Springer Science and Business Media LLC

Chúng tôi nghiên cứu một hàng đợi theo thời gian rời rạc, nhiều máy phục vụ, có dung lượng hữu hạn với sự xuất hiện theo dạng đợt. Khi công việc đầu tiên trong một đợt đến hàng đợi, các công việc tiếp theo sẽ đến trong mỗi khoảng thời gian cho đến khi công việc cuối cùng trong đợt đến. Số lượng công việc và thời gian giữa các lần đến của các đợt được cho là phân phối tổng quát, và thời gian phục vụ được giả định là bằng một khoảng thời gian. Chúng tôi đề xuất một phương pháp số hiệu quả để có được chính xác xác suất mất công việc, phân phối thời gian chờ và chiều dài hàng đợi trung bình bằng cách sử dụng một chuỗi Markov nhúng tại các khoảnh khắc đến của các đợt.

Accurate travel time information is essential for logistics vehicles to reserve the most suitable parking lot in logistics centers. The purpose of this study is to explore how the uncertainty of traffic time prediction affects parking lot reservation near logistics centers. A hybrid model integrating convolutional long short-term memory network and attention mechanism is proposed to provide the reliable information for travel time prediction intervals. Furthermore, a reliability-based parking lot reservation model is developed by explicitly considering logistics vehicles’ time probability. Several benchmark models are compared with the proposed traffic speed prediction model. The performance of the parking lot reservation model is illustrated by travel behavior questionnaire data and global positioning system data of collected from Beijing, China. The results illustrate that the proposed prediction model exhibits a better accuracy than benchmark models. Moreover, it is found that travel time prediction interval can improve the reliability and stability of travel time, and provide a reliable time information for the parking lot reservation.

Traditionally, network design and line planning have been studied as two different phases in the planning process of public transportation. At the strategic level approaches dealing with the network design problem minimize travel time or maximize trip coverage, whereas at the tactical level, in the case of line planning, most models minimize cost or the number of transfers. The main novelty of this paper is the integration of the strategic and tactical phases of the rapid transit planning process. Specifically, a mathematical programming model that simultaneously determines the infrastructure network, line planning, train capacity of each line, fleet investment and personnel planning is defined. Moreover, the demand is assumed to be elastic and, therefore it is split into the rapid transit network and a competing mode according to a generalized cost. A rigorous analysis for the calibration of the different concepts that appear as consequence of the integration of phases is presented. Our approach maximizes the total profit of the network by achieving a balance between the maximum trip coverage and the minimum total cost associated to the network. Numerical results taking into account data based on real-world instances are presented.

Forest management planning decisions are often based on the forest owner’s goals, which typically focus on economic criteria. Logging operation work productivity functions are used when costing forest operations. These functions affect the conclusions drawn during forest management analyses because different logging environments give rise to different harvesting costs. When evaluating new combinations of machines and environments, there is generally a shortage of field data on productivity that can be examined in advance. We applied a previously published deductive framework describing time consumption in forwarding to known environments, in which field studies on forwarding have been conducted and for which extensive data are available. We then adapted the deductive framework to better reproduce the results obtained in the time studies. The deductive framework accurately reproduced the observed forwarding productivities; on average, the adaptation process improved the accuracy of this reproduction. However, it may also have reduced the accuracy of individual predictions. We conclude that the deductive framework can be used as a basis for constructing work productivity functions for forest management analyses, and can serve as a foundation when constructing new productivity functions based on time study results to use when pricing forwarding.

Valuation adjustments, collectively named XVA, play an important role in modern derivatives pricing to take into account additional price components such as counterparty and funding risk premia. They are an exotic price component carrying a significant model risk and computational effort even for vanilla trades. We adopt an industry-standard realistic and complete XVA modelling framework, typically used by XVA trading desks, based on multi-curve time-dependent volatility G2++ stochastic dynamics calibrated on real market data, and a multi-step Monte Carlo simulation including both variation and initial margins. We apply this framework to the most common linear and non-linear interest rates derivatives, also comparing the MC results with XVA analytical formulas. Within this framework, we identify the most relevant model risk sources affecting the precision of XVA figures and we measure the corresponding computational effort. In particular, we show how to build a parsimonious and efficient MC time simulation grid able to capture the spikes arising in collateralized exposure during the margin period of risk. As a consequence, we also show how to tune accuracy versus performance, leading to sufficiently robust XVA figures in a reasonable time, a very important feature for practical applications. Furthermore, we provide a quantification of the XVA model risk stemming from the existence of a range of different parameterizations according to the EU prudent valuation regulation. Finally, this work also serves as an handbook containing step-by-step instructions for the implementation of a complete, realistic and robust modelling framework of collateralized exposure and XVA.

This paper deals with the demand for money, including narrow money (M 1) and broad money (M 2) in China. We use the cointegration and error‐correction model to formulate the function of money demand and merge the short‐run and long‐run equations to give forecasts over different horizons. In particular, we combine very simple artificial neural networks (ANNs) with the cointegration and error‐correction model to give a nonlinear model. These models are quarterly models, sampled from the first quarter of 1980 to the fourth quater of 1994, and the multi‐step forecasts are from the first quarter of 1990 to the fourth quarter of1994. Both the fitted values and predictive values for M 1 and M 2 are satisfactory. Finally, we give forecasts for M 2 from the first quarter of 1995 to the second quarter of 1996.