In proton therapy treatment planning, the aim is to ensure tumor control while sparing the various surrounding risk structures. The biological effect of the irradiation depends on both physical dose and linear energy transfer (LET). In order to include LET alongside physical dose in plan creation, we propose to formulate the proton treatment planning problem … Read more
We consider a type of routing problems common in defence and security, in which we control a fleet of unmanned aerial vehicles (UAVs) that have to reach one or more target locations without being detected by an adversary. Detection can be carried out by a variety of sensors (radio receivers, cameras, personnel, etc) placed by … Read more
We consider bilevel programs where a single leader interacts with multiple followers who are coupled by a Nash equilibrium problem at the lower level. We generalize the value function reformulation to include multiple followers. This allows us to propose a convergent method based on the sequential convex approximation paradigm, and study the (exact or inexact) … Read more
This paper is concerned with a mean-variance portfolio optimization model with cardinality constraint for generating high-quality lists of recommendations. It is usually difficult to accurately estimate the rating covariance matrix required for mean-variance portfolio optimization because of a shortage of observed user ratings. To improve the accuracy of covariance matrix estimation, we apply shrinkage estimation … Read more
The size and complexity of modern astronomical surveys has grown to the point where, in many cases, traditional human scheduling of observations is tedious at best and impractical at worst. Automated scheduling algorithms present an opportunity to save human effort and increase scientific productivity. A common scheduling challenge involves determining the optimal ordering of a … Read more
Increasing ocean plastic pollution is irreversibly harming ecosystems and human economic activities. We partner with a non-profit organization and use optimization to help clean up oceans from plastic faster. Specifically, we optimize the route of their plastic collection system in the ocean to maximize the quantity of plastic collected over time. We formulate the problem … Read more
This paper investigates the design and analysis of price formation in wholesale electricity markets given variability, uncertainty, non-convexity, and intertemporal operating constraints. The paper’s primary goal is to develop a framework to assess the many resource participation models, reserve product definitions, and enhanced pricing methods that have arisen in U.S. systems, especially in the context … Read more
For a system to stay operational, maintenance of its components is required and to maximize the operational readiness of a system, preventive maintenance planning is essential. There are two stakeholders—a system operator and a maintenance workshop—and a contract regulating their joint activities. Each contract leads to a bi-objective optimization problem. Components that require maintenance are … Read more
Adjustable robust optimization (ARO) is a powerful tool to model problems that have uncertain data and that feature a two-stage decision-making process. Computationally, they are often addressed using the column-and-constraint generation (CCG) algorithm introduced by Zeng and Zhao (2013). While it was empirically shown that the algorithm scales well if all second-stage decisions are continuous, … Read more
In this paper, we consider a partial sparse and partial group sparse optimization problem, where the loss function is a continuously differentiable function (possibly nonconvex), and the penalty term consists of two parts associated with sparsity and group sparsity. The first part is the $\ell_0$ norm of ${\bf x}$, the second part is the $\ell_{2,0}$ … Read more