Lewis Ntaimo, Ph.D.
Will be determined based upon researchers' availabilities
Students stand to gain significant computational research experience, co-authorship of publication(s), and employable skills relevant to industry and graduate school
Researchers with enthusiasm, strong work ethic, and who have preferably declared ISEN, CHEN, IDIS, SCMT, PETE, MATH, STAT or CSE
Applications will accepted until Sunday, June 3 at 7:00 p.m, interested students should please include a resume, transcript, and 3-5 concise bullet points on what they believe they could best contribute to the team in their email application
The large number of interactions present in supply chains renders their synthesis and optimal design challenging. This is further complicated by the existence of several sources of variability such as uncertainty in feedstock quality, uncertainty in the prices of intermediate products, and uncertainty in consumer demand. Given these uncertainties, the simulation and optimization models used for supply chain modelling become non-deterministic and the decisions of optimally allocating resources, maximizing network flows, and minimizing inventory levels require the use of sophisticated mathematical models to maximize profit while minimizing the value at risk. This project proposes to develop a two-stage stochastic optimization model with continuous recourse to assist in the planning and scheduling of production and storage. Key components of the project include: demand and price forecasting, model formulation based on integer programming, extension of the model to multi-objective optimization, solution algorithm development, model implementation, and assessment of results. Researchers selected can expect a relatively gentle introduction to computational research and a chance to apply state of the art mathematical methods to develop tailored and actionable solutions to real world problems. Depending on the team’s interests, the project has scope to be extended to consider big data analytics, supply chain resilience, and nonlinear model reduction techniques. While some familiarity with algebra and statistics and a mathematical inclination is expected, no prior programming experience is required and researchers will be brought up to speed on the theory, techniques, software and languages they will be using (ex. GAMS, MATLAB, Python, or AMPL).