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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Establishing Rules For Self-Organizing Systems-Of-Systems, David M. Curry, Cihan H. Dagli
Establishing Rules For Self-Organizing Systems-Of-Systems, David M. Curry, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Self-organizing systems-of-systems offer the possibility of autonomously adapting to new circumstances and tasking. This could significantly benefit large endeavors such as smart cities and national defense by increasing the probability that new situations are expediently handled. Complex self-organizing behaviors can be produced by a large set of individual agents all following the same simple set of rules. While biological rule sets have application in achieving human goals, other rules sets may be necessary as these goals are not necessarily mirrored in nature. To this end, a set of system, rather than biologically, inspired rules is introduced and an agent-based model …
Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke
Instance Selection Using Genetic Algorithms For An Intelligent Ensemble Trading System, Youngmin Kim, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a …
Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos
Time Series Classification Using Deep Learning For Process Planning: A Case From The Process Industry, Nijat Mehdiyev, Johannes Lahann, Andreas Emrich, David Lee Enke, Peter Fettke, Peter Loos
Engineering Management and Systems Engineering Faculty Research & Creative Works
Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, …
Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias
Loading Time Flexibility In Cross-Docking Systems, Dincer Konur, Mihalis M. Golias
Engineering Management and Systems Engineering Faculty Research & Creative Works
In this study, we investigate truck-to-door assignment problem for loading outgoing trucks in a cross-docking system with flexible handling times. Specifically, a truck's loading time depends on the number of workers assigned to the outbound door, where the truck is being loaded. An optimization problem is formulated to jointly determine the number of workers and the trucks to be loaded at each door. The resulting problem is a nonlinear integer programming model. Due to the complexity of this model, two evolutionary heuristic methods are proposed for solution. First heuristic method is based on truck assignments while the second heuristic is …