University of Florida

EIN 4343. Inventory and Supply Chain Systems. (Undergraduate senior core course)

Topics: Demand forecasting, inventory control, EOQ model, news-vendors problem, fundamentals of linear programming and network optimization, classical network flow models, the bullwhip effect, facility location problem, capaciated/uncapacitated lot-sizing problem, supply chain risk management.

University of Michigan

IOE 310: Introduction to Optimization. (Undergraduate core course)

Topics: Matrix operations, basic convex analysis, mathematical modeling with emphasis on linear programming; introduction to integer programming and network optimization; simplex algorithms, engineering applications, relevant software (e.g., Excel solver, AMPL).

IOE 510 (Math 561) (OMS 518): Linear Programming I. (Master-level graduate course)

Topics: Mathematical modeling, linear algebra and matrices, the simplex algorithm, duality theory and optimality conditions, sensitivity analysis, network flows, combinatorial optimization, computations in AMPL, basics in decomposition, integer programming, and stochastic optimization.

IOE 512: Dynamic Programming (Master-level graduate course)

Topics: The techniques of recursive optimization and their use in solving multistage decision problems, applications to various types of problems, including an introduction to Markov decision processes and reinforcement learning.

IOE 612: Network Flows. (Advanced graduate course)

Topics: Basic graph theories, minimum cost flow, shortest path, minimum spanning tree, maximum flow (minimum cut), network simplex method, network interdiction and its applications in homeland security, social networks, epidemic control.

IOE 691: Stochastic and Robust Optimization. (Advanced graduate course)

Co-instruct with Prof. Marina Epelman.

Topics: Sampling methods, stochastic mixed-integer programming models, decomposition methods, large-scale optimization, stochastic dynamic programming, approximation algorithms, (joint) chance-constrained programming, theories and applications of robust optimization, discussions of data driven models and relationship between different stochastic programs.

ENGR 455: Multidisciplinary Project Design. (Undergraduate multidisciplinary course)

  • Habitat for Humanity - Disaster and shelter relief in Haiti (2012)
    Co-instruct with Prof. Amy Cohn and James Goebel.

    • Topics: This course collaborates with the Habitat for Humanity International, and optimize transitional shelter construction in Haiti. We design an intervention process for an at-scale post-disaster shelter intervention that (a) provides immediate relief in the form of low-cost, easily-produced transitional shelter and (b) integrates seamlessly into an on-going incremental shelter process based on local housing resources, and that leverages and augments livelihoods and micro-entrepreneurship. Course contents include decision tree analysis, supply chain guideline developments based on local inputs and outsourcing risk management.

  • Union Pacific - Optimal Utilization and Management of Refrigerated Train Car Fleet (2016)

    • Topics: Union Pacific is the largest rail road in North America moving many different types of cargo using many different types of equipment. Refrigerated cars are particularly expensive assets. Thus, it is desired to use them in an optimal manner, and make sure that they are optimally fueled for transcontinental hauls, taken off-line for timely maintenance, etc. All operations have a cost associated with them. Through the collaboration with Union Pacific, we analyze all facets of the refrigerated train car fleet to optimize efficiency, including total costs and operational modeling.

Outreach Activities

Power Optimization Game

The Power Optimization Game is an Excel macro-based multiplayer-game designed for high school age students (Grades 9-12). The purpose of the game is for students to compete and provide adequate power for a random system, while trying to use the lowest dollar cost and lowest carbon emissions cost. The game demonstrates concepts of varying loads, reserves, and uncertainty. Students have the option to choose between wind power, coal, and gas as sources for electricity.

This game was created and designed by Joy Chang and Spencer Maroukis. Further modifications were made by Fanny Pinto Delgado and Abdi Zeynu. The faculty sponsors for this project were Siqian Shen, Johanna Mathieu, and Heath Hofmann. This game was used for the 2016 Power Up Tech Camp and the 2016 Tech Day Design Competition.

The creation of this game is part of our NSF CyberSEES project (CCF-1442495) and the involved undergraduate students were supported by its REU supplement.

If you are interested in using it for teaching/research purpose, please download all the materials and game instructions at: Power Game