Solving an EOQ model under fuzzy reasoning

Robust design of a green-responsive closed-loop supply chain network for the ventilator device

This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.

The green-agile supplier selection problem for the medical devices: a hybrid fuzzy decision-making approach

The supplier selection problem (SSP) is known as one of the major issues in the supply chain management area. In this field, the literature shows that the combination of green and agile indicators has been ignored by researchers. Hence, this research attempts to study the SSP considering green and agile aspects, simultaneously. To do this, an efficient hybrid fuzzy decision-making approach is developed based on the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL), Fuzzy Best-Worst Method (FBWM), Fuzzy Analytic Network Process (FANP), and Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (FVIKOR) methods. Then, to show the efficiency and application of the proposed approach, a case study in the medical devices industry is investigated. After determining the main indicators and alternatives, the interrelationships between indicators are identified employing FDEMATEL. Then, the weights of indicators are calculated using integrated FBWM-FANP. Finally, the potential suppliers are ranked applying FVIKOR. Based on the obtained results, price and greenness are the more important aspects and also material costs, environmental performance evaluation, manufacture flexibility, service level, and system reliability are the most important criteria for the green-agile supplier selection problem in the medical devices industry. Since all of the consistency ratios are less than 0.1, the reliability of the results is proved. On the other side, the results of conducting sensitivity analysis show that by changing the defuzzification methods, there is no significant change in the obtained results that demonstrates the validity of the proposed approach. Eventually, based on the obtained results, suppliers #1 and #5 are the best suppliers for the considered company.

An integrated approach for a sustainable supplier selection based on Industry 4.0 concept

The recent advances in sustainable supply chain management are integrated with Industry 4.0 concepts. This study develops a new integrated model to consider the sustainability and Industry 4.0 criteria for the supplier selection management. The proposed approach consists of the fuzzy best worst method (FBWM) and the two-stage fuzzy inference system (FIS) to assess the selection of suppliers. Firstly, this study determines a comprehensive list of Industry 4.0 and sustainability criteria along with their definitions. Then, the importance weight of each criterion is computed by the FBWM. Subsequently, a two-stage FIS is devoted to nominate the suppliers' performance with regard to the sustainability and Industry 4.0 criteria. To show the applicability of our integrated model, a case study for a textile company in Iran is provided. Finally, some sensitivity analyses are done to assess the efficiency of the proposed integrated approach. One finding is to establish a decision-making framework to evaluate suppliers separately, rather than relatively in a fuzzy environment using Industry 4.0 and sustainability criteria.