On Electricity Market Equilibria with Storage: Modeling, Uniqueness, and a Distributed ADMM

We consider spot-market trading of electricity including storage operators as additional agents besides producers and consumers. Storages allow for shifting produced electricity from one time period to a later one. Due to this, multiple market equilibria may occur even if classical uniqueness assumptions for the case without storages are satisfied. For models containing storage operators, … Read more

A Robust Optimization Approach for the Unrelated Parallel Machine Scheduling Problem

In this paper, we address the Unrelated Parallel Machine Scheduling Problem (UPMSP) with sequence- and machine-dependent setup times and job due-date constraints. Different uncertainties are typically involved in real-world production planning and scheduling problems. If ignored, they can lead to suboptimal or even infeasible schedules. To avoid this, we present two new robust optimization models … Read more

New bounds for nonconvex quadratically constrained quadratic programming

In this paper, we study some bounds for nonconvex quadratically constrained quadratic programs. Recently, Zamani has proposed a dual for linearly constrained quadratic programs, where Lagrange multipliers are affine functions. By using this method, we propose two types of bounds for quadratically constrained quadratic pro- grams, quadratic and cubic bounds. For quadratic bounds, we use … Read more

Limited-Memory BFGS with Displacement Aggregation

A displacement aggregation strategy is proposed for the curvature pairs stored in a limited-memory BFGS (a.k.a. L-BFGS) method such that the resulting (inverse) Hessian approximations are equal to those that would be derived from a full-memory BFGS method. This means that, if a sufficiently large number of pairs are stored, then an optimization algorithm employing … Read more

Iteration and evaluation complexity for the minimization of functions whose computation is intrinsically inexact

In many cases in which one wishes to minimize a complicated or expensive function, it is convenient to employ cheap approximations, at least when the current approximation to the solution is poor. Adequate strategies for deciding the accuracy desired at each stage of optimization are crucial for the global convergence and overall efficiency of the … Read more

Snow Plow Route Optimization: A Constraint Programming Approach

Many cities have to cope with annual snowfall, but are struggling to manage their snow plowing activities efficiently. Despite the fact that winter road maintenance has been the subject of many research papers over the last 3 decades, very few practical decision support systems have been developed to deal with the complex decision problems involved … Read more

Vehicle Routing Problem with Steep Roads

Most routing decisions assume that the world is flat meaning that routing costs are modelled as a weighted sum of total distance and time spend by delivery vehicles. However, this assumption does not apply for urban logistics operations in cities with significant altitude differences. We fill a space in the VRP literature and model routing … Read more

Γ-Robust Linear Complementarity Problems

Complementarity problems are often used to compute equilibria made up of specifically coordinated solutions of different optimization problems. Specific examples are game-theoretic settings like the bimatrix game or energy market models like for electricity or natural gas. While optimization under uncertainties is rather well-developed, the field of equilibrium models represented by complementarity problems under uncertainty … Read more

Dynamic optimization for airline maintenance operations

The occurrence of unexpected aircraft maintenance tasks can produce expensive changes in an airline’s operation. When it comes to critical tasks, it might even cancel programmed flights. Despite of it, the challenge of scheduling aircraft maintenance operations under uncertainty has received limited attention in the scientific literature. We study a dynamic airline maintenance scheduling problem, … Read more

Are we there yet? Manifold identification of gradient-related proximal methods

In machine learning, models that generalize better often generate outputs that lie on a low-dimensional manifold. Recently, several works have separately shown finite-time manifold identification by some proximal methods. In this work we provide a unified view by giving a simple condition under which any proximal method using a constant step size can achieve finite-iteration … Read more