Sustainable development is one of the most important preconditions for preserving resources and balanced functioning of a complete supply chain in different areas. Taking into account the complexity of sustainable development and a supply chain, different decisions have to be made day-to-day, requiring the consideration of different parameters. One of the most important decisions in a sustainable supply chain is the selection of a sustainable supplier and, often the applied methodology is multi-criteria decision-making (MCDM). In this paper, a new hybrid MCDM model for evaluating and selecting suppliers in a sustainable supply chain for a construction company has been developed. The evaluation and selection of suppliers have been carried out on the basis of 21 criteria that belong to all aspects of sustainability. The determination of the weight values of criteria has been performed applying the full consistency method (FUCOM), while a new rough complex proportional assessment (COPRAS) method has been developed to evaluate the alternatives. The rough Dombi aggregator has been used for averaging in group decision-making while evaluating the significance of criteria and assessing the alternatives. The obtained results have been checked and confirmed using a sensitivity analysis that implies a four-phase procedure. In the first phase, the change of criteria weight was performed, while, in the second phase, rough additive ratio assessment (ARAS), rough weighted aggregated sum product assessment (WASPAS), rough simple additive weighting (SAW), and rough multi-attributive border approximation area comparison (MABAC) have been applied. The third phase involves changing the parameter ρ in the modeling of rough Dombi aggregator, and the fourth phase includes the calculation of Spearman’s correlation coefficient (SCC) that shows a high correlation of ranks.
Understanding track geometry deterioration decisively influences the planning and optimisation of track maintenance and renewal works and consequently the substantial related costs (savings) each year of every railway. To understand this deterioration it must be accurately modelled, paving the way towards its forecasting into the future. The main aim of the present study was to model railway track geometry deterioration using multivariate statistical analysis of the variables involved and to predict the future behaviour of the track geometry deterioration. For this purpose, a track section of about 180 km in length was selected as the base for the model and divided into analytical segments of as uniform characteristics as possible using a special segmentation algorithm. The lengths of the individual analytical segments were not identical, but were also kept as close to uniform as possible. For each analytical segment, the following general information was collected: track structure, traffic characteristics, track layout, environmental factors, track geometry measurements records and maintenance and renewal history data. Consequently, multivariate statistical analysis was performed on the main track geometry parameters: twist, level, alignment, gauge and cant. The coefficients of the independent variables involved in track geometry were found for each parameter in order to predict future behaviour for the purposes of efficient maintenance and renewal management.
Healthcare systems worldwide are facing problems in providing health care to patients in a pandemic caused by the SARS-CoV-2 virus (COVID-19). The pandemic causes an extreme disease to spread with fluctuating needs among patients, which significantly affect the capacity and overall performance of healthcare systems. In addition, its impact on the sustainability of the entire economic and social system is enormous and certain sustainable management strategies need to be selected. To meet the challenges of the COVID-19 pandemic and ensure sustainable performance, national healthcare systems must adapt to new circumstances. This paper proposes an original multi-criteria methodology for the sustainable selection of strategic guidelines for the reorganization of a healthcare system under the conditions of the COVID-19 pandemic. The selection of an appropriate strategic guideline is made on the basis of defined criteria and depending on infection capacity and pandemic spread risk. The criteria for the evaluation of strategic guidelines were defined on the basis of a survey in which the medical personnel engaged in the crisis response team during the COVID-19 pandemic in the Republic of Serbia participated. The Level-Based Weight Assessment (LBWA) model and Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method were used to determine the weight coefficient criteria, while a novel fuzzy Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval (RAFSI) model was used to evaluate the strategic guidelines. The proposed multi-criteria methodology was tested in a case study in the Republic of Serbia. The validity of the proposed methodology is shown through the simulation of changes in input parameters of Bonferroni aggregation functions and through a comparison with other multi-criteria methodologies.
A negative flow-sharing approach to allocate transmission transaction charges among users of transmission services is proposed. The approach uses the properties of the MW-mile method but takes into account the economic benefits of both trading parties by analysing their shares in negative power flow or counterflow. This approach is incorporated with the justified distribution factor for power flow tracing purposes. Two case studies based on a 5-bus system and an IEEE 14-bus system are used to illustrate the proposed approach. The results show that the proposed approach has merit over the traditional MW-mile approaches in the context of revenue reconciliation of transmission services, regardless of transaction arrangements and locations. The profit-sharing concept introduced here provides a better economic signal in allocating charges for counterflows, which could benefit trading parties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.