A network airline revenue management framework based on decomposition by origins and destinations

We propose a framework for solving airline revenue management problems on large networks, where the main concern is to allocate the flight leg capacities to customer requests under fixed class fares. This framework is based on a mathematical programming model that decomposes the network into origin-destination pairs so that each pair can be treated as a single flight leg problem. We first discuss that the proposed framework is quite generic in the sense that not only several well-known models from the literature fit into this framework but also many single flight leg models can be easily extended to a network setting through the prescribed construction. Then, we analyze the structure of the overall mathematical programming model and establish its relationship with other models frequently used in practice. The application of the proposed framework is illustrated through two examples based on static and dynamic single-leg models, respectively. These illustrative examples are then benchmarked against several existing methods on a set of real-life network problems

A network airline reveneu management framework based on decomposition by origins and destinations

We propose a framework for solving airline revenue management problems on large networks. This framework is based on a mathematical programming model that decomposes the network into origin-destination pairs so that each pair can be treated as a single flight leg problem. We first discuss that the proposed framework is quite generic in the sense that not only several well-known models from the literature fit into this framework but also many single flight leg models can be easily extended to a network setting through the prescribed construction. Then, we formally analyze the structure of the overall mathematical programming model and establish its relationship with other models frequently used in practice. The application of the proposed framework is illustrated through two examples based on static and dynamic single-leg models, respectively. These illustrative examples are then benchmarked against several existing methods on a set of real-life network problems acquired from a major European airline

An corrigendum on the paper: Solving the job-shop scheduling problem optimally by dynamic programming

In [1] an algorithm is proposed for solving the job-shop scheduling problem optimally using a dynamic programming strategy. This is, according to our knowledge, the first exact algorithm for the Job Shop problem which is not based on integer linear programming and branch and bound. Despite the correctness of the dynamic programming algorithm presented in [1], the proof of correctness given there is unfortunately flawed. The contribution of the present note is to point out that flaw, and refer the reader to [2], where the flaw is corrected. Particularly, in [2], we recall the main idea of the proof proposed in [1] and present a counterexample that shows where the problem of that proof lies. Taking into account the nature of the problem, we propose a new approach for proving the correctness of the algorithm. This requires the introduction of new concepts and notation. It is important to remark that the new proof modifies our understanding of the algorithm that, in fact, works in a different way than the one explained in the original article. We also recommend [3], where all the elements for understanding the algorithm, the new proof of its correctness and the estimations of its complexity are fully developed.Fil: van Hoorn, Jelke J.. Vrije Universiteit Amsterdam; Países BajosFil: Nogueira, Agustín. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaFil: Ojea, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas ; ArgentinaFil: Gromicho, Joaquim A. S.. Vrije Universiteit Amsterdam; Países Bajo

A primal-dual decomposition algorithm for multistage stochastic convex programming

This paper presents a new and high performance solution method for multistage stochastic convex programming. Stochastic programming is a quantitative tool developed in the field of optimization to cope with the problem of decision-making under uncertainty. Among others, stochastic programming has found many applications in finance, such as asset-liability and bond-portfolio management. However, many stochastic programming applications still remain computationally intractable because of their overwhelming dimensionality. In this paper we propose a new decomposition algorithm for multistage stochastic programming with a convex objective and stochastic recourse matrices, based on the path-following interior point method combined with the homogeneous self-dual embedding technique. Our preliminary numerical experiments show that this approach is very promising in many ways for solving generic multistage stochastic programming, including its superiority in terms of numerical efficiency, as well as the flexibility in testing and analyzing the model. © Springer-Verlag Berlin 2005

A new Branch and Bound method for a discrete truss topology design problem

Our paper considers a classic problem in the field of Truss Topology Design, the goal of which is to determine the stiffest truss, under a given load, with a bound on the total volume and discrete requirements in the cross-sectional areas of the bars. To solve this problem we propose a new two-stage Branch and Bound algorithm. In the first stage we perform a Branch and Bound algorithm on the nodes of the structure. This is based on the following dichotomy study: either a node is in the final structure or not. In the second stage, a Branch and Bound on the bar areas is conducted. The existence or otherwise of a node in this structure is ensured by adding constraints on the cross-sectional areas of its incident bars. In practice, for reasons of stability, free bars linked at free nodes should be avoided. Therefore, if a node exists in the structure, then there must be at least two incident bars on it, unless it is a supported node. Thus, a new constraint is added, which lower bounds the sum of the cross-sectional areas of bars incident to the node. Otherwise, if a free node does not belong to the final structure, then all the bar area variables corresponding to bars incident to this node may be set to zero. These constraints are added during the first stage and lead to a tight model. We report the computational experiments conducted to test the effectiveness of this two-stage approach, enhanced by the rule to prevent free bars, as compared to a classical Branch and Bound algorithm, where branching is only performed on the bar areas