Chair: Michael Poss, CNRS, UTC, France
An empirical design of a column generation algorithm applied to a management zone delineation problem
Victor M. Albornoz (email@example.com), Linco Ñanco (firstname.lastname@example.org)
This paper develops and compares different strategies which involve column generation algorithm for the problem solving of agricultural field partition in rectangular shape management zones, represented by an integer linear programming model. The main objective of this paper is to select an efficient alternative to solve the linear relaxation of the problem, which does not alter significantly quality of the integer solution proposed. The paper also describes model addressed, the solving method studied and computational results for a set of various size problems
Keywords: column generation, OR in Agriculture, Integer programming
Decomposition for adjustable robust linear optimization subject to uncertainty polytope
Josette Ayoub (email@example.com), Michael Poss (firstname.lastname@example.org)
We present in this talk a general decomposition framework to solve exactly adjustable robust linear optimization problems subject to polytope uncertainty. Our approach is based on replacing the polytope by the set of its extreme points and generating the extreme points on the fly within row generation or column-and-row generation algorithms. We discuss mixed-integer linear programming reformulations for the subproblem. Numerical experiments realized on a robust telecommunications network design under demand uncertainty show the efficiency of the row-and-column generation algorithm.
Keywords: Adjustable robust optimization, Benders decomposition, Mixed-integer linear programming
Generalized maximum covering location model in projects' progress control
Narjes Sabeghi (email@example.com), Hamed R Tareghian (firstname.lastname@example.org ), Erik Demeulemeester (Erik.Demeulemeester@kuleuven.be)
The project control problem (PCP) consists of monitoring projects progress at some so-called control points, finding possible deviations from the baseline schedule and if necessary, making some adjustments to the deviated schedule subject to the available control budgets, the adjusting policies and also other technical and environmental possibilities in order to bring the schedule back on the right track. In this study we for the first time adapt the maximum covering location model to determine the adjusting policies such that the maximum control coverage is achieved, i.e. within the given constraints achieving a schedule that is globally as close to the baseline schedule as possible.
Keywords: Maximum covering location model, project management, project control