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The principal concern of organization managers in the global rivalry of commerce environment is how to select the project portfolio among available projects. In this matter, organizations should consider the uncertainty intrinsic in the projects regarding an appropriate valuation technique within an optimization framework. In this research, the purpose of this paper is to formulate using a robust optimization algorithm to deal with the complexities and uncertainty inherent in the construction of the project portfolio.

First, a general mathematical formulation is presented, which in compound real options valuation is highlighted. This formulation gives managerial flexibility by correcting the deficiency of traditional discounted cash flow technique that excludes any form of flexibility. Then, considering a limitation on budget of the organization, an integer programming formulation to maximize the n-fold compound options for project portfolio selection is proposed. Finally, a robust optimization model is developed along with the robust combinatorial optimization algorithm, which is effective for solving problems under uncertainty.

Sensitivity analysis showed that projects in later phases of development, having survived several phases of pre-clinical and clinical tests, are worth more because they are more likely to pertain to business. However, the investment costs related to each project during development phases limit the number of projects that a company can bring to their final portfolio. Additionally, the analysis of conservatism level represented how project managers can quite easily determine their risk attitude and the corresponding portfolio composition. From a managerial point of view, the proposed framework is very useful because it requires only financial estimates. Hence, the proposed decision support tool can assist research and development (R&D) project managers in the pharmaceutical industry for making decisions.

The first is the application of the n-fold compound options on portfolio of R&D projects and the employment of compound options value of a project portfolio as an objective function. The second one is a mathematical formulation of these concepts and solving it by the robust combinatorial optimization algorithm. The literature is lacking in the application of the robust combinatorial optimization algorithm to R&D project portfolio selection based on the generalized n-fold compound option model of Cassimon et al. (2004). Every framework from calculation of the n-fold compound option to solving robust combinatorial algorithm is programmed in Matlab software, since it can be used as a business support tool.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

The main purpose is to minimize the total inventory cost of chain, whereas the stochastic constraints are satisfied. In other words, the goal is to find optimum agreed stockpiles and period length for products to minimize the total inventory cost of the chain while the stochastic constraints are fulfilled.

This paper designs and optimizes an integrated inventory model in a four-echelon supply chain that contains a supplier, a producer, a wholesaler and multiple retailers. All four levels agree with each other to make an integrated inventory system. Products in this model have a multi-stage production process, and the model is bounded by multiple stochastic constraints. The problem model is nonlinear and large. So, the interior point method as an effective algorithm is used for solving the recent convex nonlinear model. Two numerical examples are solved to demonstrate the application of this methodology and to evaluate the performance of the proposed approach.

The findings showed the model is applicable for real-world supply chain problems in the cases that echelons are going to do executive external integration. Also, the Interior Point algorithm has a satisfactory performance and a high efficiency in terms of optimum solution for solving nonlinear and large models.

The authors designed and optimized the inventory cost in a four-level integrated supply chain in stochastic conditions. The new decision variables, number of chain levels, multi-products, stochastic constraints and multi-stage products in four-level integrated supply chain are other novelties of this paper. The authors provided an efficient algorithm for solving a large-scale and nonlinear model in this research, too.

The Russia–Ukraine war has disrupted the wheat supply worldwide. Given that wheat is one of the most important agri-food products in the world, it is necessary to pay attention to the wheat supply chain during the global crises. The use of resilience strategies is one of the solutions to face the supply chain disruptions. In addition, there is a possibility of multiple crises occurring in global societies simultaneously.

In this research, the resilience strategies of backup suppliers (BS) and inventory pre-prepositioning (IP) were discussed in order to cope with the wheat supply chain disruptions. Furthermore, the p-Robust Scenario-based Stochastic Programming (PRSSP) approach was used to optimize the wheat supply chain under conditions of disruptions from two perspectives, feasibility and optimality.

After implementing the problem of a real case in Iran, the results showed that the use of resilience strategy reduced costs by 9.33%. It was also found that if resilience strategies were used, system's flexibility and decision-making power increased. Besides, the results indicated that if resilience strategies were used and another crisis like the COVID-19 pandemic occurred, supply chain costs would increase less than when resilience strategies were not used.

In this study, the design of the wheat supply chain was discussed according to the wheat supply disruptions due to the Russia–Ukraine war and its implementation on a real case. In the following, various resilience strategies were used to cope with the wheat supply chain disruptions. Finally, the effect of the COVID-19 pandemic on the wheat supply chain in the conditions of disruptions caused by the Russia–Ukraine war was investigated.