The power of dimensional analysis in production systems design

Dimensional Analysis (DA) is a well-known methodology in physics, chemistry and other traditional engineering areas. In its simplest form, DA is used to check the meaningfulness of a set of equations (dimensional homogeneity). In the last century, the dimensional theory has been profoundly investigated: its highest achievement is the Buckingham theorem (or pi-theorem), which states that any equation modelling a physical problem can be rearranged in terms of dimensionless ratios, thus saving variables to be handled, and especially enriching the inner physical knowledge of the studied phenomenon.In this paper we investigate how DA can be applied to Operations Management (OM) topics and which benefits it can bring to researchers in this area. A literature review is performed to clarify the main operative issues regarding DA application (assumptions and limitations); then existing applications of DA to OM are explored, pointing out that few researchers have tried to apply this methodology in the OM research field.Stemming from this analysis, we applied the pi-theorem to the design of a Flexible Manufacturing System. A complex problem, requiring 13 dimensional quantities to be expressed, is first studied via simulation; then DA is applied, reducing the number of variables to 9 dimensionless ratios. The reduced problem has a suitable size to be analytically explored and a regression model is formulated which, compared with the simulation study, offers the same precision in analysing the FMS behaviour, being more compact and powerful. This application shows the potential of DA in OM research, and will hopefully draw the attention of researches to this powerful, but unfamiliar and therefore neglected, methodology.     

Entrepreneurial team's risk-based decision-making: A dynamic game analysis

It is well-known that some cognitive factors such as opportunity perception and risk perception influence entrepreneurial risk-based decision-making (RBDM). This paper proposes some rules of entrepreneur's risk decision-learning to address this RBDM taking cognitive factors into consideration. The learning mechanisms of entrepreneurial team's RBDM is derived based on entrepreneur's perception evolution. The paper employs a system dynamics model to analyze the entrepreneurial team's risk decision-learning. A case study is conducted to demonstrate the entrepreneurial RBDM process with cognitive factors.     

直接转矩控制技术在油田应用效果分析

介绍一种新型的电机控制技术——直接转矩控制技术,并对该技术在油田现场的应用效果进行了分析。该技术通过控制定子电压,实现了对电机转矩的直接控制。现场节能测试结果表明,节能效果显著。因此,该技术具有一定的经济效益和推广应用价值。     

发电设备寿命周期费用影响因素的敏感性分析

阐述了发电设备全寿命周期费用（Life Cycle Cost,LCC）的基本理论及计算方法,针对发电设备LCC的影响因素之间存在的客观联系,假定因素之间相互独立,对其中某2个因素的变动幅度进行了一定的修正,使之更贴近实际。以某2×600MW火电机组为例,选取若干影响因素,求得其全寿命周期费用净现值。对目标净现值进行单因素和多因素敏感性分析,以寻找目标值与影响因素之间所存在的内在联系,为设备投资以及后期优化管理提供决策依据。     

Forecasting the term structures of Treasury and corporate yields using dynamic Nelson-Siegel models

We extend Diebold and Li’s dynamic Nelson-Siegel three-factor model to a broader empirical prospective by including the evaluation of the state space approach and by using nine different ratings for corporate bonds. We find that the dynamic Nelson-Siegel factor AR(1) model outperforms other competitors on the out-of-sample forecast accuracy, especially on the investment-grade bonds for the short-term forecast horizon and on the high-yield bonds for the long-term forecast horizon. The dynamic Nelson-Siegel factor state space model, however, becomes appealing on the high-yield bonds in the short-term forecast horizon, where the factor dynamics are more likely time-varying and parameter instability is more probable in the model specification.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.     

A measure of regional influence with the analytic network process

The metropolitan region is commonly defined by a socio-spatial network of urban nodes that are linked in territory and function. Such a network is differentiated by size and dominance of the linked nodes, characterized by physical or virtual flows of a wide-ranging variety. The analytic network process (ANP) is a multi-criteria analytic method that measures the influence or dominance of the nodes in a network with feedback. We illustrate how ANP determines county rank as a measure of influence in a metropolitan statistical area (MSA) defined by its interrelated socio-economic and spatial elements qualitatively and quantitatively. We compare the ANP results to similar measures of regional influence in the literature.     

Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange

A new method for forecasting the trend of time series, based on mixture of MLP experts, is presented. In this paper, three neural network combining methods and an Adaptive Network-Based Fuzzy Inference System (ANFIS) are applied to trend forecasting in the Tehran stock exchange. There are two experiments in this study. In experiment I, the time series data are the Kharg petrochemical company’s daily closing prices on the Tehran stock exchange. In this case study, which considers different schemes for forecasting the trend of the time series, the recognition rates are 75.97%, 77.13% and 81.64% for stacked generalization, modified stacked generalization and ANFIS, respectively. Using the mixture of MLP experts (ME) scheme, the recognition rate is strongly increased to 86.35%. A gain and loss analysis is also used, showing the relative forecasting success of the ME method with and without rejection criteria, compared to a simple buy and hold approach. In experiment II, the time series data are the daily closing prices of 37 companies on the Tehran stock exchange. This experiment is conducted to verify the results of experiment I and to show the efficiency of the ME method compared to stacked generalization, modified stacked generalization and ANFIS.     

北京市人地关系现状及预测——基于生态足迹和GM模型的分析

采用改进过的生态足迹模型对北京市的人地关系现状进行了分析,并应用GM(1,1)模型对其耕地面积和人口数量的未来变动趋势进行了预测。结果表明,北京市近十年来耕地生态足迹逐年增加,其中区域耕地生态足迹的变化幅度比人均耕地生态足迹的变化幅度大;耕地生态承载力逐年降低,其中人均耕地生态承载力的降幅较区域耕地生态承载力的降幅更加明显。耕地生态足迹与耕地承载力异向变化,加剧了生态赤字逐年加大的幅度。在此基础上提出了控制人口规模、提高耕地产出能力及保持耕地产品地区间流动性等措施。