The classical assembly line balancing problem consists of assigning assembly work to workstations. In the presence of setup times that depend on the sequence of tasks assigned to each workstation, the problem becomes more complicated given that two interdependent problems, namely assignment and sequencing, must be solved simultaneously. The hierarchical nature of these two problems … Read more
Distributionally robust stochastic optimization (DRSO) is an approach to optimization under uncertainty in which, instead of assuming that there is an underlying probability distribution that is known exactly, one hedges against a chosen set of distributions. In this paper, we consider sets of distributions that are within a chosen Wasserstein distance from a nominal distribution. … Read more
One of the fundamental tasks in compressed sensing is finding the sparsest solution to an underdetermined system of linear equations. It is well known that although this problem is NP-hard, under certain conditions it can be solved by using a linear program which minimizes the 1-norm. The restricted isometry property has been one of the … Read more
Mixed-integer semidefinite programs arise in many applications and several problem-specific solution approaches have been studied recently. In this paper, we investigate a generic branch-and-bound framework for solving such problems. We first show that strict duality of the semidefinite relaxations is inherited to the subproblems. Then solver components like dual fixing, branching rules, and primal heuristics … Read more
A discretization-based algorithm for the global solution of semi-infinite programs (SIPs) is proposed, which is guaranteed to converge to a feasible, ε-optimal solution finitely under mild assumptions. The algorithm is based on the hybridization of two existing algorithms. The first algorithm (Mitsos in Optimization 60(10–11):1291–1308, 2011) is based on a restriction of the right-hand side … Read more
Problems aiming at finding budget constrained optimal upgrading schemes to improve network performance have received attention over the last two decades. In their general setting, these problems consist of designing a network and, simultaneously, allocating (limited) upgrading resources in order to enhance the performance of the designed network. In this paper we address two particular … Read more
This work discusses the following general packing problem-class: given a finite collection of d-dimensional spheres with arbitrarily chosen radii, find the smallest sphere in R^d that contains the entire collection of these spheres in a non-overlapping arrangement. Generally speaking, analytical solution approaches cannot be expected to apply to this general problem-type, except for very small … Read more
We present a new primal-dual algorithm for computing the value of the Lagrangian dual of a stochastic mixed-integer program (SMIP) formed by relaxing its nonanticipativity constraints. The algorithm relies on the well-known progressive hedging method, but unlike previous progressive hedging approaches for SMIP, our algorithm can be shown to converge to the optimal Lagrangian dual … Read more
Most path-following algorithms for tracing a solution path of a parametric nonlinear optimization problem are only certifiably convergent under strong regularity assumptions about the problem functions, in particular, the linear independence of the constraint gradients at the solutions, which implies a unique multiplier solution for every nonlinear program. In this paper we propose and prove … Read more
In this note, we consider the Multi-period Minimum Spanning Tree Problem (MMST), a variant of the well known Minimum Spanning Tree Problem (MST), that consists in the fol- lowing. Given a connected and undirected graph G and a finite discrete time horizon, one has to schedule the moment in time edges are added to a … Read more