multi-objective optimization

Faced with increasing threats and challenges of supply chains, it is necessary to redesign the supplier network based on hybrid approaches based on mathematical models and reliability. To solve the problem, this paper presents a new approach. Team formation, selection, design, and composition are still critical success or failure factors in any business within a company and organization. Criteria, parameters, various qualitative and quantitative methods, approaches, and techniques have been presented by several studies in TF so far. This study developed a hybrid approach to team formation (TF) and reliable supplier network design, focusing on a multi-objective model integrating fuzzy-set theory and social network analysis. Furthermore, this study addressed the relative importance value of precise relationships between members using fuzzy logic (expert workshop and fuzzy inference), backup team, capabilities (skills, expertise, or knowledge), capacity, and order allocation. It also carefully considers the relationships between team members using expert workshops and fuzzy inferences. Also, social network analysis metrics are used to suggest team leader(s). We used the augmented epsilon constraint (AUGMECON2) method to validate the model and solve small-scale problems with exact solutions. The model aimed to form a reliable team and supplier network with a maximum level of reliability, maximize the network weight of collaboration, and maximize the knowledge level of the main members( suppliers), simultaneously. The approach was evaluated through a numerical study of the actual data of the electro-optical camera for team formation, design and selection of a network of reliable suppliers, and order allocation. The results showed that the approach carefully selects the optimal supplier network and team based on all assumptions and suggests team leaders with social network analysis. One of the advantages of our model is simultaneously considering supplier network, reliability, FIS, and SNA in team formation. The use of uncertain data and combined methods and MADM for preselection can also be eective. The strategy of the optimal number of modules and product subsystems can also be included in the model. In future studies, other variables and parameters such as time, design phases, and the total cost can be considered. Also, because the problem is NP-hard; the use of meta-heuristic algorithms is suggested. Modeling a multi-product multi-period supply chain problem is suggested.

One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the organ transplant supply chain and other supply chains is the possibility of corruption of related products. Therefore, the time and also the location of organ transplant centers are of special importance. On the other hand, due to the rapid growth of the demand for organ transplantation and the lack of resources, the patient's waiting time to complete the transplantation process plays a vital role in the organ transplantation system.

This study presents a robust bi-objective mathematical model for the location problem of allocating organ transplant centers under uncertainty, which includes the total costs of the organ transplant system as well as the average patient waiting time for organ transplantation, which follows a G/G/m queuing system.

To solve this model, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been used. Finally, the applicability of this model and the efficiency of the mentioned algorithm compared to the defined indicators have been shown through numerical experiments.

Originality/Value:

 Since each organ can spend a certain amount of time outside the body and there is a possibility of corruption or a decrease in the quality of the transplant, the time between the organ leaving the body and the completion of the transplant operation plays an essential role in the transplant system.

Current societies are obliged to make the necessary plans for effective response and reducing the destructive effects of disasters. In this research, a mathematical planning model under uncertainty has been developed for earthquake relief and response. In the presented model to increase the reliability, the possibility of facility failure is considered according to the intensity of the earthquake. Distribution centers are considered to be of two types, the first type is local distribution centers, which use public centers and are close to the accident points, these types of centers are prone to failure because they use public centers. Another type is the reliable distribution centers that are built outside the accident area and have a very low probability of failure due to spending more money to build them. In the new model presented, in addition to considering the reliability capabilities, it has been tried to provide a more complete model for planning in the transportation issue by considering the multi-trips mode in the vehicles. Uncertainty is presented using the probability approach based on the modeling scenario and a case study from the city of Tehran to show the performance of the proposed model. The results obtained from the proposed model show that effective and efficient aid delivery is done in terms of time and cost, therefore it can help crisis managers in response to the crisis in order to provide the required budget and appropriate logistics planning.

Low Noise Amplifier (LNA) is the first critical and important element in the designing of analog VLSI systems and wireless communications technology. In this paper, in order to establish an appropriate trade-off between the contradictory design objectives of an LNA circuit and the improvement of its efficiency and optimality indexes, a Computer-Aided Design (CAD) tool has been used based on the use of soft computing techniques including meta-heuristic intelligent optimization methods. For this purpose, the intelligent sizing of circuit elements and the auto-circuit design have been implemented by a new and effective Multi-Objective version of the Modified Inclined Planes System Optimization (MOMIPO) algorithm. Output results, compared with other studies, indicate a desirable achievement of design characteristics and constraints along with a variety of responses in the form of a Pareto Front. The execution of optimizer algorithm in the MATLAB environment and circuit simulations has been performed using the HSPICE software.

The Pareto set of optimal solutions resulting from solving multi-objective optimization problems, although on the one hand increases the flexibility in choosing an optimal solution according to the conditions of a system, but on the other hand, due to different tastes and perspectives in a The system, choosing the most desirable Pareto front answer, can be a serious challenge. In this regard, in this article, in the first step, by defining the concept of Gaussian degree of proximity and presenting a decomposition approach based on it, we produce the Pareto front, which numerical results show that this front in comparison with fronts obtained from other quality decomposition approaches. Has a higher. In the second step, due to the lack of an evaluation criterion that examines the quality of a front from different angles, we present a new evaluation criterion for comparing different fronts, which by considering both factors of mastery and proximity to the optimal answer. Examines the quality of the answers on a Pareto front. The results obtained from the simulation of the proposed steps on the existing standard test functions confirm the efficiency and effectiveness of each of the steps of the proposed problem.