This study proposes a mixed-integer multi-objective integrated mathematical model solving facility location and order allocation optimization problems simultaneously in a two-echelon supply chain network.The proposed problem is motivated by a factoyless concept and, by providing a dynamic decision-making solution under a multi-period time horizon. Within the model, we also determine the optimal replenishment amounts of production facilities by the multi-objective functions. The multi-objective functions include minimization of the total cost, rejected and late delivery units and, maximization of the assessment score of the selected suppliers. The studied dynamic decision model is significant for the cost-efficient management of companies' supply chain networks. The mixed-integer mathematical model is developed by the LP-metric method and it is solved by the GAMS optimization software. Due to the NP-hard structure of the problem, for large-scale instances, we utilize the Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Vibration Damping Optimization (MOVDO) heuristic solution approaches.Numerical results show that for large-scale problems, the MOPSO method performs better in Pareto solutions and decrease run times. However, the MOVDO method performs better regarding the Mean Ideal Distance and the Number of Solutions Cover surface criterion. The developed solution approach by this paper is a generic model which can be applied for any two-level network for simultaneous optimization of supplier selection, location determination of facilities and their replenishment amounts.

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The pharmaceutical industry is one of the most critical industries in the country, and solving its problems is of great importance. In the current situation, on the one hand, many sanctions have been imposed on the country. On the other hand, with the outbreak of Covid-19, the importance of medicine for the country's health system has received more attention. Therefore, given the political and economic conditions, the development and accurate planning of the government to support the pharmaceutical industry must be given priority. Thus, this study examines and explains the role of the government and its support for the pharmaceutical industry during the Covid-19 pandemic by identifying policies to determine their effectiveness and rank. Methods: In the present applied-descriptive research, a questionnaire and interviews with experts were used. The number of experts participating in this study was ten; they were managers of different departments of pharmaceutical knowledge-based companies who were selected using purposive, non-random sampling method. Causal relationships and the effect of model variables were identified using the fuzzy DMATEL technique. Finally, the importance of each support was determined using the fuzzy ANP method and Super Decisions software. Results: Three categories of support policies and eight supports related to the influential role of the government in the development of the country's pharmaceutical industry were identified. As a result, exemptions were recognized as the most influential support policies, including customs exemption protections, tax exemptions for pharmaceutical companies, and exemptions or deferral of previous loans. Conclusion: Drug safety is one of the main goals of development policies, and ending dependence on the aid of other countries and self-sufficiency in this industry requires the formulation of special protection principles.The government can use the results obtained in this study in determining its support policies for the pharmaceutical industry in the face of sanctions and Covid-19 pandemic.

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