Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty.

Population growth and recent disruptions caused by COVID-19 and many other man-made or natural disasters all around the world have considerably increased the demand for medical services, which has led to a rise in medical waste generation. The improper management of these wastes can result in a serious threat to living organisms and the environment. Designing a reverse logistics network using mathematical programming tools is an efficient and effective way to manage healthcare waste. In this regard, this paper formulates a bi-objective mixed-integer linear programming model for designing a reverse logistics network to manage healthcare waste under uncertainty and epidemic disruptions. The concept of epidemic disruptions is employed to determine the amount of waste generated in network facilities; and a Monte Carlo-based simulation approach is used for this end. The proposed model minimizes total costs and population risk, simultaneously. A fuzzy goal programming method is developed to deal with the uncertainty of the model. A simulation algorithm is developed using probabilistic distribution functions for generating data with different sizes; and then used for the evaluation of the proposed model. Finally, the efficiency of the proposed model and solution approach is confirmed using the sensitivity analysis process on the objective functions' coefficients.

A hyper-hybrid fuzzy decision-making framework for the sustainable-resilient supplier selection problem: a case study of Malaysian Palm oil industry.

One of the major challenges of the supply chain managers is to select the best suppliers among all possible ones for their business. Although the research on the supplier selection with regards to green, sustainability or resiliency criteria has been contributed by many papers, simultaneous consideration of these criteria in a fuzzy environment is rarely studied. Hence, this study proposes a fuzzy decision framework to investigate the sustainable-resilient supplier selection problem for a real case study of palm oil industry in Malaysia. Firstly, the resilient-based sustainable criteria are localized for the suppliers' performance evaluation in palm oil industry of Malaysia. Accordingly, 30 criteria in three different aspects (i.e. general, sustainable and resilient) are determined by statistical tests. Moreover, a hyper-hybrid model with the use of FDEMATEL (fuzzy decision-making trial and evaluation laboratory), FBWM (fuzzy best worst method), FANP (fuzzy analytical network process) and FIS (fuzzy inference system), simultaneously is developed to employ their merits in an efficient way. In this framework, regarding the outset, the relationships among the criteria/sub-criteria are obtained by FDEMATEL method. Then, initial weights of the criteria/sub-criteria are measured by FBWM method. Next, the final weights of criteria/sub-criteria considering the interrelationships are calculated by FANP. Finally, the performance of the suppliers is evaluated by FIS method. To show the applicability of this hybrid decision-making framework, an industrial case of palm oil in Malaysia is presented. The findings indicate the high performance of the proposed framework in this concept and identify the most important criteria including the cost in general aspects, resource consumption as the most crucial sustainable criterion and agility as the most important resilient criterion.

An Efficient Taguchi Approach for the Performance Optimization of Health, Safety, Environment and Ergonomics in Generation Companies.

BACKGROUND: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. METHODS: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. RESULTS: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. CONCLUSION: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.