718 research outputs found

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

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    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

    Uncertain Programming for Network Revenue Management

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    The mathematical model for airline network seat inventory control problem is usually investigated to maximize the total revenue under some constraints such as capacities and demands. This paper presents a chance-constrained programming model based on the uncertainty theory for network revenue management, in which the fares and the demands are both uncertain variables rather than random variables. The uncertain programming model can be transformed into a deterministic form by taking expected value on objective function and confidence level on the constraint functions. Based on the strategy of nested booking limits, a solution method of booking control is developed to solve the problem. Finally, this paper gives a numerical example to show that the method is practical and efficient

    MILP Model For Network Revenue Management In Airlines

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    Seat inventory control is an important problem in revenue management which is to decide whether to accept or reject a booking request during the booking horizon in airlines. The problem can be modeled as dynamic stochastic programs, which are computationally intractable in network settings. Various researches have been tried to solve it effectively. Even though dynamic (and stochastic) programming (DP) models can be solved it optimally, they are computationally intractable even for small sized networks. Therefore, in practice, DP models are approximated by various mathematical programming models. In this paper, we propose an approximation model for solving airline seat inventory control problem in network environments. Using Linear Approximation technique, we will transform our problem into a concave piecewise LP model. Based on the optimal solution of ours, we suggest how to implement it for airline inventory control policies such as booking limits, bid-price controls and virtual nesting controls

    An Optimization Model for Cargo Container Loading Plan Problem for Seafood Products

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    — Recently Indonesian Government is focusing in marine fishery industry to be one of the key component for developing the economic of the country. Seafood products belong to this industry. In order to distribute rapidly the seafood products around Indonesia the fish industries need air express carrier service. These carrier flights are one-stop, from an origin, to the hub, then to destination. Airport operations include those at the origins, at the destinations and at the hub. In this paper we consider logistics supply chain decision problem which arises in marine fisheries industry in Indonesia. The loading planning problem is to deliver seafood products using air carriers. The objective of the model is to minimize the total container operation costs, subject to the related operating constraints. Due to the perishability of the products, if overflow occurred they should be transported by third party. The model is formulated as a mixed integer programs. We develop a neighborhood search approach for solving the model

    Dissertation - Preemptive Rerouting of Airline Passengers Under Uncertain Delays

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    An airline\u27s operational disruptions can lead to flight delays that in turn impact passengers, not only through the delay itself but also through possible missed connections. Much research has been done on crew recovery (rescheduling crews after a flight delay or cancellation), but little research has been done on passenger reaccommodation. Our goal is to design ways that passenger reaccommodation can be improved so that passengers can spend less time delayed and miss fewer connections. Since the length of a delay is often not known in advance, we consider preemptive rerouting of airline passengers before the length of the delay is known. Our goal is to reaccommodate passengers proactively as soon as it is known that a flight will be delayed instead of waiting until passengers have missed connections and to use known probabilities for the length of delay. In addition, we consider all of the affected passengers together so that we can effectively handle passengers\u27 competition for available seats. We can give certain seats to people with short connections or those connecting to international flights. When there is one delayed flight, we model the problem as a two-stage stochastic programming problem, with first-stage decisions that assign passengers initial itineraries and second-stage decisions that re-assign any passengers who are subsequently disrupted by the delay. We present a Benders decomposition approach to solving this problem. Computational results for this model are given, showing its effectiveness for reducing the length of passenger delays. When there is more than one delayed flight, we define a portfolio model which assigns passengers to portfolios that define their itineraries under all possible disruption outcomes. We focus on computational methods for solving this model

    Optimization Applications in the Airline Industry

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