Lab 1 – SOLAR Lab

Systems Optimization Learning and Renewable Energy (SOLAR) Lab

Location: IGRM 4207B

Scope:

The Systems Optimization Learning and Renewable Energy Research (SOLAR) lab supports undergraduate and graduate research in Operations Research (OR) using methodologies such as:

1. Prescriptive Analytics (PA):

    • Stochastic programming,
    • Dynamic programming,
    • Non-linear programming,
    • Linear programming,
    • Integer programming,
    • Heuristics and meta-heuristics optimization.

2. Data Analytics/Data Science (DS):

    • Data mining,
    • Bootstrapping,
    • Multiple imputation.

3. Parallel Computing (PC)  to accelerate the performance of algorithms to solve NP-hard OR problems.

    • Multiple CPUs using MPI,
    • Multiple CPUs using OpenMP,
    • Multiple GPU using CUDA,

4. Machine Learning (ML)

    • Supervised learning
      • Feature selection using heuristic optimization
      • Classification and feature selection methods using nearest neighbor, linear regression, ridge regression, logistic regression, support vector machines, naive Bayes, decision trees, ensemble methods
    • Unsupervised learning:
      • Classification and feature selection methods using K-means clustering and Gaussian mixture models, Principal component analysis (PCA) and Kernel PCA
    • Deep learning
    • Reinforced Learning

Problem Domains:

    • Renewable Energy Systems Planning (wind energy, solar energy, energy storage systems, smart contracts)
    • Logistics (e.g., vehicle routing, facility location, facility layout, humanitarian logistics)
    • Supply Chain Optimization (e.g., agile, sustainable and socially responsible supply chain design),
    • Cybersecurity of cyber physical systems,
    • Service systems optimization (healthcare, food banks, recycling industries).

In solving large-scale logistics, industrial and sustainability problems, it is common to merge Data Analytics, Prescriptive Analytics and High-Performance Computing using multiple CPUs and/or GPUs.

Students Activities:

Students working in the lab under Dr. Novoa’s supervision  present research at national and international conferences such as:

    • Institute for the Operations Research and Management Science (INFORMS),
    • Institute of Industrial and Systems Engineers (IISE),
    • Decision Sciences Institute (DSI),
    • IEEE International Conference on Industrial Informatics (INDIN)
    • IEEE Conference on Communications and Network Security (CNS),
    • Super Computing (SC 18)
    • The International Conference for High Performance Computing, Networking, Storage, and Analysis, Practice & Experience in Advanced Research Computing (PEARC),
    • Extreme Science and Engineering Discovery Environment (XSEDE), and
    • Society of Women Engineers (SWE) Local

Students also present research work at Texas State University internal conferences such as:

    • International Graduate Research Conference – Texas State University
    • Honors College Undergraduate Research Conference (URC) – Texas State University
    • Women in Science and Engineering (WiSE) – Texas State University

Students Honors and Awards:

    • “Planning Distributed Generation Systems Powered with Wind Turbines using a Stochastic Programming Model,” Fantacher Islam, Clara Novoa, Francis Mendez Mediavilla. Finalist: Best Student Paper Competition Presentations. Special Session, Energy Systems Division, Institute of Industrial and Systems Engineers (IISE) Annual Conference & Expo, May 22, 2023, New Orleans, LA. https://iise.confex.com/iise/2023/meetingapp.cgi/Paper/1860
    • “Aggregate Production Planning with Renewable Energy,” Sayed Rezwanul Islam, Clara Novoa, Tongdan Jin. First Place Annual Minority Issues Forum (MIF) Student Poster Competition, INFORMS Annual Conference, October 24, 2021, Anaheim, CA https://connect.informs.org/minorityissuesforum/awards/postercomp/pastposter 
    • “A Multi-stage Stochastic Model for Production Planning with Renewable Energy Generation,” Atamgbo Ayuwu, Clara Novoa, Tongdan Jin. People’s Choice Poster Award Winner, Women in Science and Engineering (WiSE) Conference, Texas State University, March 6, 2020, San Marcos, TX. https://wise.cose.txstate.edu/WiSE-Events/WiSE-Conference-Archive/archive-2020conference.html
    •  “Stochastic Models for Planning Distributed Wind Generation based on Data Analytics” Temitope Runsewe, Clara Novoa, Tongdan Jin.  Third place winner lighting talk and poster presentation at the 2019 Society of Women Engineers (SWE) Local Conference Collegiate Competition, February 9, 2019, Baltimore, MD. (Link not available on the Web anymore unfortunately)
    •  “Facility Layout at McNeil Warehouse Goodwill Industries” Clara Novoa and undergraduate student Nhi Mai won the Honorable Mention (2nd place) in the Decision Sciences Institute Annual Meeting Best Application Paper Award competition in Fall 2012. (Link not available on the web anymore). Complete paper citation: Novoa, C. M., & Mai, N. (2012). Facility Layout at McNeil Warehouse Goodwill Industries. In Decision Sciences Institute 43rd Annual Meeting (pp. 37501–37514).

Hardware:

Computers and Printers

      • 3 Dell desktops (one i7-97000@3.06 GHz, one i7-87000 @3.2 GHz, and one i7-8700 @3.7 GHz; all of them with 32 GB RAM, 1TB  Hard Drive, and  two wide-screen monitors) running Windows 11 (64 bit)
      • 1 Brother Color Laserjet Printer HL-3170CDW

Software:

General use:

      • 3 Microsoft Office Enterprise (Includes Microsoft Visio and Microsoft Project)

Prescriptive Analytics (i.e., Operations Research/Optimization):

      • 3 Gurobi PY running in the Anaconda Platform through Spyder and JupyterLab or Jupyter Network
      • 3 AMPL API  for Python (AMPLPY) running in the Anaconda Platform through Spyder, JupyterLab, or Jupyter Network
        • AMPL can use the CPLEX, Gurobi and Xpresss solvers for linear programming,  and  Knitro and Baron solvers for non-linear programming
      • 1 Knitro from Artelys Python API running in the Anaconda Platform through Spyder
      • 1 Analytic Solver Platform
      • 2 GAMS
      • 3 FICO Xpress
      • 3 Mathematica
      • 3 Matlab

Data analytics/data science:

      • 3 Anaconda Platform
      • 3 Minitab
      • 2 Stata

Machine Learning:

      • Python (Pytorch)

Tools for connecting to clusters and remote servers:

      • 2 Putty
      • 2 Filezilla

Clusters and Servers Accessible by Remote Connection:

      • LEAP2 –  High Performance Computing Cluster – Texas state University
        It is a cluster managed by the Division of Information Technology (DOIT). The complete capabilities of the cluster are available at https://doit.txst.edu/hpc.html
      •  Texas Advanced Computing Center (TACC) University of Texas at Austin – Pickle Research Center
        More information at https://docs.tacc.utexas.edu/hpc/frontera/

 Faculty:

      • Clara Novoa, Professor at the Ingram School of Engineering Industrial Engineering Program. Dr. Novoa’s research covers the following methodologies and applications:
        • Methodologies: operations research including data analytics/data science, predictive analytics using machine learning, and prescriptive analytics using stochastic programming, dynamic programming, non-linear programming, linear programming, integer programming heuristics, simulation optimization and  also machine learning . She also has researched on the use of high-performance computing for solving large-scale OR problems
        • Applications:  renewable energy planning (e.g., wind energy, solar energy, energy storage systems, electric vehicles, smart contracts), logistics  (e.g., vehicle routing, facility location, facility layout, humanitarian logistics), optimization of manufacturing, service, and supply chain systems (e.g., designing green supply chains, optimizing acquisition and distribution of food managed by charitable organizations. supplier selection for electronic supply chains, and optimizing cybersecurity of cyber-physical systems.

Students Involved:

  • Industrial Engineering Graduate Students:
      • Mukitul Islam (Fall 24 – Present)
      • Anil Kumar Palika (Fall 24 – Present)
      • Jakir Hassan (Spring  23 – Summer 24)
      • Md Fantacher Islam (Summer 22 – Summer 23)
      • Sayed Rezwanul Islam (Spring 20 – Summer 21)
      • Atamgbo Ayuwu (Summer 19 – Fall 20 )
      • Divya Zala (Fall 18 – Fall 20)
      • Temitope Runsewe (Spring 18 – Summer 19)
      • Khan Siddique, Industrial Engineering (Spring 17 – Fall 17)
      • Gowtham Balachandran, Industrial Engineering (Spring 16 – Spring 18)
  • Computer Science Graduate Students:
      • Chandra Kolla, Graduate Student Computer Science Department (Fall 14 – Fall 15)
      • Abhilash Chaparala, Graduate Student Computer Science Department (Summer 13 – Summer 14)
      • Sujeeth Pasham, Graduate Student Computer Science Department (Spring 12 – Spring 13)
  • Engineering Technology Graduate Students:
      • Fei Sun, Graduate Student Engineering Technology Department (Spring 13 – Spring 15)
      • Hayden Beauchamp, Graduate Student Engineering Technology Department (Spring 12 – Spring 13)
  • Industrial Engineering Undergraduate Students:
      • Brandon McCarthy (Fall 21 – June 22)
      • Jordan Givens (Fall 17 – Spring 18)
      • Karina Yanes Portillo (Fall 14 – Spring 17)
      • Chandler Franklin (Fall 14 – Spring 15)
      • Cameron Paiga (Fall 13 – Spring 14)
      • Nhi Mai (Fall 11 – Spring 13)
      • Molly McDaniel (Fall 11 – Spring 12)
      • Suleima Alkusari (Fall 10 – Spring 11)
  • Computer Science Undergraduate Students:
      • Abigail Barron (Spring 16 – Fall 17)

Publications:

To see publications from this lab please go to the publications tab and look for the names of the students listed above.