State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

A novel intelligent approach for yaw position forecasting in wind energy systems

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.     

A non-parametric approach to demand forecasting in revenue management

In revenue management, the profitability of the inventory and pricing decisions rests on the accuracy of demand forecasts. However, whenever a product is no longer available, true demand may differ from registered bookings, thus inducing a negative bias in the estimation figures, as well as an artificial increase in demand for substitute products. In order to address these issues, we propose an original Mixed Integer Nonlinear Program (MINLP) to estimate product utilities as well as capturing seasonal effects. This behavioral model solely rests on daily registered bookings and product availabilities. Its outputs are the product utilities and daily potential demands, together with the expected demand of each product within any given time interval. Those are obtained via a tailored algorithm that outperforms two well-known generic software for global optimization.     

基于ARMA模型的双路径差异性分析

拟通过单向时延的历史记录对其未来趋势进行预测。首先,通过粗粒度和细粒度两种方式采集世界各地若干目标节点的单向时延作为原始数据;然后,结合多种预测模型的特点和原始数据的内在属性,提出一种基于ARMA模型的双路径差异性分析方法;最后选取合理的预测窗口对方法的准确性进行验证。结果表明,该预测方法是正确、合理的,与灰色预测模型相比能更有效地预测单项时延差序列。     

Predictive economic efficiency of combining nuclear power plants with autonomous hydrogen power complex

Currently, the United Energy System (UEC) of Russia is trending in the deficit of peak and half-peak capacity with a simultaneous increase in the number of nuclear power plants (NPPs), which will require the participation of the NPPs in the variable part of the schedule of electrical loads.In addition to the economic need to maintain the high-level utilization rate, there are technological limitations of maneuverability for NPPs.The authors developed an approach to solving this problem by combining with an environmentally friendly energy source – an autonomous hydrogen power complex, which includes thermal batteries and an additional multifunctional low-power steam turbine installation.The developed energy complex can also provide reliable reservation of electricity supply to consumers of their own needs of the nuclear power plant in case of complete blackout of the plant.The feasibility study of the main equipment of the autonomous hydrogen power complex, which is necessary for combining with a two-unit nuclear power plant with WWER-1000, has been evaluated.On the basis of the assessment of the inflation indicators of the Russian economy over the past 11 years, three variants of fuel cost dynamics and tariff rates for electricity (capacity) as well as the size of operating costs, including depreciation deductions to the main equipment, are defined, taking into account the current principles of price formation.The result is a value for accumulated net present value, depending on the ratio of the cost of the half-peak and off-peak electricity at different inflation rates.The positive economic effect of reducing the risk of the core damage accident, replacing the construction of the gas turbine unit as a maneuverable source of electricity in the power grid and increasing the income of the Russian federal budget from the savings of natural gas has been taken into account.The greatest economic efficiency is achieved with maximum projected inflation, which is associated with the maximum rate of discounting and the high rate of growth of electricity tariffs.Reducing the risk of the core damage accident ensures that the proposed approach is competitive in all the inflation options under consideration and the ratio of electricity tariffs.     

Competitive substitution and technological diffusion for semiconductor foundry firms

To survive in an intensively competitive environment, semiconductor companies need to be more agile, responsive, and flexible than before. Generally, semiconductor industry consists of three business models: integrated design manufacturer, fabless chip design, and foundry business. In general, semiconductor firms are affected by three drivers: new entrants and rivals (competition), main customers (demand), and process technologies (R&D). Inspired by Michael Porter’s five-force analysis, a novel framework is presented to accomplish the following goals: (1) The interactions between the top three foundry firms, TSMC, Samsung, and Global Foundries, are analyzed to reveal managerial insights, (2) The main customers of TSMC including Apple, Huawei, Qualcomm, Mediatek, AMD, and NVidia are incorporated to into sales forecasting, and (3) Technological diffusion across the mature process (above 70 nm), the medium process (between 20 nm and 70 nm), and the advanced process (below 20 nm) is captured to predict sales revenues. Key findings are shown as follows: (1) a large-scale foundry frim benefits from the existence of a small-scale firm, (2) the inclusion of main customers significantly improves the performance of sales forecasting, (3) the advanced process gradually benefits from the mature process while it rapidly replaces the medium process in a “predator-prey” way.     

Machine Learning Supports Robust Operation of Thermosiphon Reboilers

The analysis of process and equipment operational data in chemical engineering regularly requires a high level of expert knowledge. This work presents a Machine Learning-based approach to evaluate and interpret process data to support robust operation of a thermosiphon reboiler. By applying an outlier detection, potentially interesting and unstable operating conditions can be identified quickly. A multidimensional regression allows to forecast the circulating mass flow. The results obtained fit well into the current state of research and manual evaluation of thermosiphon reboilers.     

Application of cross validation techniques for modelling construction costs during the very early design stage

Building client/owners need estimates of likely construction costs for budgeting purposes early in the procurement process when little detailed design information is available beyond the type, size and location of the facility. One of the more sophisticated techniques available for this purpose is the storey enclosure method, developed by James in 1954. This uses the basic physical measurements of the building envelope, together with an arbitrary set of multipliers, or weights, to forecast tender/bid prices. Although seldom used in practice, James succeeded in showing his method to be capable of significantly outperforming alternative approaches.

The research reported in this paper aimed firstly to reassess James’ claims with new data and secondly to advance his method by using regression techniques to obtain the weights involved. Based on data from 138 completed Hong Kong projects for four types of building, two types of regression models were developed. This involved the use of sophisticated features such as leave-one-out cross validation to simulate the way in which forecasts are produced in practice and a dual stepwise selection strategy that enhances the chance of identifying the best model. An algorithm was also designed to select the appropriate parametric and non-parametric tests for objective and rigorous model evaluation against alternatives.

The results indicate that, contrary to James’ claim, both his original method and the two regression-based alternatives are not significantly better or worse than other models. Surprisingly, the widely used floor area model was found to under-perform in terms of consistency for offices and private housing. For private housing in particular, it was felt that the storey enclosure method was likely to offer good prospects of improvement on those methods currently in use in practice.     

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.     

Investigations of groundwater bursting into coal mine seam floors from fault zones

This paper presents a case study of investigations into retarded groundwater bursting along the fault zones in the seam floors in coal mines. In addition to in situ measurements of ground stresses, a number of rock samples from the fault zones at the site of the case study were taken, and laboratory tests were performed for conventional and special rheological rock mechanics properties. The effects of different parameters on deformation and failure of the fault zone materials were analyzed, and the mechanism of retarded groundwater bursting along fault zones was further revealed. In the case study, different scenarios accounting for the different development phases of the fault weakness zone and groundwater bursting as well as different groundwater pressures were considered, and each of them was simulated using the 3D visual elastic–plastic numerical mechanics model FLAC^3D (Fast Lagrangian Analysis of Continua in 3 Dimensions). Through the case study, the applicability of rock mechanics modeling software for studying retarded groundwater bursting along a fault zone was examined. Several issues are discussed regarding the investigations of retarded groundwater bursting and a general structured approach to investigate and control this geological hazardous event is presented.