Adaptive robust optimization with discrete uncertainty

In this paper, we study adaptive robust optimization problems with discrete uncertainty. We first show that an adaptive robust counterpart of the multiple knapsack problem includes $\Sigma_2^P$-hard problems. Then, we theoretically prove the validity of a non-trivial reformulation of this class of problems which can be solved by an enumerative algorithm akin to a Branch-and-Benders-cut … Read more

Adaptive robust optimization with objective uncertainty

In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints (including those linking the first and the second stages) are defined by convex functions and … Read more

A Reformulation Technique to Solve Polynomial Optimization Problems with Separable Objective Functions of Bounded Integer Variables

Real-world problems are often nonconvex and involve integer variables, representing vexing challenges to be tackled using state-of-the-art solvers. We introduce a mathematical identity-based reformulation of a class of polynomial integer nonlinear optimization (PINLO) problems using a technique that linearizes polynomial functions of separable and bounded integer variables of any degree. We also introduce an alternative … Read more

Robust Optimization in Nanoparticle Technology: A Proof of Principle by Quantum Dot Growth in a Residence Time Reactor

Knowledge-based determination of the best-possible experimental setups for nanoparticle design is highly challenging. Additionally, such processes are accompanied by noticeable uncertainties. Therefore, protection against these uncertainties is needed. Robust optimization helps determining such best possible processes. The latter guarantees quality requirements regardless of how the uncertainties, e.g. with regard to variations in raw materials, heat … Read more

Tractable Reformulations of Distributionally Robust Two-stage Stochastic Programs with $\infty- Distance

In the optimization under uncertainty, decision-makers first select a wait-and-see policy before any realization of uncertainty and then place a here-and-now decision after the uncertainty has been observed. Two-stage stochastic programming is a popular modeling paradigm for the optimization under uncertainty that the decision-makers first specifies a probability distribution, and then seek the best decisions … Read more

Computational Enhancement in the Application of the Branch and Bound Method for Linear Integer Programs and Related Models

In this paper, a reformulation that was proposed for a knapsack problem has been extended to single and bi-objective linear integer programs. A further reformulation by adding an upper bound constraint for a knapsack problem is also proposed and extended to the bi-objective case. These reformulations significantly reduce the number of branch and bound iterations … Read more

Team as a Service: Team Formation on Social Networks

Team as a Service (TaaS) is a new outsourcing service that enables on-demand creation and management of distributed teams for fast growing companies. The companies that use the TaaS model form a team according to the needs of a given project and provide managerial service throughout. Motivated by this new application, we study the Team … Read more

Divisive heuristic for modularity density maximization

In this paper we consider a particular method of clustering for graphs, namely the modularity density maximization. We propose a hierarchical divisive heuristic which works by splitting recursively a cluster into two new clusters by maximizing the modularity density, and we derive four reformulations for the mathematical programming model used to obtain the optimal splitting. … Read more

Partial Relaxation of Equality-constrained Programs

This paper presents a reformulation that is a natural “by-product” of the ‘variable endogenization’ process for equality-constrained programs. The method results a partial relaxation of the constraints which in turn confers some computational advantages. A fully-annotated example illustrates the technique and presents some comparative numerical results. Citation Siwale, I.: Partial Relaxation of Equality-constrained Programs. Technical … Read more

Solving Bilevel Mixed Integer Program by Reformulations and Decomposition

In this paper, we study bilevel mixed integer programming (MIP) problem and present a novel computing scheme based on reformulations and decomposition strategy. By converting bilevel MIP into a constrained mathematical program, we present its single-level reformulations that are friendly to perform analysis and build insights. Then, we develop a decomposition algorithm based on column-and-constraint … Read more