In this article we study the effectiveness of alternative integer programming formulations for area constrained harvest scheduling, known as the area restriction model (ARM). Empirical assessment of the effect of area constraints on the optimal objective value of these alternative approaches is carried out, focusing on the root Linear Programming relaxation. We also examine how this relates to ...

The IDP knowledge base system currently usesMiniSAT(ID) as its backend Constraint Programming (CP) solver. A few similar systems have used a Mixed Integer Programming (MIP) solver as backend. However, so far little is known about when the MIP solver is preferable. This paper explores this question. It describes the use of CPLEX as a backend for IDP and reports on experiments comparing both back...

We propose two exact approaches for non-convex quadratic integer minimization subject to linear constraints where lower bounds are computed by considering ellipsoidal relaxations of the feasible set. In the first approach, we intersect the ellipsoids with the feasible linear subspace. In the second approach we penalize exactly the linear constraints. We investigate the connection between both a...

We consider several formulations of two-dimensional two-stage constrained cutting, where the number of variables is polynomial. Some new models with variable strip widths are developed. Symmetries in the search space are eliminated by lexicographic constraints which are already known from the literature. However, previously known models with fixed strip widths are shown to be more effective. Th...

In this paper, we analyze in depth a simplicial decomposition like algorithmic framework for large scale convex quadratic programming. In particular, we first propose two tailored strategies for handling the master problem. Then, we describe a few techniques for speeding up the solution of the pricing problem. We report extensive numerical experiments on both real portfolio optimization and gen...

Shoham In this chapter we consider the empirical hardness of the winner determination problem. We identify distribution-nonspecific features of data instances and then use statistical regression techniques to learn, evaluate and interpret a function from these features to the predicted hardness of an instance, focusing mostly on ILOG's CPLEX solver. We also describe two applications of these mo...

Task allocation problems consist of assigning a set of tasks to one of several processors such that the associated inter-task communication and processing costs are minimized. Modeled as a nonlinear integer program, these problems are computationally challenging. This paper illustrates how the unconstrained quadratic binary program can be used to effectively model and efficiently solve large in...