This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data‐driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data‐driven uncertainty sets are incorporated into a two‐stage adaptive robust mixed‐integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single‐level MILP problem. The proposed framework is applied to the steam system of a real‐world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers
ROC++: Robust Optimization in C++
Over the last two decades, robust optimization has emerged as a popular means to address decision-making problems affected by uncertainty. This includes single-stage and multi-stage problems involving real-valued and/or binary decisions and affected by exogenous (decision-independent) and/or endogenous (decision-dependent) uncertain parameters. Robust optimization techniques rely on duality theory potentially augmented with approximations to transform a (semi-)infinite optimization problem to a finite program, the robust counterpart. Whereas writing down the model for a robust optimization problem is usually a simple task, obtaining the robust counterpart requires expertise. To date, very few solutions are available that can facilitate the modeling and solution of such problems. This has been a major impediment to their being put to practical use. In this paper, we propose ROC++, an open-source C++ based platform for automatic robust optimization, applicable to a wide array of single-stage and multi-stage robust problems with both exogenous and endogenous uncertain parameters, that is easy to both use and extend. It also applies to certain classes of stochastic programs involving continuously distributed uncertain parameters and endogenous uncertainty. Our platform naturally extends existing off-the-shelf deterministic optimization platforms and offers ROPy, a Python interface in the form of a callable library, and the ROB file format for storing and sharing robust problems. We showcase the modeling power of ROC++ on several decision-making problems of practical interest. Our platform can help streamline the modeling and solution of stochastic and robust optimization problems for both researchers and practitioners. It comes with detailed documentation to facilitate its use and expansion. The latest version of ROC++ can be downloaded from https://sites.google.com/usc.edu/robust-opt-cpp/ . Summary of Contribution: The paper “ROC++: Robust Optimization in C++” proposes a new open-source C++ based platform for modeling, automatically reformulating, and solving robust optimization problems. ROC++ can address both single-stage and multi-stage problems involving exogenous and/or endogenous uncertain parameters and real- and/or binary-valued adaptive variables. The ROC++ modeling language is similar to the one provided for the deterministic case by state-of-the-art deterministic optimization solvers. ROC++ comes with detailed documentation to facilitate its use and expansion. It also offers ROPy, a Python interface in the form of a callable library. The latest version of ROC++ can be downloaded from https://sites.google.com/usc.edu/robust-opt-cpp/ . History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This material is based upon work supported by the National Science Foundation under Grant No. 1763108. This support is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1209 ) or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at ( https://dx.doi.org/10.5281/zenodo.6360996 ).
more »
« less
- Award ID(s):
- 1763108
- NSF-PAR ID:
- 10433804
- Date Published:
- Journal Name:
- INFORMS Journal on Computing
- Volume:
- 34
- Issue:
- 6
- ISSN:
- 1091-9856
- Page Range / eLocation ID:
- 2873 to 2888
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
AIChE J , 65: e16500 2019 -
Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models have been proposed to tackle such uncertainty; however, they produce 0-1 nonlinear problems and lack scalability. In this work, we investigate this emerging data science problem and develop a unique computational framework to solve the robust causal inference test instances from observational data with continuous outcomes. In the proposed framework, we first reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve a globally optimal solution. We perform experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms and compare our results with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process. Finally, the proposed framework not only facilitates robust decision making through big-data causal inference, but it can also be utilized in developing efficient algorithms for other nonlinear optimization problems such as quadratic assignment problems. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation of the National Science Foundation [Grant 2047094]. Supplemental Material: The online supplements are available at https://doi.org/10.1287/ijoc.2022.1226 .more » « less
-
This paper introduces a major redesign of SimOpt, a testbed of simulation-optimization (SO) problems and solvers. The testbed promotes the empirical evaluation and comparison of solvers and aims to accelerate their development. Relative to previous versions of SimOpt, the redesign ports the code to an object-oriented architecture in Python; uses an implementation of the MRG32k3a random number generator that supports streams, substreams, and subsubstreams; supports the automated use of common random numbers for ease and efficiency; includes a powerful suite of plotting tools for visualizing experiment results; uses bootstrapping to obtain error estimates; accommodates the use of data farming to explore simulation models and optimization solvers as their input parameters vary; and provides a graphical user interface. The SimOpt source code is available on a GitHub repository under a permissive open-source license and as a Python package. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This work was supported by the National Science Foundation [Grant CMMI-2035086]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1273 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0011 ) at ( http://dx.doi.org/10.5281/zenodo.7468744 ).more » « less
-
Obeid, I. ; Selesnick, I. (Ed.)The Neural Engineering Data Consortium at Temple University has been providing key data resources to support the development of deep learning technology for electroencephalography (EEG) applications [1-4] since 2012. We currently have over 1,700 subscribers to our resources and have been providing data, software and documentation from our web site [5] since 2012. In this poster, we introduce additions to our resources that have been developed within the past year to facilitate software development and big data machine learning research. Major resources released in 2019 include: ● Data: The most current release of our open source EEG data is v1.2.0 of TUH EEG and includes the addition of 3,874 sessions and 1,960 patients from mid-2015 through 2016. ● Software: We have recently released a package, PyStream, that demonstrates how to correctly read an EDF file and access samples of the signal. This software demonstrates how to properly decode channels based on their labels and how to implement montages. Most existing open source packages to read EDF files do not directly address the problem of channel labels [6]. ● Documentation: We have released two documents that describe our file formats and data representations: (1) electrodes and channels [6]: describes how to map channel labels to physical locations of the electrodes, and includes a description of every channel label appearing in the corpus; (2) annotation standards [7]: describes our annotation file format and how to decode the data structures used to represent the annotations. Additional significant updates to our resources include: ● NEDC TUH EEG Seizure (v1.6.0): This release includes the expansion of the training dataset from 4,597 files to 4,702. Calibration sequences have been manually annotated and added to our existing documentation. Numerous corrections were made to existing annotations based on user feedback. ● IBM TUSZ Pre-Processed Data (v1.0.0): A preprocessed version of the TUH Seizure Detection Corpus using two methods [8], both of which use an FFT sliding window approach (STFT). In the first method, FFT log magnitudes are used. In the second method, the FFT values are normalized across frequency buckets and correlation coefficients are calculated. The eigenvalues are calculated from this correlation matrix. The eigenvalues and correlation matrix's upper triangle are used to generate feature. ● NEDC TUH EEG Artifact Corpus (v1.0.0): This corpus was developed to support modeling of non-seizure signals for problems such as seizure detection. We have been using the data to build better background models. Five artifact events have been labeled: (1) eye movements (EYEM), (2) chewing (CHEW), (3) shivering (SHIV), (4) electrode pop, electrostatic artifacts, and lead artifacts (ELPP), and (5) muscle artifacts (MUSC). The data is cross-referenced to TUH EEG v1.1.0 so you can match patient numbers, sessions, etc. ● NEDC Eval EEG (v1.3.0): In this release of our standardized scoring software, the False Positive Rate (FPR) definition of the Time-Aligned Event Scoring (TAES) metric has been updated [9]. The standard definition is the number of false positives divided by the number of false positives plus the number of true negatives: #FP / (#FP + #TN). We also recently introduced the ability to download our data from an anonymous rsync server. The rsync command [10] effectively synchronizes both a remote directory and a local directory and copies the selected folder from the server to the desktop. It is available as part of most, if not all, Linux and Mac distributions (unfortunately, there is not an acceptable port of this command for Windows). To use the rsync command to download the content from our website, both a username and password are needed. An automated registration process on our website grants both. An example of a typical rsync command to access our data on our website is: rsync -auxv nedc_tuh_eeg@www.isip.piconepress.com:~/data/tuh_eeg/ Rsync is a more robust option for downloading data. We have also experimented with Google Drive and Dropbox, but these types of technology are not suitable for such large amounts of data. All of the resources described in this poster are open source and freely available at https://www.isip.piconepress.com/projects/tuh_eeg/downloads/. We will demonstrate how to access and utilize these resources during the poster presentation and collect community feedback on the most needed additions to enable significant advances in machine learning performance.more » « less
-
Simulation optimization involves optimizing some objective function that can only be estimated via stochastic simulation. Many important problems can be profitably viewed within this framework. Whereas many solvers—implementations of simulation-optimization algorithms—exist or are in development, comparisons among solvers are not standardized and are often limited in scope. Such comparisons help advance solver development, clarify the relative performance of solvers, and identify classes of problems that defy efficient solution, among many other uses. We develop performance measures and plots, and estimators thereof, to evaluate and compare solvers and diagnose their strengths and weaknesses on a testbed of simulation-optimization problems. We explain the need for two-level simulation in this context and provide supporting convergence theory. We also describe how to use bootstrapping to obtain error estimates for the estimators. History: Accepted by Bruno Tuffin, area editor for simulation. Funding: This work was supported by the National Science Foundation [Grants CMMI-2035086, CMMI-2206972, and TRIPODS+X DMS-1839346]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1261 ] or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at [ http://dx.doi.org/10.5281/zenodo.7329235 ].more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/ijoc.2022.1209
