Efficient Crisis Management by Selection and Analysis of Relief Centers in Disaster Integrating GIS and Multicriteria Decision Methods: A Case Study of Tehran

In Iran, location is usually done by temporary relief organizations without considering the necessary standards or conditions. The inappropriate and unscientific location may have led to another catastrophe, even far greater than the initial tragedy. In this study, the proposed locations of crisis management in the region and the optimal points proposed by the Geographic Information System (GIS), taking into account the opinions of experts and without the opinion of experts, were evaluated according to 18 criteria. First, the optimal areas have been evaluated according to standard criteria extracted by GIS and the intended locations of the region for accommodation in times of crisis. Then, the position of each place is calculated concerning each criterion. The resulting matrix of optimal options was qualitatively entered into the Preference Ranking Organization Method for Evaluation (PROMETHEE) for analysis. The triangular fuzzy aggregation method for weighting and standard classification of criteria for extracting optimal areas using GIS and integrating entropy and the Multiobjective Optimization Based on Ratio Analysis (MOORA) method for prioritizing places in the region are considered in this research. Finally, by applying constraints and using net input and output flows, optimal and efficient options are identified by PROMETHEE V. Among the research options, only four options were optimal and efficient. A case study of the Tehran metropolis is provided to show the ability of the proposed approach for selecting the points in three modes, with/without applying weights and applying crisis management

A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods

The working world is undergoing profound changes, and occupational accidents are always a global concern due to substantial impacts on productivity collapse and workers’ safety. To address this problem, Failure Mode and Effects Analysis (FMEA) has been widely implemented to assess such risks. This, however, fails to provide reliable results because of some shortcomings of the risk priority number score of the FMEA including neglecting the weight of risk factors, having doubtful formulation, and performing poorly in distinguishing risks. This study presents a two-phase approach to identify and prioritize Health, Safety and Environment (HSE) risks to focus on critical risks instead of diverting organizational efforts to non-critical ones and overcoming the shortcomings of the traditional score. In the first phase, potential risks are identified, and after determining the value of risk factors using the FMEA technique, Fuzzy C-means (FCM) algorithm is applied to cluster these risks. Then, the weight of risk factors is calculated based on the Fuzzy Best–Worst Method (FBWM), and following this, clusters are labeled based on weighted Euclidean distance. In the second phase, a hybrid Multi-Criteria Decision-Making (MCDM) method is proposed based on the FBWM and combined compromise solution to prioritize risks belonging to the critical cluster. This is to create a distinct priority for risks and facilitate the implementation of corrective/preventive actions. This approach is applied in the automotive industry, and results are compared with other FMEA-based MCDM methods to validate findings. Eventually, a sensitivity analysis is designed to show the ability and applicability of the proposed approach

GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game

Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-means is one of the well-known algorithms for clustering data. The foundation of this algorithm is based on the distance measure. However, the traditional k-means has some weaknesses that appear in some data sets related to real applications, the most important of which is to consider only the distance criterion for clustering. Various studies have been conducted to address each of these weaknesses to achieve a balance between quality and efficiency. In this paper, a novel k-means variant of the original algorithm is proposed. This approach leverages the power of bargaining game modelling in the k-means algorithm for clustering data. In this novel setting, cluster centres compete with each other to attract the largest number of similar objectives or entities to their cluster. Thus, the centres keep changing their positions so that they have smaller distances with the maximum possible data than other cluster centres. We name this new algorithm the game-based k-means (GBK-means) algorithm. To show the superiority and efficiency of GBK-means over conventional clustering algorithms, namely, k-means and fuzzy k-means, we use the following syntactic and real-world data sets: (1) a series of two-dimensional syntactic data sets; and (2) ten benchmark data sets that are widely used in different clustering studies. The evaluation criteria show GBK-means is able to cluster data more accurately than classical algorithms based on eight evaluation metrics, namely F-measure, the Dunn index (DI), the rand index (RI), the Jaccard index (JI), normalized mutual information (NMI), normalized variation of information (NVI), the measure of concordance and error rate (ER)

Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Organizations are continuously endeavoring to provide a healthy work environment without any incident, by Health, Safety, and Environment (HSE) management. As most of the activities and processes in the organizations have risk-taking nature, identification and evaluation of risks can be useful to decrease their negative effects on the system. Although Failure Mode and Effect Analysis (FMEA) technique is used widely for risk assessment, the traditional Risk Priority Number (RPN) score has shortcomings like do not considering different weights and the inherent uncertainty of risk factors as well as do not regarding all viewpoints of the experts in decision making. The aim of this study is presenting a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS) and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering some RPN shortcomings and the prioritization of HSE risks. First, after identifying potential risks and assigning values to the RPN determinant factors by linguistic FMEA team members due to the differentiation of these values, FIS is used to reach a consensus opinion about these factors. Then, the outputs of FIS are used by the fuzzy DEA and its supper efficiency model to risk prioritization which can contribute to full prioritization. In addition to considering uncertainty and decreasing dependence on the team’s opinions, in this phase weights of triple factors are calculated based on mathematical programming. To show the ability of the proposed approach in terms of HSE risks prioritization, it has been implemented in an active company in the chemical industry. After identifying risks having high priority based on the proposed score, preventive/corrective actions are presented in accordance with the case study, and for more analysis of results, the self-organizing map has been applied in this study