The role of information flow in engineering optimization

Current optimization techniques work well for single components represented by a single model. However, many of the problems we face today are multi-disciplinary problems requiring the integration of complex models from different fields to gain a more complete understanding of the overall performance of a biological, engineering, or human system. One example is a modern automobile. Multiple systems (such as the power train and electronic engine control system) are designed and built from various assemblies and components, all of which are then integrated into one final product. This design process evokes a systems-of-systems concept that is also found in agricultural facilities, aircraft design, and many other industrial applications where multiple systems are orchestrated to achieve common goals. Optimization of these complex systems is challenging. Tight coupling between the various models, discontinuous search spaces, and long run times can quickly defeat traditional optimization techniques.;Evolutionary algorithms provide a way to approach optimization of these complex systems. Evolutionary algorithms blend the information contained in a population of solutions to answer problems that thwart many classical optimization methods, but loss of diversity in the evolving solutions is a critical issue. As this information is shared between the population members, the diversity in that population decreases as the solutions converge to a single answer. For many challenging engineering problems this loss of diversity occurs too rapidly for novel solutions to emerge. In addition, systems of systems optimization problems are often deceptive because the global optimum is composed of multiple building blocks, making the preservation of diversity crucial.;This work presents graph based evolutionary algorithms as a tool to control the rate at which information is spread throughout an evolving population and thereby limit diversity loss. Graph based evolutionary algorithms impose a computational geography on the evolving population, placing barriers to information flow to allow for the development of the building blocks required to assemble one or more superior solutions. Graph based evolutionary algorithms can be used to find new solutions and decrease the time to convergence to a global optimum for complex, deceptive problems. In addition, the performance of a problem on a set of graphs can be used as a taxonomical character to classify evolutionary computation problems. If comparisons can be made between classified problems and a new problem being examined, it would be possible to select a graph that matches the desired performance. This careful graph selection can provide solutions that are both novel and superior to those found by standard evolutionary algorithms. Successful examples can be found in a variety of disciplines, including the engineering design problem of optimizing cook stoves for use in the third world to biological systems-of-systems, such as the tailoring of antibiotic regimens for use in swine production

Multi-Objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition

Model Development of a Virtual Learning Environment to Enhance Lean Education

AbstractModern day industry is becoming leaner by the day. This demands engineers with an in-depth understanding of lean philosophies. Current methods for teaching lean include hands-on projects and simulation. However, simulation games available in the market lack simplicity, ability to store the results, and modeling power. The goal of this research is to develop a virtual simulation platform which would enable students to perform various experiments by applying lean concepts. The design addresses these deficiencies through the use of VE-Suite, a virtual engineering software. The design includes user-friendly dialogue boxes, graphical models of machines, performance display gauges, and an editable layout. The platform uses laws of operations management such as Little's law, economic order quantity (EOQ) models, and cycle time. These laws enable students to implement various lean concepts such as pull system, just-in-time (JIT), single piece flow, single minute exchange of dies (SMED), kaizen, kanban, U-layout, by modifying the process parameters such as process times, setup times, layout, number, and placement of machines. The simulation begins with a traditional push type mass production line and the students improve the line by implementing lean techniques. Thus, students experience the advantages of lean real time while facing the real life problems encountered in implementing it

Flood Prediction and Uncertainty Estimation using Deep Learning

Floods are a complex phenomenon that are difficult to predict because of their non-linear and dynamic nature. Therefore, flood prediction has been a key research topic in the field of hydrology. Various researchers have approached this problem using different techniques ranging from physical models to image processing, but the accuracy and time steps are not sufficient for all applications. This study explores deep learning techniques for predicting gauge height and evaluating the associated uncertainty. Gauge height data for the Meramec River in Valley Park, Missouri was used to develop and validate the model. It was found that the deep learning model was more accurate than the physical and statistical models currently in use while providing information in 15 minute increments rather than six hour increments. It was also found that the use of data sub-selection for regularization in deep learning is preferred to dropout. These results make it possible to provide more accurate and timely flood prediction for a wide variety of applications, including transportation systems

Application of an Artificial Neural Network to Predict Graduation Success at the United States Military Academy

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent\u27s education status, and time since high school graduation. These inputs and the multi-layer neural network model are used to classify students as: graduates, late graduates, or non-graduates. Several neural network architectures are examined and compared by run time, minimum mean square error achieved (MSE), mean correct classification rate, precision, recall, and specificity. A multi-layer neural network with 50 hidden neurons, momentum value of 0.8, and learning rate of 0.1, with hyperbolic tangent hidden neuron activation functions was able to accurately predict graduation success and achieved the best performance with classification accuracy exceeding 95%, high recall, high precision, and high specificity. This prediction model may be used to inform admission decisions and identify opportunities for required remediation with the potential to improve graduation rates, increase student retention, reduce late graduation, and reduce first-term course failures

Flood Management Deep Learning Model Inputs: A Review of Necessary Data and Predictive Tools

Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on region-specific geospatial data and information

Agent based Modeling for Flood Inundation Mapping and Rerouting

Natural disasters like earthquakes and floods can have a serious impact on road networks, which are critical to supply chain infrastructure and to provide connectivity. These extreme events can result in isolating people in the affected area from hospitals and emergency response. This paper presents an agent-based model for understanding flood propagation and developing inundation mapping. The results from the mapping are used to identify the roads prone to floods based on elevation data and flood simulation. A simulation environment was set up in SUMO, and the costs associated with the traffic disruption are evaluated. This paper discusses the integration of various techniques for a comprehensive flood prediction and rerouting system

Identifying Geographical Interdependency in Critical Infrastructure Systems Using Open Source Geospatial Data in Order to Model Restoration Strategies in the Aftermath of a Large-Scale Disaster

In the wake of a large-scale disaster, strategies for emergency search and rescue, short-term recovery and medium- to long-term restoration are needed. While considerable effort is geared to developing strategies for the former two options, little comprehensive guidance exists on the latter. However, medium- to long-term restoration has a significant effect on local, regional and national economies and is essential to community vitality. In part, the deficit of robust strategies can be linked to the complexity in the data acquisition and limited methodologies to understand the interconnectedness of the relevant systems elements. This research utilizes infrastructure data for Supply Chain Interdependent Critical Infrastructure Systems (SCICI) such as transportation, energy, communications, or water, obtained or derived through open sources (such as The National Map of the U.S. Geological Survey) to identify, understand, and map the interdependencies between these system elements to enable restoration planning. Specifically, internal geographical relationships (herein called the ‘geographical interdependency’) of SCICI elements are mapped. These interdependencies highlight the stress points on the larger SCICI where failures occur and are not included in current built environment models. The mapping of these interdependencies is a key step forward in attempts to optimally restore an urban center’s supply chain in the wake of an extreme event

Supply Chain Infrastructure Restoration Calculator Software Tool -- Developer Guide and User Manual

This report describes a software tool that calculates costs associated with the reconstruction of supply chain interdependent critical infrastructure in the advent of a catastrophic failure by either outside forces (extreme events) or internal forces (fatigue). This tool fills a gap between search and recover strategies of the Federal Emergency Management Agency (or FEMA) and construction techniques under full recovery. In addition to overall construction costs, the tool calculates reconstruction needs in terms of personnel and their required support. From these estimates, total costs (or the cost of each element to be restored) can be calculated. Estimates are based upon historic reconstruction data, although decision managers do have the choice of entering their own input data to tailor the results to a local area