uncertainty

Ensuring an adequate and healthy blood supply is a persistent challenge that healthcare systems worldwide face. The need for blood donors and their products is constant, while the supply from donors is somewhat irregular, and the demand for these products is often unpredictable. Furthermore, the levels of demand and blood donation are uncertain. As a result, uncertainty plays a crucial role in the blood supply chain, especially during crises such as earthquakes and pandemics. In this regard, designing the  Blood Supply Chain Network (BSCN) under uncertainty is essential for meeting fluctuating demand, addressing logistical challenges, responding to emergencies, and ensuring the quality and safety of blood products throughout the supply chain. This research aims to present a Mixed-Integer Linear Programming (MIP) model under uncertainty for strategic and tactical decision-making in the blood supply chain over a determined planning horizon. The fuzzy theory approach has been used to incorporate uncertainty into the model's parameters. An interactive fuzzy solution approach based on credibility measurement has been developed to solve the fuzzy optimization model. The results obtained from designing and implementing the proposed model in a case study indicate the desirable efficiency of this model in determining the optimal number and location of facilities in a BSCN, including fixed facilities, temporary facilities, and blood banks, as well as the optimal amount of blood transfer between different entities of the blood supply chain. Furthermore, a sensitivity analysis of the parameters is performed to determine the most influential factors affecting the objective function of the problem.

In this article, a health service delivery model based on the Internet of Things (IoT) under uncertainty is presented. The considered model includes a set of patients, doctors, vehicles, and services that should be provided in the shortest time and cost. The most important decisions of the network include the allocation of specialist doctors to patients, the routing of vehicles, and doctors to provide health services. The dataset of the problem has been provided to the hospital and centers using IoT tools and an integration framework has been designed for this problem. The results of solving the numerical examples show that to reduce the service delivery time and the distance traveled by vehicles, the design costs of the model should be increased. Also, the increase in the rate of uncertainty during service delivery leads to an increase in total costs in the health system. In this article, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-objective imperialist Competitive algorithm (MOICA) were proposed to solve the model, and the results showed that the proposed methods are more efficient than the exact methods. These algorithms have achieved close to optimal results in the shortest possible time. Also, the calculation results in large numerical examples show the high efficiency of the MOICA.

This study optimizes the multi-commodity routing problem in a constrained network, integrating dynamic warehouse management, diverse vehicle ownership options, and congestion management. The model addresses the efficient routing of goods with limited vehicle and warehouse capacities, enabling the addition or removal of warehouses based on demand fluctuations. It incorporates a hybrid fleet strategy, balancing owned and outsourced vehicles to minimize costs while ensuring flexibility. The model also considers network congestion, optimizing routes and schedules to mitigate delays. This approach provides a comprehensive solution for cost-effective and responsive supply chain logistics. In this research, the complexity of the mathematical model and its multi-objective nature led to the use of the epsilon constraint method and the MOGWO and NSGA II algorithms in the model. Solving the model using the mentioned methods showed that the total costs increased with the improvement of the second objective function. This problem has been due to the use of vehicles with higher speeds and higher prices, and also by reducing the risk of transporting products, the total costs have increased again.

The hydrocarbon supply chain (HCSC) is integral to the world economy. This chain includes petroleum products extraction, refinement, distribution, and consumption. Considering the importance of planning HCSC for the chain’s activities, it is necessary to simultaneously review and optimize these activities by incorporating important and influential factors. The problem considered in this research has three objectives: 1) maximizing profits, 2) minimizing withdrawal from reservoirs, and 3) minimizing greenhouse gas emissions. The results demonstrated that the profit level in a specific time (10 periods) improved by 18% compared to the current point. In addition, a numerical example was used to simulate distribution, refinement, and extraction locations as a supply chain for petroleum products. Finally, the sensitivity analysis revealed that the optimization results are robust to parameter changes and can further improve the optimization of the oil and gas supply chain by maintaining different balances (e.g., natural resources) and reducing environmental effects. Interactive fuzzy programming based on credibility criteria was applied to address the parameter uncertainty. Further, to reduce the problem’s computational complexity and produce valid and reliable optimal Pareto cuts, the Benders decomposition method has been employed, which has led to the production of efficient solutions.

In decision-making contexts marked by uncertainty, the application of fuzzy numbers has emerged as a crucial tool. These numbers offer a mathematical framework for representing imprecise information, enabling a more nuanced approach to decision-making. Fuzzy numbers find widespread application in quantifying the inherent uncertainty present in decision-making contexts. When incorporating fuzzy numbers into decision-making procedures, the necessity to compare these fuzzy numbers becomes an unavoidable occurrence. Ranking fuzzy numbers is a challenging topic. In this paper, we propose a new method for ranking generalized fuzzy numbers based on the center of the area concept. First, we present the concepts of the presented method. Additionally, the proposed method can rank symmetric fuzzy numbers relative to the y-axis easily. Then the advantages of the proposed method are illustrated through several numerical examples. The results demonstrate that this approach is effective for ranking generalized fuzzy numbers and overcomes the shortcomings in recent studies. Finally, we checked the result of the presented method with other existing methods. The results show that the presented method has consistent results with less computational complexity.