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991.
In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model. 相似文献
992.
Considering the inherent connection between supplier selection and inventory management in supply chain networks, this article presents a multi-period inventory lot-sizing model for a single product in a serial supply chain, where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage. The demand is known and may change from period to period. The stages of this production–distribution serial structure correspond to inventory locations. The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility, and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers. The problem is modeled as a time-expanded transshipment network, which is defined by the nodes and arcs that can be reached by feasible material flows. A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time. The proposed model minimizes the total variable cost, including purchasing, production, inventory, and transportation costs. The model can be linearized for certain types of cost structures. In addition, two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time. 相似文献
993.
Junhui Wang 《Journal of computational and graphical statistics》2013,22(1):178-192
The boosting algorithm is one of the most successful binary classification techniques due to its relative immunity to overfitting and flexible implementation. Several attempts have been made to extend the binary boosting algorithm to multiclass classification. In this article, a novel cost-sensitive multiclass boosting algorithm is proposed that naturally extends the popular binary AdaBoost algorithm and admits unequal misclassification costs. The proposed multiclass boosting algorithm achieves superior classification performance by combining weak candidate models that only need to be better than random guessing. More importantly, the proposed algorithm achieves a large margin separation of the training sample while attaining an L1-norm constraint on the model complexity. Finally, the effectiveness of the proposed algorithm is demonstrated in a number of simulated and real experiments. The supplementary files are available online, including the technical proofs, the implemented R code, and the real datasets. 相似文献
994.
《Journal of computational and graphical statistics》2013,22(3):455-463
The assignment algorithm is an old, well-known, widely implemented, fast, combinatorial algorithm for optimal matching in a bipartite graph. This note proposes a method for using the assignment algorithm to solve the problem of optimal matching with a variable number of controls, in which there is a choice not only of who to select as a control for each treated subject, but also of how many controls to have for each treated subject. The strategy uses multiple copies of treated subjects and sinks with zero cost to absorb extra controls. Also, it is shown that an optimal matching with variable numbers of controls cannot be obtained by starting with an optimal pair matching and adding the closest additional controls. An example involving mortality after surgery in Pennsylvania hospitals is used to illustrate the method. 相似文献
995.
AbstractWe study the problem of optimally liquidating a financial position in a discrete-time model with stochastic volatility and liquidity. We consider the three cases where the objective is to minimize the expectation, an expected exponential or a mean-variance criterion of the implementation cost. In the first case, the optimal solution can be fully characterized by a forward-backward system of stochastic equations depending on conditional expectations of future liquidity. In the other two cases, we derive Bellman equations from which the optimal solutions can be obtained numerically by discretizing the control space. In all three cases, we compute optimal strategies for different simulated realizations of prices, volatility and liquidity and compare the outcomes to the ones produced by the deterministic strategies of Bertsimas and Lo (1998; Optimal control of execution costs. Journal of Financial Markets, 1, 1–50) and Almgren and Chriss (2001; Optimal execution of portfolio transactions. Journal of Risk, 3, 5–33). 相似文献
996.
Éva Gyurkovics Dietmar Meyer Tibor Takács 《Mathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences》2013,19(2):179-192
A two-person nonlinear dynamic game is presented to model the government's strategy to decrease the budget deficit, where Player 1 is the government using fiscal control and Player 2 represents the private sector. In our macroeconomic model the growth rate of the labour force is not known, but its lower and upper bounds are given a priori. This means that the system is uncertain, which makes the determination of an optimal solution (in a Nash, Stackelberg, etc. sense) impossible. Therefore, only a guaranteeing cost control is determined for Player 1. It is shown that the balancing by a guaranteeing cost control is possible even in the most unfavourable situation, when the governmental debt is higher and the volume of fixed capital stock is lower than the equilibrium value. 相似文献
997.
998.
美军提出的"以费用为独立变量(CAIV)"方法从论证阶段开始,就强调需求、性能与费用三者之间的平衡,并采用费用-效能综合权衡优化模型进行系统费用和性能参数的最优决策.但是该模型没有考虑采办过程中的不确定因素,且由于决策者往往很关心权衡结果中费用与性能的置信水平问题,因此提出要将采办中的不确定性因素作为随机变量引入权衡模型,建立一种综合权衡优化的随机机会约束规划模型,并进行了示例验证. 相似文献
999.
A bivariate optimal replacement policy for a cold standby repairable system with preventive repair 总被引:1,自引:0,他引:1
In this paper, the repair-replacement problem for a deteriorating cold standby repairable system is investigated. The system consists of two dissimilar components, in which component 1 is the main component with use priority and component 2 is a supplementary component. In order to extend the working time and economize the running cost of the system, preventive repair for component 1 is performed every time interval T, and the preventive repair is “as good as new”. As a supplementary component, component 2 is only used at the time that component 1 is under preventive repair or failure repair. Assumed that the failure repair of component 1 follows geometric process repair while the repair of component 2 is “as good as new”. A bivariate repair-replacement policy (T, N) is adopted for the system, where T is the interval length between preventive repairs, and N is the number of failures of component 1. The aim is to determine an optimal bivariate policy (T, N)∗ such that the average cost rate of the system is minimized. The explicit expression of the average cost rate is derived and the corresponding optimal bivariate policy can be determined analytically or numerically. Finally, a Gamma distributed example is given to illustrate the theoretical results for the proposed model. 相似文献
1000.