A deep cut ellipsoid algorithm for convex programming

This paper proposes a deep cut version of the ellipsoid algorithm for solving a general class of continuous convex programming problems. In each step the algorithm does not require more computational effort to construct these deep cuts than its corresponding central cut version. Rules that prevent some of the numerical instabilities and theoretical drawbacks usually associated with the algorithm are also provided. Moreover, for a large class of convex programs a simple proof of its rate of convergence is given and the relation with previously known results is discussed. Finally some computational results of the deep and central cut version of the algorithm applied to a min—max stochastic queue location problem are reported.location theory;convex programming;deep cut ellipsoid algorithm;min—max programming;rate of convergence

Fractional location problems

In this paper we analyze some variants of the classical uncapacitated facility location problem with a ratio as an objective function. Using basic concepts and results of fractional programming, we identify a class of one-level fractional location problems which can be solved in polynomial time in terms of the size of the problem. We also consider the fractional two-echelon location problem, which is a special case of the general two-level fractional location problem. For this two-level fractional location problem we identify cases for which its solution involves decomposing the problem into several one-level fractional location problems.discrete location;fractional program

General models in min-max planar location

This paper studies the problem of deciding whether the present iteration point of some algorithm applied to a planar singlefacility min-max location problem, with distances measured by either anl p -norm or a polyhedral gauge, is optimal or not. It turns out that this problem is equivalent to the decision problem of whether 0 belongs to the convex hull of either a finite number of points in the plane or a finite number of differentl q -circles . Although both membership problems are theoretically solvable in polynomial time, the last problem is more difficult to solve in practice than the first one. Moreover, the second problem is solvable only in the weak sense, i.e., up to a predetermined accuracy. Unfortunately, these polynomial-time algorithms are not practical. Although this is a negative result, it is possible to construct an efficient and extremely simple linear-time algorithm to solve the first problem. Moreover, this paper describes an implementable procedure to reduce the second decision problem to the first with any desired precision. Finally, in the last section, some computational results for these algorithms are reported.optimality conditions;continuous location theory;computational geometry;convex hull;Newton-Raphson method

An Integrated Approach to Single-Leg Airline Revenue Management: The Role of Robust Optimization

In this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments it turns out that for these robust versions the variability compared to their classical counter parts is considerably reduced with a negligible decrease of average revenue.Robust Optimization;Dynamic Models;Single-Leg Problems;Static Models;Airline Revenue Management

An integrated approach to single-leg airline revenue management: The role of robust optimization

In this paper we introduce robust versions of the classical staticand dynamic single leg seat allocation models as analyzed byWollmer, and Lautenbacher and Stidham, respectively. These robustmodels take into account the inaccurate estimates of the underlyingprobability distributions. As observed by simulation experiments itturns out that for these robust versions the variability compared totheir classical counter parts is considerably reduced with anegligible decrease of average revenue.dynamic models;robust optimization;static models;airline revenue management;single-leg problems

Fractional location problems

In this paper we analyze some variants of the classical uncapacitated facility location problem with a ratio as an objective function. Using basic concepts and results of fractional programming, we identify a class of one-level fractional location problems which can be solved in polynomial time in terms of the size of the problem. We also consider the fractional two-echelon location problem, which is a special case of the general two-level fractional location problem. For this two-level fractional location problem we identify cases for which its solution involves decomposing the problem into several one-level fractional location problems

An Integrated Approach to Single-Leg Airline Revenue Management: The Role of Robust Optimization

In this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments it turns out that for these robust versions the variability compared to their classical counter parts is considerably reduced with a negligible decrease of average revenue

General models in min-max continous location

In this paper, a class of min-max continuous location problems is discussed. After giving a complete characterization of th stationary points, we propose a simple central and deep-cut ellipsoid algorithm to solve these problems for the quasiconvex case. Moreover, an elementary convergence proof of this algorithm and some computational results are presented.location theory;min-max programming;ellipsoid algorithm;quasiconvexity

Duality theory for convex/quasiconvex functions and its application to optimization

In this paper an intuitive and geometric approach is presented explaining the basic ideas of convex/quasiconvex analysis and its relation to duality theory. As such, this paper does not contain new results but serves as a hopefully easy introduction to the most important results in duality theory for convex/quasiconvex functions on locally convex real topological vector spaces. Moreover, its connection to optimization is also discussed

The role of robust optimization in single-leg airline revenue management

In this paper we introduce robust versions of the classical static and dynamic single leg seat allocation models as analyzed by Wollmer, and Lautenbacher and Stidham, respectively. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments it turns out that for these robust versions the variability compared to their classical counterparts is considerably reduced with a negligible decrease in average revenu