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An airport serves as an interface between ground and air transportation; the efficient processing of ground transportation arrivals and departures is, therefore, an important part of airport operations. At many airports, the current pickup and dropoff locations for taxis and other passenger cars are along the terminal curb or in existing parking facilities, and many of these pickup and dropoff facilities suffer from excessive congestion. In addition, in recent years there has been growth in the use of on-demand ground transportation to and from airports, with the increased adoption of ride-hailing services, resulting in increased congestion. Since most airports are severely space constrained, there is a need to consider pickup and dropoff facilities that are more efficient (in vehicle throughput per unit space) than terminal curbs. In this paper we compare the throughput capacities of different pickup and dropoff facility layouts. We take into account the effect of the facility layout and operational rules on conflicts between the movements of different vehicles, the resulting delays in the movements of vehicles, as well as the spatial requirements of different layouts. We demonstrate the impact of mean service times, variability in service times and vehicle movement times, and operational rules on the relative throughput capacities of different facility layouts.

Customer cancellations and no‐shows have an important impact on the performance of an airline's revenue management system. In this study, we study airline network revenue management problems with customer cancellations and no‐shows, considering two types of demand models: independent‐demand and choice‐based‐demand models. In independent‐demand models, the booking request rates for different products are independent of the assortment of products offered by the airline (exogenous), whereas in choice‐based‐demand models they depend on the airline's assortment (endogenous). The stochastic problems of optimizing the booking policies for the two demand models are not tractable because of the high‐dimensional state space. Therefore, for each model, we use the corresponding deterministic, continuous‐time, and continuous‐state model, known as the fluid model. A booking policy based on the fluid solution can be implemented for the original stochastic, discrete model. This fluid policy is shown to be asymptotically optimal when the arrival rates become high and the seat capacities become large. In the independent‐demand setting, an optimal fluid solution can be computed by solving a convex optimization problem for which efficient algorithms exist, whereas for some choice‐based‐demand models, the fluid control problems are known to be intractable. We also develop mixed fluid policies for the independent‐demand setting, taking advantage of the real‐time booking state, to improve the performance over the fluid policy. In a factorial experiment of 20 small‐sized single‐leg problems, the revenue loss under one of the mixed fluid policies is demonstrated to be less than 1% of the optimal value for all problems.

We consider a truckload transportation marketplace in which a market maker sells transportation services to shippers and buys transportation services from carriers. Shippers enter into contracts with the market maker that specify the prices that the shippers have to pay the market maker for transportation. These contracts cover multiple loads over a relatively long specified period of time. When a shipper wants a load to be transported, the shipper enters the load data, including the origin, destination, load type, and requested pickup time, into the marketplace. The market maker chooses the prices offered to carriers for transporting the loads. The market maker can adjust these prices over time based on the dynamics of supply and demand. Carriers check the offers in the marketplace and choose among the available loads. We formulate the market maker's load pricing problem as a Markov decision process (MDP). The MDP captures randomness in the load and carrier arrival processes, as well as carriers' random choice behavior. As it is computationally inefficient to solve this MDP as the market size increases, we study a discrete-time fluid approximation of the problem. This approximation results in a simple pricing policy whose price for each load depends on the time remaining until the load has to be picked up, but does not depend on the other loads in the marketplace. We show that this policy is asymptotically optimal with a loss ratio of order $O(1/\theta)$, where $\theta$ represents the scale of shipper demand and carrier supply. We also present a continuous-time fluid model and discuss the managerial insights provided by its solution.

Various markets exhibit growth and collapse in prices that are sometimes called bubbles, and cycles if the phenomenon repeats. Rational expectations models have been proposed to explain these phenomena; however, such models suffer from a number of criticisms, including lack of experimental support. Moreover, they assume unbiased forecasts without explaining how the investors form their forecasts. In this paper, we provide a model of asset price forecasts and market prices that can be calibrated with experimental data and that can capture various behavioural characteristics of investors such as confidence and panic. We study the resulting dynamical system and show that asset price bubbles may result from seemingly reasonable forecasts and decisions of investors. The model attempts to provide insight into the qualitative connection between investors’ forecasting behaviour and the formation of bubbles and cycles. We present results on the convergence of the process to a fixed point, or to an attractor that d...

Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a choice model called the spiked multinomial logit (spiked-MNL) model was proposed. We study a network revenue management problem under the spiked-MNL choice model. We show that efficient sets, i.e., assortments that offer a Pareto-optimal trade-off between revenue and resource usage, are nested-by-revenue when the spike effect is nonnegative. We use this result to show how a deterministic approximation of the stochastic dynamic program can be solved efficiently by solving a small linear program. The solution of the small linear program is used to construct a booking limit policy, and we prove that the policy is asymptotically optimal. This is the first such result for a booking limit policy under a choice model, and our proof uses an approach that is different from those used for previous asymptotic optimality results. Finally, we evaluate different revenue management policies in numerical experiments using both synthetic and airline data.

Multinomial selection is concerned with selecting the most probable (best) multinomial alternative. The alternatives compete in a number of independent trials. In each trial, each alternative wins with an unknown probability specific to that alternative. A long-standing research goal has been to find a procedure that minimizes the expected number of trials subject to a lower bound on the probability of correct selection ( (CS)). Numerous procedures have been proposed over the past 55 years, all of them suboptimal, for the version where the number of trials is bounded. We achieve the goal in the following sense: For a given multinomial probability vector, lower bound on (CS), and upper bound on trials, we use linear programming (LP) to construct a procedure that is guaranteed to minimize the expected number of trials. This optimal procedure may necessarily be randomized. We also present a mixed-integer linear program (MIP) that produces an optimal deterministic procedure. In our computational stud...

Investors’ forecasting behavior affects their trading decisions and the resulting asset prices. It has been shown in the literature how different, apparently reasonable investor forecasting behaviors can lead to qualitatively different asset price trajectories. For example, investors’ confidence or panic in relying on observed asset prices to forecast future prices can lead to price bubbles, price crashes, and unpredictable price cycles. In this paper, we report the results of behavioral experiments to study investors’ forecast formation. We present the design of an experiment consisting of multiple investors that participate in a market for a virtual asset. We conducted three experimental sessions with different participants in each session. Unlike most other market experiments with known finite durations and predictable decreasing prices toward the end of the experiment, our experimental sessions had random durations chosen to emulate an infinite horizon market with discounting. Asset price bubbles and cycles occurred in all three sessions. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past prices, even when they know the fundamental value exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. There is some evidence for adjustment of forecasts towards the fundamental value as the extrapolated forecasts deviate from the fundamental value. The calibrated forecasting models of all participants were consistent with dynamical systems that exhibit price bubbles and cycles.

With an OR background of varied practice. I began my editorial career in operations research over three decades ago. Even then it was clear that readers of our journals wanted more of what were already being called applications papers. Note that this term appears to have conceded that the subject's center of attention was developing theory rather than exploring the phenomena that OR was undertaking to understand and explain. A balanced view of the subject should regard the two activities as inseparable partners: theory without an observational base is only an intellectual game; practice without adequate intellectual conceptions (that is, models) is mere management gamesmanship.

From time to time there arise in professional circles discussions of how scientific and technical journals are managed and especially how the editors choose their contents. Members of INFORMS had such a discussion recently, and the editor-in-chief of this journal offered a thoughtful response (Michael H. Rothkopf, Editorial refereeing—How blind should we be? Interfaces, Vol. 24, No. 6, pp. 14–18, 1994.). Unfortunately for a scientific community dedicated to seeking models of operating systems involving human behavior, OR/MS has offered no discriminating and objective studies of the editorial process to enlighten those with concerns about it.

Discussion of the “practical credentials” of the OR/MS work and what it is that constitutes quality and effectiveness in OR/MS practice as seen by our profession, and especially by its large and growing academic community. The matter of scientific quality is left aside, as the supporting material for the cases offers ample evidence of their excellence.

Firms choose strategies based on their attributes and industry conditions; therefore, strategy choice is endogenous and self-selected. Empirical models that do not account for this and regress performance measures on strategy choice variables are potentially misspecified and their conclusions incorrect. I highlight how self-selection on hard-to-measure or unobservable characteristics can bias strategy performance estimates and recommend an econometric technique that has been developed to account for this effect. Although this concern applies to a wide range of strategy questions, to demonstrate its effect I empirically examine if entry mode choice (acquisition versus greenfield) influences foreign direct investment survival. In specifications that do not account for self-selection, I find that greenfield entries have survival advantages compared to acquisitions. This confirms previous findings. However, the significance of this effect disappears once I account for self-selection of entry mode in the empirical estimates. The results confirm that estimates from models that do not account for self-selection of strategy choice can lead to incorrect or misleading conclusions.

on competition, we study product assortment competition in a duopoly in which each seller may choose whether to include a decoy in the seller’s product assortment. We provide a complete characterization of the Nash equilibria and their dependence on choice model parameters. We study the evolution of assortment competition and we evaluate the stability of the equilibria in the context of sellers learning about the behavior of their competitors. Our results indicate under what conditions it is benecial for a seller to include a decoy into the seller’s assortment, and under what conditions the seller obtains a free ride from the competitor’s decoy. Our results also show that every pure-strategy Nash equilibrium is stable and every mixed-strategy Nash equilibrium is unstable.

In this paper a dynamic and stochastic model of the well-known knapsack problem is developed and analyzed. The problem is motivated by a wide variety of real-world applications. Objects of random weight and reward arrive according to a stochastic process in time. The weights and rewards associated with the objects are distributed according to a known probability distribution. Each object can either be accepted to be loaded into the knapsack, of known weight capacity, or be rejected. The objective is to determine the optimal policy for loading the knapsack within a fixed time horizon so as to maximize the expected accumulated reward. The optimal decision rules are derived and are shown to exhibit surprising behavior in some cases. It is also shown that if the distribution of the weights is concave, then the decision rules behave according to intuition.

Many carriers, such as airlines and ocean carriers, collaborate through the formation of alliances. The rules of alliances are important for both the stability of the alliance and the profitability of the alliance members. In this paper, we address the design of resource exchange alliances, a type of alliance agreements widely used in practice, especially in the ocean cargo industry. Resource exchange alliances often increase competition among alliance members since the members can sell substitutable products after the exchange. We propose a resource exchange model that takes into account the resulting competition and alliance profit allocation among alliance members. The problem of determining the optimal amounts of resources to exchange is formulated as a stochastic mathematical program with equilibrium constraints (MPECs). Although MPEC problems are, in general, very difficult to solve, we successfully demonstrate the efficient solution approach and provide valuable insights for the resource exchange design problem. In particular, we show that a resource exchange alliance we propose can induce greater profit for the alliance members (both total and individual profits) even though it leads to greater competition.

The role of logistics management is changing. Many companies are realizing that value for a customer can, in part, be created through logistics management (Langley and Holcomb, 1996). Customer value can be created through product availability, timeliness and consistency of delivery, ease of placing orders, and other elements of logistics service. Consequently, logistics service is becoming recognized as an essential element of customer satisfaction in a growing number of product markets today.

Networks have become increasingly pervasive in our lives: social networks not only have a defining impact on consumer choice but are also central in social and political decisions, ranging from political discourse in the blogosphere to the organization and coordination of protests in the Arab Spring. Economic and financial transactions also increasingly occur over large, complex, dynamically changing networks, not only contributing to the efficient allocation of resources, but also occasionally creating large, systemic, and poorly understood risks. Similarly, network effects are now seen as major elements in healthcare, smart power grids, urban transportation, and more. We invite high-quality submissions in social and economic networks, construed broadly to include any system in which human action and control play a major role. We welcome contributions based on rigorous mathematical models, as well as empirical and simulation studies, that contribute to the understanding of network formation, cascades, reliability, information and action propagation, decision making, inference, measurement, and efficient optimization algorithms over networks. We seek papers that apply tools from operations research, statistics, computer science, applied mathematics, and physics to offer novel insights, build new models, and possibly provide recommendations to policy makers. To submit your paper, go to the Operations Research ScholarOne Manuscripts site at http://mc.manuscriptcentral.com/opre.manuscriptcentral.com/opre and follow the instructions provided. July/August 2015: Expected issue publication date.

Summarization: Recently, auction methods have been investigated as effective, decentralized methods for multi-robot coordination. Experimental research has shown great potential, but has not been complemented yet by theoretical analysis. In this paper we contribute a theoretical analysis of the performance of auction methods for multi-robot routing. We suggest a generic framework for auction-based multi-robot routing and analyze a variety of bidding rules for different team objectives. This is the first time that auction methods are shown to offer theoretical guarantees for such a variety of bidding rules and team objectives. Παρουσιάστηκε στο: Proceedings of Robotics: Science and Systems

Summarization: We consider the problem of allocating a number of exploration tasks to a team of mobile robots. Each task consists of a target location that needs to be visited by a robot. The objective of the allocation is to minimize the total cost, that is, the sum of the travel costs of all robots for visiting all targets. We show that finding an optimal allocation is an NP-hard problem, even in known environments. The main contribution of this paper is PRIM ALLOCATION, a simple and fast approximate algorithm for allocating targets to robots which provably computes allocations whose total cost is at most twice as large as the optimal total cost. We then cast PRIM ALLOCATION in terms of a multi-round single-item auction where robots bid on targets, which allow for a decentralized implementation. To the best of our knowledge, PRIM ALLOCATION is the first auction-based allocation algorithm that provides a guarantee on the quality of its allocations. Our experimental results in a multi-robot simulator demonstrate that PRIM ALLOCATION is fast and results in close-to-optimal allocations despite its simplicity and decentralized nature. In particular, it needs an order of magnitude fewer bids than a computationally intensive allocation algorithm based on combinatorial auctions, yet its allocations are at least as good. Παρουσιάστηκε στο: Intelligent Robots and Systems, . Proceedings IEEE/RSJ International Conference on