Abolfazl Babazadeh Rafiei; Majid Motamedi; Sohrabi Tahmoores; Mohammad Hossein Darvish Motevalli
Abstract
Supply chain risk management is a preventive approach to risk management in the supply chain to avoid possible unex-pected consequences and to manage the blood supply chain (BSC) and achieve the maximum effectiveness and efficiency of this chain, risk management of the BSC is inevitable. This research ...
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Supply chain risk management is a preventive approach to risk management in the supply chain to avoid possible unex-pected consequences and to manage the blood supply chain (BSC) and achieve the maximum effectiveness and efficiency of this chain, risk management of the BSC is inevitable. This research aims to propose a mathematical model to reduce the risk of the BSC in the conditions of the COVID-19 pandemic. One of the most important contributions of this research is to consider the uncertainty in the demand parameter in the conditions of the COVID-19 pandemic and to provide a ro-bust planning model to overcome it in order to properly manage and control its risks. For this purpose, in this research a scenario-based multi-objective model is proposed with the aim of reducing the risk of the BSC in the conditions of the COVID-19 pandemic. In order to test the model, the problem is investigated in different sizes and using actual data and the results are presented, and sensitivity analysis is carried out on the changes in the parameters. Baron solver in GAMS 24.9 software is used to solve the proposed mathematical model. The proposed model determines the product sent from the blood center to the hospital, the amount of product produced in the blood center, the amount of blood collected from donors, the number of collection centers, the amount of blood stock in the blood center and hospital with the aim of reduc-ing cost and risk and increasing reliability. In this research, a scenario-based non-linear integer multi-objective model is proposed considering the level of supply and with the aim of reducing the risk of the BSC by reducing the cost and in-creasing the reliability of the BSC in the conditions of the COVID-19 pandemic, which can be used for risk management of the BSC in critical conditions of blood supply, such as the COVID-19 pandemic. Finally, to measure the sensitivity of the presented model performance to the change in the parameters, the sensitivity analysis on the behavior of the model in terms of the change in the shortage cost, the number of blood collection facilities and the objective functions is presented. The sensitivity analysis on the shortage cost parameter showed that with the increase in the shortage cost, the shortage rate decreased and this leads to an increase in the total cost.
Morteza Karimi; Tahmoores Sohrabi; Hasan Mehrmanesh
Abstract
In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most ...
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In this study, the problem of simultaneous determination of order acceptance, scheduling and batch delivery considering sequence-dependent setup and capacity constraint has been presented. This problem is a combination of the three problems of order acceptance, scheduling and batch delivery. The most important innovation of this research is the simultaneous optimization of profits and the total weighted earliness and tardiness as two conflicting objectives in the problem of combining order, scheduling and batch delivery. Another innovation of this research is the use of multi-objective Grey Wolf Optimization (GWO) algorithm, which has not been used in studies of this field so far. It has also been shown that the multi-objective Grey Wolf Optimization algorithm is comparable to the exact solution methods. The second part of the numerical results compares the results of the ε-constraint method, NSGA-II and the multi-objective Grey Wolf Optimization algorithm. The results of this section show that by increasing the scale of the problem, the efficiency of the multi- objective Grey Wolf Optimization algorithm is better displayed, and in general, this method has a significant advantage relative to NSGA-II and ε-constraint in terms of DM, SNS and NPS indicators. Also, the solving time of this method is very shorter than that of the ε-constraint. Therefore, from a managerial point of view, a tool called the multi-objective Grey Wolf Optimization algorithm can be used as an efficient tool for supply and production managers, which is able to provide several optimal solutions with different profits, earliness and tardiness.