Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Human reliability analysis in deep excavation projects using a fuzzy Bayesian HEART-5M integrated method: case of a residential tower in north Tehran.

Objectives. Numerous labourers lose their lives or suffer from injuries and disabilities yearly due to the lack of safety enforcement in construction projects and accidents caused by excavation collapses. The identification and ranking of human errors have always been a central concern in civil engineering. Previous studies on excavation work and related risks have focused on retaining structure methods, while human errors may be a significant contributor to accidents and near misses. Methods. This study identified human errors in deep excavation projects using hierarchical task analysis (HTA) and a systematic human error reduction and prediction approach (SHERPA). Results. The fuzzy Bayesian human error assessment and reduction technique (HEART)-5M method was implemented to determine the human error probability (HEP) for all case-study tasks. Critical tasks were obtained as 'drainage system execution', 'water and wastewater pipes', 'gas pipes', 'checking cracks in surrounding buildings' and 'checking soil slippage' with probability levels of 0.46, 0.44, 0.44, 0.37 and 0.37, respectively. Finally, remedial measures were presented for crucial tasks. Conclusions. Six unbiased experts approved the model's desirability. The suggested approach can serve as a valuable guide for all project stakeholders in identifying, evaluating and taking corrective actions in similar projects.

Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages.

As supplying adequate blood in multiple countries has failed due to the Covid-19 pandemic, the importance of redesigning a sensible protective-resilience blood supply chain is underscored. The outbreak-as an extensive disruption-has caused a delay in ordering and delivering blood and its by-products, which leads to severe social and financial loss to healthcare organizations. This paper presents a robust multi-phase optimization approach to model a blood supply network ensuring blood is collected efficiently. We evaluate the effectiveness of the model using real-world data from two mechanisms. Firstly, a Geographic Information System (GIS)-based method is presented to find potential alternative locations for blood donation centers to maximize availability, accessibility, and proximity to blood donors. Then, a protective mathematical model is developed with the incorporation of (a) blood perishability, (b) efficient collation centers, (c) multiple-source of suppliers, (d) back-up centers, (e) capacity limitation, and (f) uncertain demand. Emergency back-up for laboratory centers to supplement and offset the processing plants against the possible disorders is applied in a two-stage stochastic robust optimization model to maximize the level of hospitals' coverage. The results highlight the fraction cost of considering back-up facilities in the total costs and provide more resilient decisions with lower risks by examining resource limitations.

Green dynamic multimodal logistics network design problem considering financing decisions: a case study of cement logistics.

Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment-based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε-constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.

Developing a Multi-Criteria Decision Making approach to compare types of classroom furniture considering mismatches for anthropometric measures of university students.

The mismatch between students' anthropometric measures and school furniture dimensions have been investigated in many countries. In Iran, collegians spend at least a quarter of the day hours at university in the sitting position, so it is essential to evaluate furniture mismatch among university students. In Iranian universities, the use of chairs with an attached table is widespread, while the study of mismatches in these chairs among the collegian community is rare. This study was aimed to compare and rank different classroom furniture types based on the mismatch between collegians' anthropometric measures and the dimensions of classroom furniture among Industrial Engineering students by developing a Multi-Criteria Decision Making approach in an integrated Methodology. The sample consisted of 111 participants (71 males, 40 females). Ten anthropometric measures were gathered, together with eight furniture dimensions for four types of chairs. Mismatch analyses were carried out using mismatch equations, and the Simple Additive Weighting method was used as a base method to solve the decision-making problem. The results indicated that Underneath Desk Height and Seat to Desk Clearance showed the highest levels of the match, while Seat Width presents the highest levels of low mismatch. According to the results, Type 1 and Type 3 were the best current classroom furniture. The Sensitivity Analysis was performed in two ways: changing the weights of criteria in nine scenarios and comparing the results with five other MCDM methods. The proposed MCDM approach can be used widely in furniture procurement processes and educational environments.

Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set.

The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPSS21 software. Fields have been converted into discrete fields and finally the APRIORI algorithm has been used to analyze and explore the unknown patterns. After the rule extraction, experts in the field of breast cancer eliminated redundant and meaningless relations. 100 association rules with a confidence value of more than 0.9 explored by the APRIORI algorithm and after the clinical expert feedback, 10 clinically meaningful relations have been detected and reported. Due to the high number of risk factors, the use of data mining is effective for cancer data. These patterns provide the future study hypotheses of specific clinical studies.