A dynamic free-entry oligopoly with sluggish entry and exit adjustments

We study a dynamic free-entry oligopoly with sluggish entry and exit of firms under general demand and cost functions. We show that the number of firms in a steady-state open-loop solution for a dynamic free-entry oligopoly is smaller than that at static equilibrium and that the number of firms in a steady-state memoryless closed-loop solution is larger than that in an open-loop solution.     

基于利他偏好的电商供应链最优决策与契约协调

构建了由一个占主导地位的电商平台和一个处于跟随地位的制造商组成的Stackelberg主从博弈模型,研究了电商平台有无利他偏好时电商供应链的最优决策和契约协调问题,并通过数值算例验证了本文的主要结论.研究表明:电商平台的利他偏好行为能够促使自身服务水平提高、正向影响制造商的最优销售价格并削弱自身利润.但电商平台的让利行为能够提高制造商的利润水平、缓和供应链冲突并改善供应链整体绩效."销售佣金比例+服务成本共担"契约能够完美的协调电商供应链,使双方最优利润获得帕累托改进,从而保证电商平台有足够的激励执行利他偏好行为.另外,进一步分析发现电商平台的利他偏好正向影响制造商支付给电商平台的固定技术服务费、制造商占有的电商供应链利润份额和服务成本分担比例,负向影响自身的销售佣金比例.     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

多集值信息表中的多粒度模糊决策论粗糙集

多粒度粗糙集和决策论粗糙集是Pawlak粗糙集的重要推广,目前已成为人工智能研究的热点.然而,它们大多处理的都是单值信息系统中的问题.而实际生活中绝大多数都是处理多值问题,为了解决这一问题,在多集值信息表中将多粒粗糙集与模糊决策论粗糙集相结合进行研究,提出了其在乐观,悲观情形下的上下近似,研究了一些相关性质并给出了多集值信息表中的多粒度模糊决策论粗糙集精度、粗度的概念,最后通过一个具体例子验证其有效性.