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<p style='text-indent:20px;'>According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly significant and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of distribution of products in the supply chain by considering disruption has received more attention recently. In this study a multi-objective mixed-integer linear programming model is developed for a green multi-echelon closed-loop supply chain network design under uncertainty. Moreover, a partial disruption is considered for distribution centers where has not been studied enough in previous works. The fuzzy credibility constraint approach is applied to cover uncertainty. In the following, the ε-constraint method is presented to solve and validate the model in small-sized instances. Moreover, a Non-dominated Sorting Genetic Algorithm is developed for solving the large-sized problems. Results show that uncertainty has a crucial impact on objective functions where the increase of objective functions by increasing the level of uncertainty, which was observed in all categories. Furthermore, the proposed NSGA-Ⅱ is the best tool to deal with large-size problems where the EC method lacks the necessary efficiency.</p>

The required processes of supply chain management include optimal strategic, tactical, and operational decisions, all of which have important economic and environmental effects. In this regard, efficient supply chain planning for the production and distribution of perishable productsis of particular importance due to its leading role in the human food pyramid. One of the main challenges facing this chain is the time when products and goods are delivered to the customers and customer satisfaction will increase through this.In this research, a bi-objective mixed-integer linear programming (MILP)model is proposedto design a multi-level, multi-period, multi-product closed-loop supply chain (CLSC) for timely production and distribution of perishable products, taking into account the uncertainty of demand. To face the model uncertainty, the robust optimization (RO) method is utilized. Moreover, to solve and validate the bi-objective model in small-size problems, the -constraint method (EC) is presented. On the other hand, a Non-dominated Sorting Genetic Algorithm (NSGA-II) is developed for solving large-size problems. First, the deterministic and robust models are compared by applying the suggested solutions methods in a small-size problem, and then,the proposed solution methods are compared in large-size problems in terms of different well-known metrics. According to the comparison, the proposed model has an acceptable performance in providing the optimal solutions and the proposed algorithm obtains efficient solutions.Finally, managerial insights are proposed using sensitivity analysis of important parameters of the problem.

Integration voice of customer into new product development (NPD) process is an important factor that creates more customer satisfaction. Quality function deployment (QFD) is widely used to capture customer requirements (CRs) which are real by focusing on customers' needs. It helps organizations to translate CRs to the major design requirements (DRs) in such a way that they can develop better products which meet their customers' needs. In most of existing literature, traditional QFD discovers the needs of customers and fulfill them. Yet research and development team can improve QFD tool by adding need creation item so organizations can provide suitable products that cover customers' hidden needs and delight them. This paper presents a new approach that develops quality function deployment through the house of quality matrix expansion. The main innovative idea of the method is the extracting new design requirements (NDRs) without considering CRs in QFD. Analytic network process (ANP) is used to determine relations and correlations between QFD components. The Zero One Goal Programming (ZOGP) is employed in order to prioritize DRs and NDRs by considering additional factors such as manpower constraint, cost budget, extendibility and manufacturability level. Finally the effectiveness of proposed method is illustrated by provided case study.

The provision of medical equipment during pandemics is one of the most crucial issues to be dealt with by health managers. This issue has revealed itself in the context of the COVID-19 outbreak in many hospitals and medical centers. Excessive demand for ventilators has led to a shortage of this equipment in several medical centers. Therefore, planning to manage critical hospital equipment and transfer the equipment between different hospitals in the event of a pandemic can be used as a quick fix. In this paper, a multi-objective optimization model is proposed to deal with the problem of hub network design to manage the distribution of hospital equipment in the face of epidemic diseases such as Covid-19. The objective functions of the model include minimizing transfer costs, minimizing the destructive environmental effects of transportation, and minimizing the delivery time of equipment between hospitals. Since it is difficult to estimate the demand, especially in the conditions of disease outbreaks, this parameter is considered a scenario-based one under uncertain conditions. To evaluate the performance of the proposed model, a case study in the eastern region of Iran is investigated and sensitivity analysis is performed on the model outputs. The sensitivity of the model to changing the cost parameters related to building infrastructure between hubs and also vehicle capacity is analyzed too. The results revealed that the proposed model can produce justified and optimal global solutions and, therefore, can solve real-world problems.