods. Advantages of new framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.

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Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria

Cited by 124 publications

References 35 publications

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods

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