考虑平台竞争的众包物流社会配送服务最优定价策略

基于互联网的众包物流服务平台共享社会闲置人力资源,为电子商务O2O提供最后一公里配送服务。通过配送服务的动态定价来调控众包物流的社会配送供应能力,成为优化众包物流平台运营的重要手段。在众包物流平台激烈的竞争环境下,考虑到众包物流社会化配送供应能力的不确定性,采用动态优化理论,建立平台价格竞争下众包物流社会配送服务最优定价模型。运用汉密尔顿函数求解社会配送服务最优价格,研究众包物流社会配送服务供应能力随着价格变化的规律,进一步,分析众包物流平台竞争对最优价格变化规律的影响。数值仿真结果表明,众包物流社会配送服务价格增长率随着平台竞争的加剧而增加,可以有效调控众包物流服务的供应与需求平衡,优化众包物流平台的期望收益。     

MCMC calibration of spot‐prices models in electricity markets

The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.     

Deep learning for energy markets

Deep Learning (DL) is combined with extreme value theory (EVT) to predict peak loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose a deep temporal extreme value model to capture these effects, which predicts the tail behavior of load spikes. Deep long‐short‐term memory architectures with rectified linear unit activation functions capture trends and temporal dependencies, while EVT captures highly volatile load spikes above a prespecified threshold. To illustrate our methodology, we develop forecasting models for hourly price and demand from the PJM interconnection. The goal is to show that DL‐EVT outperforms traditional methods, both in‐ and out‐of‐sample, by capturing the observed nonlinearities in prices and demand spikes. Finally, we conclude with directions for future research.     

An exact solution procedure for multi-item two-echelon spare parts inventory control problem with batch ordering in the central warehouse

We consider a multi-item two-echelon inventory system in which the central warehouse operates under a (Q,R) policy, and the local warehouses implement basestock policy. An exact solution procedure is proposed to find the inventory control policy parameters that minimize the system-wide inventory holding and fixed ordering cost subject to an aggregate mean response time constraint at each facility.     

The improved screening curve method regarding existing units

In this study, a new method is developed to determine the least cost capacity expansion of a power system by using the screening curve method. The proposed methodology differs from the previous studies by its geometrical solution process to evaluate a capacity expansion problem considering both existing and candidate power plants. The algorithms are computationally more efficient and simple than the ones in previous studies for the same improvement. Further, the interpretation of the optimal capacity expansion plan is enhanced by explicitly exhibiting the results of all considered capacity expansion alternatives. The solution process can be interpreted as minimizing the long run marginal cost of supplying 1 megawatt of capacity during the whole year by finding the optimal combination of units. The developed method calculates and finds the cost polygon with the minimum area by moving along the intersection points of the screening curves to form trapezoids and then joining them to form cost polygons. The intersection points, which are needed to calculate the areas of the cost polygons, are found by using the Karush–Kuhn–Tucker conditions in a recursive manner. The last unit in the merit order of dispatching is determined by scenarios to yield an optimal capacity expansion plan. The scenarios are primarily based on a tradeoff between incurring investment costs by commissioning candidate units or taking online existing units with relatively higher variable costs compared to the candidate units.     

价格和服务双因素影响的供应链协调决策模型研究

以单个制造商和存在竞争的两个零售商组成的供应链结构模型为研究对象,建立价格和服务两个重要因素影响的市场需求函数,研究供应链协调决策模型。以销售价格、服务质量和制造商批发价格作为三个决策变量,分析集中决策和分散决策两种情景的最优策略,研究收益共享、服务成本分担、收益共享成本分担等三种不同契约的供应链协调问题,并使用数值仿真方法,分析了相关参数变化对系统协调的影响。仿真结果表明:收益共享契约和成本分担契约需满足一定条件才能达到供应链协调,但其效果不一定实现帕累托最优;收益共享和成本分担契约可实现供应链协调,但收入共享系数需在合理区间取值;改进的收益共享成本分担契约能够避免共享系数的盲区,更加有效地达到供应链协调,保持供应链良好运行。     

基于可信性理论的PPP项目特许期-价格联合调整策略研究

需求预测误差是影响PPP项目收益预测准确性的主要因素。为减少谈判争议,确保风险和收益的动态均衡,本文基于模糊数学可信性理论,构建了考虑需求不确定的特许期-价格联合调整模型。将项目运营期间的需求预测误差作为模糊变量,将运营期内特许期和价格的联合调整策略作为决策变量,通过模糊模拟求解出不同特许期和价格调整组合下的期望收益误差以及正收益预期下的可信性,进而得到特许期和价格的联合调整策略可行解集。并将该模型应用于某污水项目中,结果表明,该模型能够有效地解决需求不确定性风险对特许期测算影响的问题,弥补了目前PPP项目特许期和价格调整决策研究中未考虑需求预测误差的不足,对PPP项目特许期和价格的调整决策有着重要的参考意义。     

房屋销售价格指数与地产指数关系的协整分析

对房屋销售价格指数和深交所地产指数的协整关系进行实证分析,并做了Granger因果检验,结果显示:在所考虑的数据区间房屋销售价格指数与地产指数确实存在协整关系,地产指数是引起房屋销售价格指数变化的原因,最后给出了一些相关的建议.     

沪深300仿真股指期货价格不对称跳跃波动的实证分析

本研究利用2006年10月30日至2009年3月13日期间的仿真的沪深300指数期货每日结算价,探讨了期货价格的不对称跳跃波动行为。在实证研究方法上,本文以Chan和Maheu的GARCH(1,1)-ARJI模型为基础并进行了扩展,以EGARCH(1,1)-CJI和EGARCH(1,1)-ARJI两种模型来刻画股指期货价格的不对称和跳跃波动行为。实证结果显示:(1)沪深300仿真股指期货价格存在不对称跳跃波动,而且跳跃强度不为一固定常数,异常信息所产生的跳跃强度是随着时间变动的。(2)经过似然比检验,结果显示EGARCH(1,1)-ARJI模型比EGARCH(1,1)-CJI模型具有更好的拟合能力。     

基于平均证券价格的衍生品期权定价公式

为了更好的平滑证券价格在市场中波动的不确定性,本文建立了基于平均证券价格的证券价格模型,并在此基础上计算出了欧式看涨期权价格公式。对比传统的Black-Scholes定价公式,新模型能够更好的适应市场的波动,对期权定价方法的拓展具有重要的作用。