Using artificial neural networks for transport decisions: Managerial guidelines

One information technology that may be considered by transportation managers, and which is included in the portfolio of technologies that encompass TMS. is artificial neural networks (ANNs). These artificially intelligent computer decision support software provide solutions by finding and recognizing complex patterns in data. ANNs have been used successfully by transportation managers to forecast transportation demand, estimate future transport costs, schedule vehicles and shipments, route vehicles and classify earners for selection. Artificial neural networks excel in transportation decision environments that are dynamic, complex and unstructured. This article introduces ANNs to transport managers by describing ANN technological capabilities, reporting the current status of transportation neural network applications, presenting ANN applications that offer significant potential for future development and offering managerial guidelines for ANN development

Alarm Forecasting in Natural Gas Pipelines

This thesis examines alarm forecasting methods for a natural gas production pipeline to assure the efficient transportation of high-quality natural gas. Natural gas production companies use pipelines to transport natural gas from the extraction well to a distribution point. Forecasting natural gas pipeline pressure alarms helps control room operators maintain a functioning pipeline and avoid costly down time. As gas enters the pipeline and travels to the distribution point, it is expected that the gas meets certain specifications set in place by either state law or the customer receiving the gas. If the gas meets these standards and is accepted at the distribution point, the pipeline is referred to as being in a steady-state. If the gas does not meet these standards, the production company runs the risk of being shut-in, or being unable to flow any more gas through the distribution point until the poor-quality gas is removed.Sensors are used to collect real-time gas quality information from within the pipe, and alarms are used to alert the control operators when a threshold is exceeded. If operators fail to keep the pipeline’s gas quality within an acceptable range, the company risks being shut¬¬-in or rupturing the pipeline. Predicting gas quality alarms enables operators to act earlier to avoid being shut-in and is a form of predictive maintenance. We forecast alarms by using a 10th-order autoregressive model, autoregressive model with exogenous variable, simple exponential smoothing with drift (Theta Method) and an artificial neural network with alarm thresholds. The alarm thresholds are defined by the production company and are occasionally adjusted to meet current environment conditions. The results of the alarm forecasting method show that we accurately forecast natural gas pipeline alarms up to a 30-minute time horizon. This translates into sensitivity rates that drop from around 100% at one minute to 82.7% at a 30-minute forecast horizon. This means that at 30 minutes, we correctly forecast 82.7% of the alarms. All alarm forecasting models outperform the state-or-the-art forecaster used by the production company, with the artificial neural network performing the best

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Problems in Longterm Forecasting and Planning: with reference to transport and energy

In this thesis I have set out my experiences of forecasting in the long term. (I have taken long term to be anything beyond 5 to 10 years). I have set forecasting in its context as a component of the planning process and have related the problems of forecasting to the ensuing problems in planning. The main areas I have covered are Transport and Energy, in a UK context.However I have touched on other fields and I expect the conclusions I have drawn about forecasting and planning to apply to many fields. The thesis is in two parts plus appendices. In the first part I report three substantial pieces of research. They illustrate many of the problems associated with forecasting, and I examine possible solutions to these problems. In the second part I describe how forecasts are made and some of the problems inherent in forecasting. I then examine why forecasts appear to be necessary in the planning/ policy making process. I conclude with the implications that my views on forecasting

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

The Echo: December 9, 1983

Wallace Speaks: Reflections on Minority Issues – Final Exams Schedule – OPEC Ministers Likely To Hold Oil Prices – Career Development Update – President’s Perspective – ‘Time To Reflect’ – A Dozen Do’s When There Is Nothing To Do – ½ Wit, ½ Jest – ‘Tis The Season To Be Chubby – SAC Plans Christmas Activities – Sprunger Displays Art – Chorale Performs Yuletide Celebration – The Echo wishes the Taylor Community a Christ-filled Christmas! – Black Cultural Society Reaches Out To Marion Community – Wrestling Team Starts Season – Top Sprinter Joins Taylor – Racquetball Tourney Results – Taylor’s Weightlifting Club – Women’s Basketball Victories – Trojan Basketball Team Defeated – Taylor University 1983-84 Basketball Schedulehttps://pillars.taylor.edu/echo-1983-1984/1009/thumbnail.jp

Speculative Asset Prices

Nobel Prize Lectur

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Managing the uncertainties in commodity trading: A Bayesian software implementation

A decision support software tool is developed to aid commodity traders with the management of uncertainty. A methodology to control the risk of losses is examined and this is likely to be highly desirable for practical use in the financial markets. Bayesian techniques are used to forecast future рrices and this is combined with the optimization of a utility function to support a trader's buy and sell decisions. The difficulties of eliciting both a prior belief and a utility function are discussed. The effectiveness of the tool is explored using a practical application of the Design of Experiments (using simulated prices) and also by testing the software with traders. The trials of the tool with traders received positive feedback