Sabre Airline Solutions is a subsidiary of Sabre Holdings. The main product of Sabre Airline Solutions is the SabreSonic system. This provides departure control, reservations, and, inventory management. Other products include resource management, fares and revenue management, data services, flight planning & management, crew planning & management and frequent flyer systems. Sabre Airline Solutions newly acquired Swedish owned company RM Rocade, merging in a cost-efficient product suite for small and mid-size carriers to whom price is a heavy deciding factor. The new acquisition makes Sabre Airline Solutions one of the largest-covering players in the airline software market. Wikipedia.
Zhu X.,University of Tennessee at Knoxville |
Yuan Q.,University of Tennessee at Knoxville |
Garcia-Diaz A.,University of Tennessee at Knoxville |
Dong L.,Sabre Airline Solutions
Computers and Operations Research | Year: 2011
The minimal-cost network flow problem with fixed lower and upper bounds on arc flows has been well studied. This paper investigates an important extension, in which some or all arcs have variable lower bounds. In particular, an arc with a variable lower bound is allowed to be either closed (i.e., then having zero flow) or open (i.e., then having flow between the given positive lower bound and an upper bound). This distinctive feature makes the new problem NP-hard, although its formulation becomes more broadly applicable, since there are many cases where a flow distribution channel may be closed if the flow on the arc is not enough to justify its operation. This paper formulates the new model, referred to as MCNF-VLB, as a mixed integer linear programming, and shows its NP-hard complexity. Furthermore, a numerical example is used to illustrate the formulation and its applicability. This paper also shows a comprehensive computational testing on using CPLEX to solve the MCNF-VLB instances of up to medium-to-large size. © 2010 Elsevier Ltd.
Kang Y.,Sabre Airline Solutions |
Batta R.,State University of New York at Buffalo |
Kwon C.,State University of New York at Buffalo
Computers and Operations Research | Year: 2014
Recently, the Value-at-Risk (VaR) framework was introduced for the routing problem of a single hazmat trip. In this paper, we extend the VaR framework in two important ways. First, we show how to apply the VaR concept to a more realistic multi-trip multi-hazmat type framework, which determines routes that minimize the global VaR value while satisfying equity constraints. Second, we show how to embed the algorithm for the single hazmat trip problem into a Lagrangian relaxation framework to obtain an efficient solution method for this general case. We test our computational experience based on a real-life hazmat routing scenario in the Albany district of New York State. Our results indicate that one can achieve a high degree of risk dispersion while controlling the VaR value within the desired confidence level. © 2013 Elsevier Ltd. All rights reserved.
Wang T.,Sabre Airline Solutions |
Cassandras C.G.,Boston University |
Pourazarm S.,Boston University
IFAC Proceedings Volumes (IFAC-PapersOnline) | Year: 2014
We study the problem of routing vehicles with energy constraints through a network where there are at least some charging nodes. We seek to minimize the total elapsed time for vehicles to reach their destinations by determining routes and recharging amounts when the vehicles do not have adequate energy for the entire journey. For a single vehicle, we formulate a mixed-integer nonlinear programming (MINLP) problem and derive properties of the optimal solution allowing it to be decomposed into two simpler problems. For a multi-vehicle problem, including traffic congestion effects, we use a similar approach by grouping vehicles into "subflows." We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy. © IFAC.
Manwani N.,General Electric |
Desai K.,Bidgely Technologies Pvt Ltd |
Sasidharan S.,General Electric |
Sundararajan R.,Sabre Airline Solutions
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015
The performance of a reject option classifiers is quantified using 0 − d − 1 loss where d ∈ (0,. 5) is the loss for rejection. In this paper, we propose double ramp loss function which gives a continuous upper bound for (0 − d − 1) loss. Our approach is based on minimizing regularized risk under the double ramp loss using difference of convex programming. We show the effectiveness of our approach through experiments on synthetic and benchmark datasets. Our approach performs better than the state of the art reject option classification approaches. © Springer International Publishing Switzerland 2015.
Pourazarm S.,Boston University |
Cassandras C.G.,Boston University |
Wang T.,Sabre Airline Solutions
International Journal of Robust and Nonlinear Control | Year: 2015
We study the problem of routing vehicles with energy constraints through a network where there are at least some charging nodes. We seek to minimize the total elapsed time for vehicles to reach their destinations by determining routes, as well as recharging amounts when the vehicles do not have adequate energy for the entire journey. For a single vehicle, we formulate a mixed-integer nonlinear programming problem and derive properties of the optimal solution allowing it to be decomposed into two simpler problems. For a multi-vehicle problem, where traffic congestion effects are included, we seek to optimize a system-wide objective and formulate the problem by grouping vehicles into 'subflows'. We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy. Because the problem size increases with the number of subflows, a proper selection of this number is essential to render the problem computationally manageable and reflects a trade-off between proximity to optimality and computational effort needed to solve the problem. We propose a criterion and procedure leading to an appropriate choice of the number of subflows. We also quantify the 'price of anarchy' for this problem and compare user-optimal to system-optimal performance. Finally, when the system consists of both electric vehicles (EVs) and non-electric vehicles, we formulate a system-centric optimization problem for optimal routing of both non-electric vehicles and EVs along with an optimal policy for charging EVs along the way if needed. Numerical results are included to illustrate these approaches. © 2015 John Wiley & Sons, Ltd.