A first-order primal-dual algorithm with linesearch

The paper proposes a linesearch for the primal-dual method. Each iteration of the linesearch requires to update only the dual (or primal) variable. For many problems, in particular for regularized least squares, the linesearch does not require any additional matrix-vector multiplications. We prove convergence of the proposed method under the standard assumptions. We also show … Read more

Vehicle Routing Problems with Time Windows and Convex Node Costs

We consider a variant of the vehicle routing problems with time windows, where the objective includes the inconvenience cost modeled by a convex function on each node. We formulate this mixed integer convex program using a novel set partitioning formulation, by considering all combinations of routes and block structures over the routes. We apply a … Read more

Designing Response Supply Chain Against Bioattacks

Bioattacks, i.e., the intentional release of pathogens or biotoxins against humans to cause serious illness and death, pose a significant threat to public health and safety due to the availability of pathogens worldwide, scale of impact, and short treatment time window. In this paper, we focus on the problem of prepositioning inventory of medical countermeasures … Read more

The Stochastic Multistage Fixed Charge Transportation Problem: Worst-Case Analysis of the Rolling Horizon Approach

We introduce the Stochastic multistage fixed charge transportation problem in which a producer has to ship an uncertain load to a customer within a deadline. At each time period, a fixed transportation price can be paid to buy a transportation capacity. If the transportation capacity is used, the supplier also pays an uncertain unit transportation … Read more

A Study of the Difference-of-Convex Approach for Solving Linear Programs with Complementarity Constraints

This paper studies the difference-of-convex (DC) penalty formulations and the associated difference-of-convex algorithm (DCA) for computing stationary solutions of linear programs with complementarity constraints (LPCCs). We focus on three such formulations and establish connections between their stationary solutions and those of the LPCC. Improvements of the DCA are proposed to remedy some drawbacks in a … Read more

Elementary polytopes with high lift-and-project ranks for strong positive semidefinite operators

We consider operators acting on convex subsets of the unit hypercube. These operators are used in constructing convex relaxations of combinatorial optimization problems presented as a 0,1 integer programming problem or a 0,1 polynomial optimization problem. Our focus is mostly on operators that, when expressed as a lift-and-project operator, involve the use of semidefiniteness constraints … Read more

Stochastic Optimization of Vaccine Vial Replenishment

We present a two-stage stochastic linear programming model to manage the inventory of vaccine vials in developing countries. In these countries immunization programs often involve targeted outreach services in remote locations. Organizations managing these programs are in need of tools which identify inventory replenishment and vaccine utilization plans to minimize costs and wastage while achieving … Read more

Decomposition of loosely coupled integer programs: A multiobjective perspective

We consider integer programming (IP) problems consisting of (possibly a large number of) subsystems and a small number of coupling constraints that link variables from different subsystems. Such problems are called loosely coupled or nearly decomposable. Motivated by recent developments in multiobjective programming (MOP), we develop a MOP-based decomposition algorithm to solve loosely coupled IPs. … Read more

Improved Handling of Uncertainty and Robustness in Set Covering Problems

This paper studies the emergency service facility location problem in an uncertain environment. The main focus is the integration of uncertainty regarding the covered area due to uncertain traveling times. Previous approaches only consider either probabilistic or fuzzy optimization to cope with uncertainty. However, in many real-world problems the required statistical parameters are not precisely … Read more