In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and ...In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.展开更多
The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understand...The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understanding its function.The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm.The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction.To predict the subcellular location of eukaryotic protein,a systematic prediction approach comprised of a novel feature extraction method,an idea of combining this feature extraction method with support vector machine(SVM) algorithm,and ’one-versus-rest’ & ’all-versus-all’ strategies have been proposed in this paper.Consequently,the total predictive accuracies reach 95.5% for four locations.Compared with existing methods,this new approach provides better predictive performance.For example,it is 13.5%,5.1% higher than Yuan’s and Hua’s methods respectively.These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location.It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features.展开更多
基金supported by the Science Challenge Project(TZ2018007)the National Natural Science Foundation of China(71671009+2 种基金 61871013 61573041 61573043)
文摘In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.
文摘The rapidly increasing number of sequences entering into the genome databank has created the need for fully automated methods to analyze them.Knowing the cellular location of a protein is a key step towards understanding its function.The development in statistical prediction of protein attributes generally consists of two cores: one is to construct a training dataset and the other is to formulate a predictive algorithm.The latter can be further separated into two subcores: one is how to give a mathematical expression to effectively represent a protein and the other is how to find a powerful algorithm to accurately perform the prediction.To predict the subcellular location of eukaryotic protein,a systematic prediction approach comprised of a novel feature extraction method,an idea of combining this feature extraction method with support vector machine(SVM) algorithm,and ’one-versus-rest’ & ’all-versus-all’ strategies have been proposed in this paper.Consequently,the total predictive accuracies reach 95.5% for four locations.Compared with existing methods,this new approach provides better predictive performance.For example,it is 13.5%,5.1% higher than Yuan’s and Hua’s methods respectively.These results demonstrate the applicability of this new method and concept and possible improvement of prediction for the protein subcellular location.It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features.
