Performance indicators for aquatic centres in Canada: Identification and selection using fuzzy based methods

Selection of green remediation alternatives for chemical industrial sites: An integrated life cycle assessment and fuzzy synthetic evaluation approach

The concept of green site remediation calls for a model that can consider environmental impacts in the selection of site remediation alternatives. In this study, an integrated life cycle assessment (LCA)-fuzzy synthetic evaluation (FSE) model is developed to help practitioners select the optimal site remediation plan by incorporating life cycle impacts into the comprehensive suitability evaluation. The LCA module quantifies environmental and economic impacts using ReCiPe and Input-Output LCA methods, respectively. The impacts are evaluated along with other suitability considerations, presented in 32 indicators under ten criteria, by practitioners through a questionnaire survey. FSE is used to process the collected subjective judgments and generate a suitability index for informed selection. The integrated model is applied to a case study of an abandoned chemical industrial site contaminated by various organic chemicals and mercury. Four remediation alternatives, designed as the combined uses of ex-situ thermal desorption, in-situ thermal desorption, and in-situ containment, are evaluated. The LCA results show that the alternative with extensive use (treating 93.8 % of the contaminated soil) of in-situ thermal desorption is associated with the highest environmental and economic impacts, followed by the alternative with less extensive use (6.2 %) of in-situ thermal desorption. The FSE results show that the economic, technical, and environmental impact considerations are the top three important criteria. The integrated LCA-FSE results indicate that the alternative with mixed use of ex-situ thermal desorption and in-situ containment could be the optimal plan. Excluding LCA results could alter the suitability ranks of the alternatives.

Framework of Prediction Model for Mid- to Long-Term Performance Changes of Urban Railway Facilities Based on Performance Evaluation Reports

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.

A Multistakeholder Approach to the Airport Gate Assignment Problem: Application of Fuzzy Theory for Optimal Performance Indicator Selection

Airport gate assignment performance indicator selection is a complicated decision-making problem with strong subjectivity and difficulty in measuring the importance of each indicator. A better selection of performance indicators (PIs) can greatly increase the airport overall benefit. We adopt a multicriteria decision-making approach to quantify qualitative PIs and conduct subsequent selection using the fuzzy clustering method. First, we identify 21 commonly used PIs through literature review and survey. Subsequently, the fuzzy analytic hierarchy process technique was employed to obtain the selection criteria weights based on the relative importance of significance, availability, and generalisability. Further, we aggregated the selection criteria weights and experts' score to evaluate each PI for the clustering process. The fuzzy-possibilistic product partition c-means algorithm was applied to divide the PIs into different groups based on the three selection criteria as partitioning features. The cluster with highest weights of the centre was identified as the very high-influence cluster, and 10 PIs were identified as a result. This study revealed that the passenger-oriented objective is the most important performance criterion; however, the relevance of the airport/airline-oriented and robustness-oriented performance objectives was highlighted as well. It also offers a scientific approach to determine the objective functions for future gate assignment research. And, we believe, through slight modifications, this model can be used in other airports, other indicator selection problems, or other scenarios at the same airport to facilitate policy making and real situation practice, hence facilitate the management system for the airport.