Optimal Power Flow in Distribution Networks under N-1 Disruptions: A Multi-stage Stochastic Programming Approach

Contingency research to find optimal operations and post-contingency recovery plans in distribution networks has gained a major attention in recent years. To this end, we consider a multi-period optimal power flow (OPF) problem in distribution networks, subject to the N-1 contingency where a line or distributed energy resource fails. The contingency can be modeled as … Read more

Improving solve times of stable matching problems through preprocessing

We present new theory, heuristics and algorithms for preprocessing instances of the Stable Marriage with Ties and Incomplete lists (SMTI), the Hospitals/Residents with Ties (HRT), and the Worker-Firms with Ties (WFT) problems. We show that instances of these problems can be preprocessed by removing from the preference lists of some agents entries that correspond to … Read more

A Polyhedral Approach to Bisubmodular Function Minimization

We consider minimization problems with bisubmodular objective functions. We propose a class of valid inequalities, which we call the poly-bimatroid inequalities and prove that these inequalities, along with trivial bound constraints, fully describe the convex hull of the epigraph of a bisubmodular function. We develop a cutting plane algorithm for general bisubmodular minimization problems using … Read more

Service Network Design for Same-Day Delivery with Hub Capacity Constraints

We study a new service network design problem for an urban same-day delivery system in which the number of vehicles that can simultaneously load or unload at a hub is limited. Due to the presence of both time constraints for the commodities and capacity constraints at the hubs, it is no longer guaranteed that a … Read more

Practical Risk Modeling for the Stochastic Technician Routing and Scheduling Problem

Planning for uncertainty is crucial for finding good, stable solutions. However, it is often impractical to incorporate stochastic elements into a large production system. Our paper tackles this issue in the context of the Technician Routing and Scheduling Problem (TRSP). We develop a set of techniques, based on phase-type distributions, to quickly and accurately evaluate … Read more

Variance Reduction of Stochastic Gradients Without Full Gradient Evaluation

A standard concept for reducing the variance of stochastic gradient approximations is based on full gradient evaluations every now and then. In this paper an approach is considered that — while approximating a local minimizer of a sum of functions — also generates approximations of the gradient and the function values without relying on full … Read more

A mixed-integer linear programming approach for the T-row and the multi-bay facility layout problem

We introduce a new facility layout problem, the so-called T-Row Facility Layout Problem (TRFLP). The TRFLP consists of a set of one-dimensional departments with pairwise transport weights between them and two orthogonal rows which form a T such that departments in different rows cannot overlap. The aim is to find a non-overlapping assignment of the … Read more

Complexity of cutting planes and branch-and-bound in mixed-integer optimization

We investigate the theoretical complexity of branch-and-bound (BB) and cutting plane (CP) algorithms for mixed-integer optimization. In particular, we study the relative efficiency of BB and CP, when both are based on the same family of disjunctions. We extend a result of Dash to the nonlinear setting which shows that for convex 0/1 problems, CP … Read more

Geometry of First-Order Methods and Adaptive Acceleration

First-order operator splitting methods are ubiquitous among many fields through science and engineering, such as inverse problems, signal/image processing, statistics, data science and machine learning, to name a few. In this paper, we study a geometric property of first-order methods when applying to solve non-smooth optimization problems. With the tool of “partial smoothness”, we design … Read more