Zijiu Lyu, Jackson Gao
Abstract: We build a (Markovian) stochastic control model to investigate the optimal capacity expansion policy of a manufacturing company whose carbon emission is regulated by the government. For simplicity, our model is built over discrete and finite time stamps. At each time stamp, the company’s state is represented by its installed total capacity and product inventories, and the company imposes feedback controls to guide its capacity expansion and production in the next time period. We assume that it takes one time period for the newly installed capacity and newly produced products to become available for use/sale. The only uncertainty in our model originates from the market demand, which is assumed to be an increasing stochastic process. The objective of the company is to maximize the expected cumulative profit over the finite time horizon.
Charlie Gulian, Jon Bodine, Ed Dowling
Abstract: Our electricity system capacity expansion model is a two-stage stochastic program. The first-stage decisions are the investment decisions, i.e., which resources to build, and the second-stage decisions are operational decisions i.e., how to operate the system once resources are built. The scenarios within the stochastic program correspond to sets of operating conditions which may be encountered over some period – in our case, over a day – of the planning horizon. The operating conditions within a scenario are defined by (1) the realization of electricity demand and (2) the realization of generation resource availability factors over a day. Some generation resources such as gas or nuclear are considered “firm” and are always fully available in each scenario, while others such as wind or solar are considered “variable” and have availability which fluctuates randomly over time.
Junyu Guo, Ziheng Cheng, Sansen Wei
Abstract: Consider a manufacturing company that has been operating under its original operations mode for years, with corresponding production capacity, unit cost, and unit emission levels. The company is now considering an expansion of its production capacity. Additionally, the company is evaluating the adoption of more advanced technologies to reduce carbon emissions. Specifically, while the new technology offers a lower carbon emission level, it comes with higher setup and production costs. The goal of this project is to design an optimal capacity expansion policy that improves the companys overall performance by balancing operational costs and carbon emissions.
Jeshua Gustafson
Abstract: In modern luxury automotive manufacturing, balancing sustainability with evolving engineering requirements presents significant challenges. We examine these challenges through a simulated production run of 80 vehicles over a 120-week production horizon, implemented using Python with Gurobi optimization. The manufacturing system employs five distinct processes with increasing carbon intensity, where each process has an associated warning state indicating potential revision requirements. Initial processes prioritize minimal carbon emissions while enabling production of geometrically complex parts. However, engineering revisions during production may require switching to progressively more carbon-intensive processes to meet emerging requirements.