Purpose Creating green ports, while observing international and international standards and maritime conventions and regulations and moving toward smart ports, can increase the speed of goods transfer, enable the tracking of ships and goods, increase the transparency of statistics, increase the quality and capacity of ports and reduce costs. Hence, the purpose of this study the development and evaluation of ports play a key role in their commercial success. Development policies can be formulated for these ports by properly evaluating their performance indicators. On the other hand, traditional methods of performance evaluation cannot provide a good multidimensional evaluation of the status of ports. Design/methodology/approach More than 90% of the world’s heavy transit today is carried out by the sea. With this volume of freight, transit accidents are inevitable for ships passing through oceans, seas, waterways, rivers, ports and mooring at docks. Besides, gases from ships’ fuels at sea, especially in ports, oil spills due to maritime incidents, the negligence of the ship’s crew, the use of port equipment, dirty fuel of diesel power substations, etc., have increased greenhouse gases, polluted the environment and endangered human lives. Findings A new approach has been introduced in the field of port performance evaluation based on the components of greenness and intelligence. This approach performs evaluations in two stages and a network. In this study, the performance of 11 Iranian ports was evaluated based on the network data envelopment analysis approach in 2 stages of greenness and intelligence during 4 years. The results indicated that only 5% of the ports meet the standards of intelligence and greenness. Originality/value On the other hand, as shown in the above studies, the issue of green ports is directly related to the development of animal and plant ecosystems in the seas and the environment around ports. The presence of pollution in the ports has caused the animal and plant habitats around the ports to face a complete pollution crisis or to be completely destroyed. Therefore, the development of green port concepts in third world countries will help prevent environmental pollution of the seas. Therefore, it is necessary for ports to review their strategic maritime transport model and use the development of green port indicators in their implementation processes. Therefore, the strategic development of green ports is created to create and benefit from the components of intelligence, and as mentioned in previous research, intelligence and greenness are in line and the lack of development of one of the concepts causes defects in others. According to reports provided in Iran’s maritime transport systems, most accidents have led to environmental disasters during the absence of intelligent equipment. The use of smart technologies prevents all environmental damage and the development of port services. On the other hand, in evaluating the published articles in the field of development of green and smart ports, so far, the components of intelligence and greenness have not been evaluated and analyzed in a practical and operational way in ports and only the influencing the development of agents on each other has been done (Chen, 2019). Therefore, evaluating the efficiency of ports based on green components and intelligence causes ports to fundamentally review their executive infrastructure and take an active part in the global green development plan.

The present research offeres a model to the advantage of operations for the food reverse supply chain by perfor-mancing Industry 4.0 Revolutions model of expanding a fuzzy multi-phase model for the food waste gathering reverse supply chain. This study introduces, a household waste recycling machine, which symbolizes the Industry 4.0 Revolutions. Also, electric-type vehicles have been considered for collection and delivery in accordance with the Industry 4.0 Revolutions. The rate of technology has been described in recycling stations. Several methods with different technologies to recycle food waste have been selected and assessed based on the Industry 4.0 Revolutions indicators. The food wastes are sent to recycling stations, that is places maintained, operated or used to store, buy or sell wastes before they recycled with appropriate technology. The understudy model is multi-objective, maximizing the benefit of recycling and customer response and minimizing the adverse effects of environmental pollution and transportation costs. In this research, the whale optimization algorithm is applied. The present work proposes an end-to-end solution for Reverse Supply Chain Management for food waste based on the Industry 4.0 Revolutions.

<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.

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.