Distribution network modeling and optimization for rapid and cost-effective deployment of oilfield drilling equipment

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references.AAA, a large oil and gas field services company, is in the business of providing drilling services to companies that extract and market hydrocarbons. One of the key success factors in this industry is the ability to provide comprehensive drilling solutions on short notice and in demanding conditions; fast and reliable delivery of drilling equipment to well sites is critical to maintaining customer satisfaction and market share. The company is considering a reconfiguration of its tool distribution network in order to facilitate a more rapid and cost-effective delivery of drilling tools to drilling sites. Specifically, the company is considering using either a "pure" hub-and-spoke distribution setup, with one of its major facilities - OK - serving as a logistics hub, or a hub-and-spoke system with postponement capabilities, whereby the OK facility will also have certain assembly and configuration capabilities. This thesis develops a model of the AAA distribution network and creates a simulation of the flow of drilling tools through the two alternative network configurations. As customer service levels and logistics costs are evaluated under various levels of end-user demand, both network setups are shown to increase the effectiveness and cost-efficiency of tool deliveries. The key finding is that the hub-and-spoke with postponement design appears to be superior in terms of logistics costs and timely deliveries.by Alexander Martchouk.M.Eng.in Logistic

Investigation of Driver Route Choice Behaviour using Bluetooth Data

Many local authorities use small-scale transport models to manage their transportation networks. These may assume drivers’ behaviour to be rational in choosing the fastest route, and thus all drivers behave the same given an origin and destination, leading to simplified aggregate flow models, fitted to anonymous traffic flow measurements. Recent price falls in traffic sensors, data storage, and compute power now enable Data Science to empirically test such assumptions, by using per-driver data to infer route selection from sensor observations and compare with optimal route selection. A methodology is presented using per-driver data to analyse driver route choice behaviour in transportation networks. Traffic flows on multiple measurable routes for origin-destination pairs are compared based on the length of each route. A driver rationality index is defined by considering the shortest physical route between an origin-destination pair. The proposed method is intended to aid calibration of parameters used in traffic assignment models e.g. weights in generalized cost formulations or dispersion within stochastic user equilibrium models. The method is demonstrated using raw sensor datasets collected through Bluetooth sensors in the area of Chesterfield, Derbyshire, UK. The results for this region show that routes with a significant difference in lengths of their paths have the majority (71%) of drivers using the optimal path but as the difference in length decreases, the probability of optimal route choice decreases (27%). The methodology can be used for extended research considering the impact on route choice of other factors including travel time and road specific conditions

Origin-Destination Tools for District Offices

Understanding through trip patterns is crucial when making decisions concerning a traffic diversion strategy, such as whether to build a highway to bypass a city. While conducting a vehicle license plate origin-destination survey may be the most accurate way to estimate through trip patterns, many small communities may be unable to bear this cost. Thus, a simple and affordable sketch planning tool to estimate through trip movements would be useful. Three through trip estimation methods that have been used or published are Modlin’s method, Anderson’s method, and subarea analysis. Subarea analysis, while an effective tool, requires personnel with knowledge of modeling software, as well as a license for that software. Hence, subarea analysis may be of limited use to small cities and DOT district offices. Anderson and Modlin methods on the other hand are simpler to implement. However, these methods require some data that are not routinely collected, use parameters that can be highly subjective, and rely on calculations that may distort the results. To address some of the shortcomings of the existing methods, a logit-based external trip estimation method was created that had strong statistical justification. Evaluation of the logit model using small cities in Indiana yielded results that are usually better than Modlin’s and Anderson’s methods. The logit model is readily implemented in a spreadsheet and requires only two input variables. When subarea analysis using modeling software is not feasible, the logit model has been shown to produce good estimates of through trips

Travel time reliability

Travel time and travel time reliability are important performance measures for assessing traffic condition and extent of congestion on a roadway. Most commonly used methods to obtain travel time data either produce only estimates of travel times or too few travel time data points for meaningful analysis. This study focuses on using a new probe vehicle technique, the Bluetooth technology, to collect two weeks of travel time data on Interstate-69 in Indianapolis. These data are then used to estimate econometric models, which can be used to predict freeway segment travel times. First, an autoregressive model is estimated based on the collected data. Individual vehicle travel times on a freeway segment are expressed as a function of speed, volume, time of day indicators, and previous vehicle travel times. In addition to the autoregressive formulation, a duration model is estimated based on the same travel time data. The duration model enables calculation of the probability of the vehicle exiting the segment of the road at any point in time. The estimated models indicate that the rate of vehicles exiting the segment as a function of their travel time rises initially until the inflection point and then decreases. It is hypothesized that the inflection point occurs at the onset of congestion, when longer travel time may not result in a higher probability of exiting the freeway segment. Lastly, a seemingly unrelated regression equation model to predict travel time and intervehicle variability is proposed. This model predicts 15-minute interval travel time and the standard deviation of travel time based on speed, volume and time of day indicators. The estimated model shows a good fit with the data. Furthermore, the results indicate that it is superior to the model based on point-speed estimates, which is commonly used in practice. Thus, the SURE model can be used to improve real-time travel time prediction

Travel Time Reliability in Indiana

Travel time and travel time reliability are important performance measures for assessing traffic condition and extent of congestion on a roadway. This study first uses a floating car technique to assess travel time and travel time reliability on a number of Indiana highways. Then the study goes on to describe the use of Bluetooth technology to collect real travel time data on a freeway and applies it to obtain two weeks of data on Interstate 69 in Indianapolis. An autoregressive model, estimated based on the collected data, is then proposed to predict individual vehicle travel times on a freeway segment. This model includes speed, volume, time of day indicators, and previous vehicle travel times as independent variables. In addition to the autoregressive formulation, a duration model is estimated based on the same travel time data. The duration model of travel time provided insights into how one could predict the probability of a car’s duration of time on a roadway segment changed over time. Interestingly, the three duration models estimated (all hours, peak hour and night time models) showed that the point where the conditional probability of travel times becoming longer occurs roughly at the onset of level-of-service F conditions. Finally, a seemingly unrelated regression equation model to predict travel time and travel-time variability is estimated. This model predicts 15-minute interval travel times and standard deviation of travel time based on speed, volume and time of day indicators. The model has a very good statistical fit and thus can be used in the field to compute real-time travel time using data available from remote traffic microwave sensors

Is bus overrepresented in Bluetooth MAC Scanner data? Is MAC-ID really unique?

One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC IDs