Abstract Sustainability has become a significant subject in most economies, causing many leading organizations to focus on product recovery and reverse logistics. However, work with quantitative models is still rare. This research is focused on inventory control in the remanufacturing process. In the model presented in this paper, product demand can be carried out by remanufactured and new items to minimize the... Sustainability has become a significant subject in most economies, causing many leading organizations to focus on product recovery and reverse logistics. However, work with quantitative models is still rare. This research is focused on inventory control in the remanufacturing process. In the model presented in this paper, product demand can be carried out by remanufactured and new items to minimize the use of raw materials. In this regard, the basic assumption is that the remanufactured products are as good as new products, and the recurring items from customers can be remanufactured at a fixed rate. In this study, a method has been developed to model the reverse supply chain variables. An approach is used for the calculation of optimum level for the new items and the optimum level of the returned items for remanufacturing together. The major objective is to minimize the waste and gain the competitive advantage of the cost of conversion. From among the advantages of using the method, we may refer to the simultaneous calculation of optimized level of newly produced items, and optimized level of returning items for remanufacturing with the aim of minimizing wastes, and achieving competitive advantage of transformation cost. From the model analysis, it can be concluded that the cost of ordering new products, the capacity of the remanufacturing process, the final product demand, the maintenance costs of returning items inventory for remanufacturing, and the maintenance cost of new product inventory for should be at a low level. Read More Read Less

Abstract In this paper, we investigate the single-valued neutrosophic multiple attribute decision making (MADM) problems with preference information on alternatives, some operational laws of single-valued neutrosophic numbers, score function where accuracy function of single-valued neutrosophic numbers are introduced. An optimization model based on the minimum deviation method, by which the attribute weights can be determined, is established. For the special situations where the... In this paper, we investigate the single-valued neutrosophic multiple attribute decision making (MADM) problems with preference information on alternatives, some operational laws of single-valued neutrosophic numbers, score function where accuracy function of single-valued neutrosophic numbers are introduced. An optimization model based on the minimum deviation method, by which the attribute weights can be determined, is established. For the special situations where the information about attribute weights is completely unknown, we establish another optimization model. By solving this model, we get a simple and exact formula, which can be used to determine the attribute weights. We utilize the singlevalued neutrosophic weighted averaging (SVNWA) operator to aggregate the single-valued neutrosophic information corresponding to each alternative, and then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. The method can sufficiently utilize the objective information, and meet decision makers’ subjective preference and can be easily performed on computer. Furthermore, we have extended the above results to interval neutrosophic environment. Finally, an illustrative example for potential evaluation of emerging technology commercialization is given to verify the developed approach and to demonstrate its practicality and effectiveness. Read More Read Less

Abstract The parameters of the PID controller directly affect the stability and the response speed of the controlled system. As traditional swarm based parameter optimization algorithms have the problems of high computational complexity, strong parameter dependence and weak global optimization ability, a multiple repellents reverse driving based fruit fly optimization algorithm is proposed. The concept of multiple repellents is proposed based on the theory... The parameters of the PID controller directly affect the stability and the response speed of the controlled system. As traditional swarm based parameter optimization algorithms have the problems of high computational complexity, strong parameter dependence and weak global optimization ability, a multiple repellents reverse driving based fruit fly optimization algorithm is proposed. The concept of multiple repellents is proposed based on the theory of bacterial chemotaxis. Moreover, the advantages of traditional fruit fly algorithms on convergence speed are combined, and a single attractant based attraction operation is used to update the positions of the fruit flies. At the end of the iterations, the multiple repellents based reverse driving operation is proposed to solve the invalid global searching problem of traditional algorithms. The experimental results on the standard functions and the PID controller show that, the performance of the proposed method is improved when compared to traditional algorithms. The optimized PID controller has fast response speed and high stability, showing the ability of the proposed algorithm on PID parameter optimization. Read More Read Less