20 citations · 71 across the 16 of their papers we have counts for
12 papers · 1 filter
Solution Methods for Infinite-Dimensional Generalized Disjunctive Programming
Daniel Nguyen, Joshua L. Pulsipher
Generalized disjunctive programming (GDP) expresses mixed discrete-continuous decisions through Boolean indicators and disjunctions, and can be systematically solved via a library…
A Digital Twin Simulator of a Pastillation Process with Applications to Automatic Control based on Computer Vision
Leonardo D. González, Joshua L. Pulsipher, Shengli Jiang +2
We present a digital-twin simulator for a pastillation process. The simulation framework produces realistic thermal image data of the process that is used to train computer vision-…
Event Constrained Programming
Daniel Ovalle, Stefan Mazzadi, Carl D. Laird +2
In this paper, we present event constraints as a new modeling paradigm that generalizes joint chance constraints from stochastic optimization to (1) enforce a constraint on the pro…
Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions
Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye +4
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unp…
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom +1
We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We sho…
Random Field Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of r…
New Measures for Shaping Trajectories in Dynamic Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We propose a new class of measures for shaping time-dependent trajectories in dynamic optimization (DO). The proposed measures are analogous to risk measures used in stochastic opt…
A Unifying Modeling Abstraction for Infinite-Dimensional Optimization
Joshua L. Pulsipher, Weiqi Zhang, Tyler J. Hongisto +1
Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional d…
Measuring and Optimizing System Reliability: A Stochastic Programming Approach
Joshua L. Pulsipher, Victor M. Zavala
We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subjec…
A Scalable Stochastic Programming Approach for the Design of Flexible Systems
Joshua L. Pulsipher, Victor M. Zavala
We study the problem of designing systems in order to minimize cost while meeting a given flexibility target. Flexibility is attained by enforcing a joint chance constraint, which…
A Computational Framework for Quantifying and Analyzing System Flexibility
Joshua L. Pulsipher, Daniel Rios, Victor M. Zavala
We present a computational framework for analyzing and quantifying system flexibility. Our framework incorporates new features that include: general uncertainty characterizations t…
A Mixed-Integer Conic Programming Formulation for Computing the Flexibility Index under Multivariate Gaussian Uncertainty
Joshua L. Pulsipher, Victor M. Zavala
We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility i…