In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determine More
In this paper, a bi-objective pharmaceutical supply chain network under uncertainty demand and transportation costs is modeled and developed. To control the uncertainty parameters, the robust optimization method is considering. The main objective of this paper determines the number and location of potential facilities such as drug manufacture centers and drug distribution centers by considering the minimizing the total costs and minimizing the maximum unsatisfied demand for distribution of drugs to demand zones. In this paper, production time, delivery, and drug perishable time is also considered in modeling. To solve the model, an example is designed and the multi-decision TH method is used. The results obtained the model shows this method is effective in finding the Pareto front at the right time. The results obtained the model shows this method is effective in finding the Pareto front at the right time.The results obtained the model shows this method is effective in finding the Pareto front at the right time.
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The classic overall profit needs precise information of inputs, outputs, inputs and outputs price vectors. In real word, all data are not certain. Therefore, in this case, stochastic and fuzzy methods use for measuring overall profit efficiency. These methods require mo More
The classic overall profit needs precise information of inputs, outputs, inputs and outputs price vectors. In real word, all data are not certain. Therefore, in this case, stochastic and fuzzy methods use for measuring overall profit efficiency. These methods require more information about the data such as probability distribution function or data membership function, which in some cases may not have sufficient information to estimate them, and only we have knowledge about the parameters so that they change in a convex space that is closed and bounded. Therefore, in this paper, we consider a budget uncertainty model in the robust optimization problem that able to adjust the conservative degree. The robust model by the input and output price vectors is proposed to compute overall profit efficiency measure. To illustrate the application of the proposed method, a numerical example is presented and the results show that the robust overall efficiency of the decision making units is higher than the optimistic model.
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Uncertainty is a prominent feature of real world problems and more especially financialmarkets; with this in mind, dealing with uncertainty becomes a necessary part of performanceevaluation by means of data envelopment analysis. This paper presents three robust dataenve More
Uncertainty is a prominent feature of real world problems and more especially financialmarkets; with this in mind, dealing with uncertainty becomes a necessary part of performanceevaluation by means of data envelopment analysis. This paper presents three robust dataenvelopment analysis (DEA) models and their application for performance evaluation inTehran Stock Exchange (TSE). Based on the results, the evaluated performance of stocks andthe number of efficient stocks is decreased in all three models by increasing the level ofuncertainty.
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Background and Objective: Environmental pollution and dependency on fossil fuels are the most important incentives for the development of biofuels in Iran. Microalgae are introduced as one of the best raw materials for the production of biofuels in the world. In additio More
Background and Objective: Environmental pollution and dependency on fossil fuels are the most important incentives for the development of biofuels in Iran. Microalgae are introduced as one of the best raw materials for the production of biofuels in the world. In addition, for the production of microalgae, the emissions from power plants are used which leads to the reduction of greenhouse gas emissions to the atmosphere. Method: This paper proposes a microalgae-based biofuel supply chain network design model to study the development of such fuels. First, a deterministic model was developed to model the all activities of the supply chain including provision of raw materials for the production of microalgae, microalgae cultivation, turning them into biofuel and eventually biofuel distribution. Then, the deterministic model was extended to a robust network design model to achieve a safe and stable supply chain decisions in the face of uncertainty. Findings: Results of using the proposed model for the development of microalgal biofuel production show that the cost biofuel production from microalgae is 88.5 thousand Rials per liter. Discussion and Conclusion: Current production cost of microalgae-based biofuel cannot compete with that of fossil fuel, but the cost can be significantly decreased with a slight increase in algae productivity or oil content in future.
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Climate change and environmental impacts of economic activities, have forced supply chains to implement green policies and reduce environmental impacts and destruction to achieve competiete advantage. One approach to achive simultaneously to the economic and envitonment More
Climate change and environmental impacts of economic activities, have forced supply chains to implement green policies and reduce environmental impacts and destruction to achieve competiete advantage. One approach to achive simultaneously to the economic and envitonmental objectives is to design closed loop supply chain networks (CLSCN) that integrate reverse logistics into their forward paths. In this paper, a bi-objective mixed integer linear programming model was developed for the CLSCN problem. The first objective is to minimize the cost function and the second objective function tries to minimize the time of transferring products from manufacturers to the distributors. Lp Metric and ε -constraint methods were utilized to solve the model. A numerical example was presented to show the applicability of the model and also sensitivity analysis was done. In this model two parameters of cost and demand are uncertain, in order to deal with uncertain parameters a robust optimization approach was utilizec. Multi objective particle swarm optimization (MOPSO) was used to solve the model in lare scales ad the solutions were compared with the solutions that obtained by exact methods. the findings of this research can help decision makers and executives to design efficient closed loop supply chains.
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