Abstract:The Federal Aviation Administration (FAA) and the airline community within the United States have adopted a new paradigm for air traffic flow management, called Collaborative Decision Making (CDM). A principal goal of CDM is shared decision-making responsibility between the FAA and airlines, so as to increase airline control over decisions that involve economic tradeoffs. So far, CDM has primarily led to enhancements in the implementation of Ground Delay Programs, by changing procedures for allocating slots to airlines and exchanging slots between airlines. In this paper, we discuss how these procedures may be formalized through appropriately defined optimization models. In addition, we describe how inter-airline slot exchanges may be viewed as a bartering process, in which each "round" of bartering requires the solution of an optimization problem. We compare the resulting optimization problem with the current procedure for exchanging slots and discuss possibilities for increased decision-making capabilities by the airlines.

The linear programming approach to approximate dynamic programming has received considerable attention in the recent network revenue management literature. A major challenge of the approach lies in solving the resulting approximate linear programs (ALPs), which often have a huge number of constraints and/or variables. We show that the ALPs can be dramatically reduced in size for both affine and separable piecewise linear approximations to network revenue management problems, under both independent and discrete choice models of demand. Our key result is the equivalence between each ALP and a corresponding reduced program, which is more compact in size and admits an intuitive probabilistic interpretation. For the affine approximation to network revenue management under an independent demand model, we recover an equivalence result known in the literature, but provide an alternative proof. Our other equivalence results are new. We test the numerical performance of solving the reduced programs directly using off-the-shelf commercial solvers on a set of test instances taken from the literature.

Despite the historical difference in focus between AI planning techniques and Integer Programming (IP) techniques, recent research has shown that IP techniques show significant promise in their ability to solve AI planning problems. This paper provides approaches to encode AI planning problems as IP problems, describes some of the more significant issues that arise in using IP for AI planning, and discusses promising directions for future research.

The Federal Aviation Administration (FAA) and the major airlines in the United States have embraced a new initiative to improve air traffic flow management. This initiative, called collaborative decision making (CDM), is based on the recognition that improved data exchange and communication between the FAA and the airlines will lead to better decision making. In particular, the CDM philosophy emphasizes that decisions with a potential economic impact on airlines should be decentralized and made in collaboration with the airlines whenever possible. The CDM paradigm has led to fundamental changes in the implementation of ground delay programs. A key component has been the introduction of the compression procedure, which allows for the exchange of arrival slots between airlines. In this paper, we consider opportunities for increased airline control by interpreting the compression procedure as a mediated slot trading mechanism. Based on this interpretation, we propose an extension that allows airlines to submit so-called at-least, at-most offers. We develop an efficient integer programming model to solve the mediator’s problem, and show that the resulting mechanism can substantially improve the ability of airlines to optimize their internal cost functions.

When demand is uncertain, manufacturers and retailers often have private information on future demand, and such information asymmetry impacts strategic interaction in distribution channels. In this paper, we investigate a channel consisting of a manufacturer and a downstream retailer facing a product market characterized by short product life, uncertain demand, and price rigidity. Assuming the firms have asymmetric information about the demand volatility, we examine the potential benefits of sharing information and contracts that facilitate such cooperation. We conclude that under a wholesale price regime, information sharing might not improve channel profits when the retailer underestimates the demand volatility but the manufacturer does not. Although information sharing is always beneficial under a two-part tariff regime, it is in general not sufficient to achieve sharing, and additional contractual arrangements are necessary. The contract types we consider to facilitate sharing are profit sharing and buyback contracts.