Civil Aeronautics Board Research Articles

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Remaining Human: How the Airline Deregulation Act Shields Commercial Air Carriers From Legal Liability for Mishandling Human Remains

The Airline Deregulation Act of 1978 (ADA) deregulated the domestic airline industry. Specifically, the ADA ended the dual administrative system, which allowed the states to regulate intrastate airfare and permitted the federal government’s Civil Aeronautics Board (CAB) to regulate interstate airfare. The Act also included a broad preemption clause to prevent the states from reimposing economic regulations on air travel. The preemption clause prohibits a “State . . . [from] enact[ing] or enforc[ing] any law, rule, regulation, standard, or other provision having the force and effect of law relating to rates, routes, or services of any air carrier.” The Supreme Court has interpreted the scope of the ADA’s preemption clause expansively and concluded that only state law claims with a “tenuous, remote, or peripheral” relationship to an airline’s services survive preemption. Yet, the Supreme Court has never defined the key word: Service. Today, federal circuit courts disagree on the definition of “service,” and a majority have adopted the Fifth Circuit’s definition articulated in Hodges v. Delta Airlines, Inc. Applying the Supreme Court’s expansive view of the ADA’s preemption clause, the Fifth Circuit defined the term “service” as all bargained-for air carrier services, including “ticketing, boarding procedures, provision of food and drink, and baggage handling, in addition to the transportation itself.” The Fifth Circuit’s definition preempts almost all conceivable state law claims despite the Supreme Court excepting claims with a “tenuous, remote, or peripheral” relationship to an airline’s services from preemption. Consequently, the aviation industry largely evades state law. This Comment argues that courts should interpret the ADA’s preemption clause more narrowly to allow more state law claims to survive preemption, particularly claims arising from an airline’s mishandling, delaying, or misplacing of a loved one’s remains. As the COVID-19 pandemic claims lives and frustrates air travel, a perfect storm threatens to mishandle, delay, or misplace a loved one’s remains. Grieving mothers, fathers, husbands, and wives are left without a legal remedy because the ADA shields the airline industry from liability concerning “baggage handling” and the “transportation itself.” Courts should interpret the ADA’s preemption clause more narrowly based on the ADA’s clear purpose, the plain meaning of the ADA’s text, stare decisis, and the law’s treatment of other entities that mishandle human remains.

An artificial immune system algorithm for solving the uncapacitated single allocation p-Hub median problem

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

A Multi-Objective Green Hub Location Problem with Multi Item-Multi Temperature Joint Distribution for Perishable Products in Cold Supply Chain

This paper investigates a bi-objective green hub location problem, in which multiple perishable products with various storage temperatures can be distributed simultaneously in a cold supply chain (CSC). The objectives of this problem include minimizing the system's total cost (including transportation, hub establishment, adjustment of the storage compartments' temperatures, and carbon emission costs) and maximizing the quality of the delivered product to the customer via the proposed model. Mixed-integer linear programming (MILP) in the GAMS software was employed to formulate this problem. Then, the ε-Constraint method was adopted to solve the presented bi-objective model to obtain the Pareto frontier and consequently, a numerical example based on the CAB (Civil Aeronautics Board) database is presented to validate the applicability of the model. The solutions of the model provide information regarding the hub location (HL), allocating customers to the hubs, allocating customers to the vehicles, and the sequence of vehicles' services for the Multi Item-Multi Temperature Joint Distribution of perishable products in CSCs. Moreover, the final results revealed the existence of a contradictory exchange between the two objectives of this paper, implying that the higher is the quality of the delivered perishable product to the customer, the greater is the system's total cost. The novelty of the proposed model compared to other hub location problems (HLPs) lies in the integration of the tactical/operational decisions with strategic decisions to provide logistic solutions in CSCs by considering the carbon emissions as an environmental factor in the transportation systems for the simultaneous distribution of dissimilar storage temperatures perishable products within a CSC. The proposed model in this research can help the distributers of perishable products by maintaining the quality of the delivered items and reducing the system's total costs and considering the carbon emissions of transportation systems. This study has practical implications for the logistics and CSCs managers to not only establish a distribution network for multiple perishable products on the basis of the findings, but also respond to the environmental sustainability.

Distributionally Robust Hub Location

We study the adaptive distributionally robust hub location problem with multiple commodities under demand and cost uncertainty in both uncapacitated and capacitated cases. The hub location decision anticipates the worst-case expected cost over an ambiguity set of possible distributions of the uncertain demand and cost, and the routing policy, being adaptive to the uncertainty realization, ships commodities through selected hubs. We investigate the adaptivity and tractability of the distributionally robust model under different distributional information about uncertainty. In the uncapacitated case in which demand and cost are independent and costs of different commodities are also mutually independent, the adaptive distributionally robust model is equivalent to a nonadaptive classical robust model and the second-stage routing decision follows an optimal static policy. We then relax the independence assumption and show that the second-stage routing decision follows an optimal scenario-wise policy if either the demand or the cost is supported on a convex hull of given scenarios. We extend our analysis to the capacitated case and show that the second-stage routing decision still follows an optimal scenario-wise policy if the demand is supported on the convex hull of given scenarios. In terms of tractability, for all mentioned cases, we reformulate the distributionally robust model as a moderate-sized mixed-integer linear program, and we recover the associated worst-case distribution by solving a collection of linear programs. Through numerical studies using the Civil Aeronautics Board data set, we demonstrate the advantages of the distributionally robust model by examining its superior out-of-sample performance against the classical robust model and the stochastic model.

Benders decomposition for multiple-allocation hub-and-spoke network design with economies of scale and node congestion

This paper is the first to consider a novel and more realistic variant of the uncapacitated hub location problem where both flow-dependent economies of scale and congestion considerations are incorporated into the multiple-allocation version of the problem. Given an undirected graph with edge per-unit costs and flow requirements for a given set of source-destination pairs of nodes, the goal is to decide where to locate hub nodes and then to route flow through these hubs in a way that minimizes total cost. Transportation costs are influenced by two conflicting effects: consolidation discounts on hub-to-hub flow due to economies of scale, and congestion penalties due to excessive demand through hubs. The problem is formulated as a mixed-integer linear program by piecewise-linearizing non-linear cost elements. Since the resulting model is found to be difficult to solve directly using commercial solvers, a specialized Benders decomposition approach is devised to solve the problem. The proposed method is tested on two standard testbeds (the Civil Aeronautics Board and Australian Post datasets). While the commercial solver is unable to obtain any feasible solution for networks larger than 25 nodes, the proposed algorithm is shown to be able to obtain optimal solutions for networks as large as 70 nodes, and is able to obtain small-gap feasible solutions for networks with up to 100 nodes. Whenever both algorithms are able to solve instances to optimality, the Benders algorithm is able to do so in a fraction of the time needed by the commercial solver. The incorporation of congestion costs into the multiple-allocation topology as opposed to the single-allocation one is also found to lead to significant cost savings.

Stochastic Hierarchical Multimodal Hub Location Problem for Cargo Delivery Systems: Formulation and Algorithm

This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with pre-specified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution.

Robust Hub Location Problem With Flow-Based Set-Up Cost

Hub location problems are network design problems on the level of strategic decision-making processes. During strategic planning, input data, such as flows and set-up costs, are not always known in advance. Hence, decisions have to be made in an uncertain environment. In this paper, two sources of uncertainty are considered: the flows from origins to destinations and the set-up costs of hubs. A robust optimization formulation is proposed for both single and multiple allocation cases, in which the flow between each pair of nodes is assumed to be uncertain and correlated. In addition, the set-up cost of a hub is related to the total flow through the hub. Nonlinear integer program models are presented for both single and multiple allocation cases, and they are solved using CPLEX. Computational tests using the Civil Aeronautics Board and Australian Post datasets are provided. The numerical results suggest that the robust optimization strategy locates more hubs than in the deterministic case with a relatively small cost increase, and the total cost of the robust solution calculated for the multiple allocation case is marginally lower than that for the single allocation case. The robust optimization strategy is proven to be effective for protecting the solution against the worst case for different uncertain parameters.

The growing nostalgia for past regulatory misadventures and the risk of repeating these mistakes with Big Tech

Abstract Digital markets are increasingly described as the ‘railroads’ of the 21st century. Extending that metaphor, some commentators argue we should revive stale railroad-era economic regulations and adapt them to the digital age. This enthusiasm appears to be buoyed by both a sudden nostalgia for railroad and airline regulations once administered by the Interstate Commerce Commission (ICC) and the Civil Aeronautics Board (CAB) and an equally sudden amnesia of the enormous harm those regulations caused to consumers. ICC and CAB regulations are indeed an apt metaphor, as they illustrate perfectly how sectoral regulations sold to the public as simple, clear, and cheap can go awry. Ultimately, a bipartisan consensus emerged to disband those agencies and deregulate those industries. After deregulation, prices fell, output expanded, and firms innovated. Proposals to regulate Big Tech today in a similar fashion forget these important lessons. We should know better than to do the same thing again today and expect a different result.

Robust solution for a minimax regret hub location problem in a fuzzy-stochastic environment

In the present paper, a robust approach is used to locate hub facilities considering network risks. An additional objective function, minimax regret, is added to the classical objective function in the hub location problem. In the proposed model, risk factors such as availability, security, delay time, environmental guidelines and regional air pollution are considered using triangular fuzzy-stochastic numbers. Then an equivalent crisp single objective model is proposed and solved by the Benders decomposition method. Finally, the results of both Benders decomposition and commercial optimization software are compared for different instances. Numerical instances were developed based on the well-known Civil Aeronautics Board (CAB) data set, considering different levels of uncertainty in parameters. The results show that the proposed model is capable of selecting nodes as sustainable hubs. Also, the results confirm that using Benders decomposition is more efficient than using classical solution methods for large-scale problems.

Solving Single Allocation Hub Location Problems on Euclidean Data

If shipments have to be transported between many sources and sinks, direct connections from each source to each sink are often too expensive. Instead, a small number of nodes are upgraded to hubs that serve as transshipment points. All sources and sinks are connected to these hubs, so that only a few, strongly consolidated transport relations exist. While hubs and detours lead to additional costs, the savings from bundling shipments—i.e., economies of scale—usually outweigh these costs. Typical applications for hub networks are in cargo, air freight, and postal and parcel transport services. In this paper, we consider three classical and two recent formulations of single allocation hub location problems—i.e., hub location problems in which every source and sink is connected to exactly one hub. Solving larger instances of these problems to optimality is difficult because the inherent quadratic structure of the problem has to be linearized: This leads to a sharp rise in the number of variables. Our new approach relies on the fact that many instances—including the Civil Aeronautics Board and Australian Post data sets—have a Euclidean structure: The distances between the possible hub locations are Euclidean. This enables us to construct a new linearization together with a row generation procedure that solves instances of up to 200 nodes to optimality. For problems like the uncapacitated single allocation p-hub median problem and the uncapacitated single allocation hub location problem, we present the first optimal results for instances of this size.

A tabu search heuristic for the uncapacitated single allocation p-hub maximal covering problem

This paper describes a tabu search (TS) heuristic for the uncapacitated single allocation p-hub maximal covering problem. The objective is to determine the best location for p hubs and the assignment of each of the spokes to a single hub such that the total demand between pairs of nodes within a given coverage distance is maximized. We consider all nodes as possible candidates for establishing hub facilities, what increases the complexity of the problem. Based on the mathematical programming formulation proposed by Peker and Kara (2015) we also report, for the first time, the optimal solutions for instances with up to 50 nodes from the AP (Australian Post) benchmark dataset, as well as the complete set of results for the CAB (Civil Aeronautics Board) dataset, including some heretofore yet unpublished results. The computational experiments have also demonstrated that our TS heuristic is efficient, leading to improved solutions in shorter CPU times when compared to previously published results, as well as for new derived instances with tigher coverage. It was also able to solve, for the first time, all instances of the AP data set, with up to 200 nodes, as well as new instances with tighter coverage parameters, thus evidencing it capacity to solve effectively large, realistic-sized instances of the problem.

Hub-and-spoke network design problem under uncertainty considering financial and service issues: A two-phase approach

This paper proposes a bi-objective hub-and-spoke (H&S) network design problem with type-2 (T2) fuzzy transportation cost and travel time described by parametric secondary possibility distributions, which are obtained using three types of mean value (MV) reduction methods. The considered objectives jointly minimize the generalized expectation of the total transportation costs and the maximum travel time requirement in terms of generalized value-at-risk (VaR). To solve the fuzzy bi-objective H&S network design problem, we develop a two-phase approach, where in the first phase we convert the proposed model into its equivalent parametric mixed-integer programming problems by applying an equivalent transformation method. This is followed by the second phase using a fuzzy linear programming approach implemented with an augmented max-min operator to obtain a non-dominated solution that has an equal satisfactory degree on both objectives. Finally, a case study based on the Civil Aeronautics Board (CAB) data set is conducted to demonstrate the effectiveness of the proposed model and solution approach.

Climate Change, “Grid Neutrality,” and Electricity Distributors

My late colleague and friend Alfred Kahn relayed how President Jimmy Carter hired him in 1977 to head the Civil Aeronautics Board to reform and ultimately deregulate the US airline industry. Accepting the job, he told a group of airline executives (to their fury), “I really don't know one plane from the other. To me they are all marginal costs with wings.” He used that term to signal his intention of reforming the industry in keeping with economic principles, uninfluenced by the romance of air travel.

Finding p-Hub Median Locations: An Empirical Study on Problems and Solution Techniques

Hub location problems have been studied by many researchers for almost 30 years, and, accordingly, various solution methods have been proposed. In this paper, we implement and evaluate several widely used methods for solving five standard hub location problems. To assess the scalability and solution qualities of these methods, three well-known datasets are used as case studies: Turkish Postal System, Australia Post, and Civil Aeronautics Board. Classical problems in small networks can be solved efficiently using CPLEX because of their low complexity. Genetic algorithms perform well for solving three types of single allocation problems, since the problem formulations can be neatly encoded with chromosomes of reasonable size. Lagrangian relaxation is the only technique that solves reliable multiple allocation problems in large networks. We believe that our work helps other researchers to get an overview on the best solution techniques for the problems investigated in our study and also stipulates further interest on cross-comparing solution techniques for more expressive problem formulations.

Robust bi-objective optimization of uncapacitated single allocation p-hub median problem using a hybrid heuristic algorithm

The p-hub median problem aims at locating p-hub facilities in a network and allocating non-hub nodes to the hubs such that the overall transportation cost is minimized. One issue of major importance in this problem remarks the requirement to deal with uncertain factors such as weather conditions and traffic volume. These lead to uncertainty in travel time between origin and destination points. In today's competitive markets in which customers look for robust delivery services, it is important to minimize the upper bound of uncertainty in the network routes. In this paper, a robust bi-objective uncapacitated single allocation p-hub median problem (RBUSApHMP) is introduced in which travel time has non-deterministic nature. The problem aims to select location of the hubs and allocation of the other nodes to them so that overall transportation cost and maximum uncertainty in network are minimized. To do this, a desirability function-based approach is suggested that ensures both interested objectives to fall within their specification limits. Due to the complexity of the model, a heuristic based on scatter search and variable neighborhood descent is developed. To evaluate the performance of the proposed method a computational analysis on Civil Aeronautics Board and Australian Post data sets was performed. The obtained results using the proposed hybrid metaheuristic are compared to those of the optimum solutions obtained using GAMS. The results indicate excellent performance of the suggested solution procedure to optimize RBUSApHMP.

Forming cognitions by investing in a form: Frequent Flyer Programs in US air travel post-deregulation (1981–1991)

This paper addresses the link between action and cognition in markets. Increasing reliance on markets to co-ordinate economic activity across diverse contexts underscores the need to better understand how such coordination is achieved and how it can be changed. Our starting point is sense-making in formal organizations and markets. We argue that, whereas focusing on intersubjective agreements supported by frequent communication provides useful insights on coordination in formal organizations, coordination in markets cannot rely on these mechanisms alone. We borrow Thévenot's (1984) notions of form and investments in form to show how cognition and action are interrelated, and coordination achieved, in market settings. A form is characterized by its capacity to generate temporal and spatial equivalencies, allowing us to move from the particular (e.g., one exchange episode) to the generic (e.g., recurrent exchanges with particular types of customers). The development of a form helps construct common contexts of action which become the basis for coordinated actions.Our empirical case describes how the introduction of Frequent Flyer Programs created a new market form following the US Airline Deregulation Act (1978). Under the previous regulatory regime, the US Civil Aeronautics Board controlled routes and prices and airlines were limited to competing on frequency and quality of service. We show how successive rounds of investments by a variety of market actors helped articulate and consolidate the new market form over a 10-year period. Whereas action preceded cognition in the early stages, the gradual consolidation of the new form provided opportunities for market participants to gain reflexive distance and conjure up further ways to engage with the form.

A Book, the Application, and the Outcomes: How Right Was Alfred Kahn in The Economics of Regulation About the Effects of the Deregulation of the US Domestic Airline Market?

This article is concerned with the foresights that Alfred Kahn had when he led the deregulation of the US passenger airline industry, and in particular with the role the ideas in his Economics of Regulation played in his actions. Kahn was a reluctant deregulator of the US interstate airline industry. The 1978 Airline Deregulation Act, which he “fathered,” however, not only ushered in a wave of other similar actions both within and outside of transportation in the United States, but also provided a demonstration effect that resulted in a more general rolling back of microeconomic regulation across the globe. Kahn was head of the US Civil Aeronautics Board for only eighteen months or so, but he brought about considerable changes to the airline industry and, through its spread effects, to all other modes of transportation. Here we focus on the degree to which he actually foresaw, or at least generally predicted, what the outcome of airline deregulation would be. While Kahn himself acknowledged that market outcomes are, almost by definition, unpredictable, he clearly had some ideas of what the results of his would be. Here we look at what he thought would happen, what did happen, and at whether his own short term ex post assessment of outcomes has been of durable accuracy.

Enforcing Federal Supremacy: Relief against Federal-State Regulatory Conflicts

fornia claims as its own notwithstanding some thirty miles of Pacific Ocean separating the two. One may reach Catalina by boat or by plane, the latter operated by United Air Lines, an air carrier in interstate and intrastate commerce. This Catalina air service had been under the unchallenged jurisdiction of the Civil Aeronautics Board for twelve years when, in September, 1951, United received a letter from the California Public Utilities Commission instructing it to file its Catalina tariffs with the Commission. Then followed a series of conferences between United and the Commission in which United claimed, and the Commission denied, that the CAB had exclusive jurisdiction over the Catalina operation. These conferences proved inconclusive and in June, 1952, United brought suit in the federal district court for declaratory relief against the Commission's attempt to interfere with the CAB's jurisdiction, United Air Lines, Inc., et al. v. Public Utilities Commission of California.' United's problem was one that has become increasingly common with the steady expansion of administrative regulation by the states. Federal regulation having been extended to its constitutional limits in many fields, the system of federalism is now being put to the test with unpiecedented frequency as state regulatory power is being pushed to its constitutional limits.2 The problems of federalism in action in such fields as labor relations, taxation and public utilities are before the public eye constantly.3 The United case sheds no light on the distribution of regulatory powers between state and federal governments. It is no less of interest, however, because it focuses on the practical problem of the litigant who is the object of regulation and is caught between the massive powers of governments in

A Note from the General Editor About This Special Issue

The enactment of the Interstate Commerce Act (ICA) in 1887 was a landmark in the annals of the American system of the regulation of business. It was one of the first major pieces of legislation that regulated industry in the US;1 and the ICA’s creation of the Interstate Commerce Commission (ICC) provided the template for subsequent economic regulatory efforts (e.g., vis-a-vis other surface transportation, the airlines, and over-the-air broadcasting) and regulatory organizational structures [e.g., the Civil Aeronautics Board (CAB), the Federal Communications Commission (FCC), and the Federal Energy Regulatory Commission (FERC)] for many decades. Even though the economic deregulation efforts of the 1970s and 1980s (and the continuing efforts in subsequent decades) have undone many of the specific efforts and effects of the ICC, the spirit and philosophy that underlay the ICA and the ICC have continued to wax and wane—and then wax and wane again—over the decades in the US policy arena. The year 2012 marked the 125th anniversary of the ICA and the ICC. This seemed to be a fitting time to have a special issue of the Review of Industrial Organization (RIO) that would take a retrospective look at the ICA and the ICC and the long shadow that they subsequently cast on railroads and surface transportation more generally. Fortunately, Wesley W. Wilson agreed to “shepherd” this special issue; and the results are to be found in the subsequent articles in this issue of the RIO. I am fully confident that the readers of the RIO will find the reading of these articles to be well worth the effort.