The Personal Interview

1 Defining Network Flow A flow network is a directed graph G = (V,E) in which each edge (u, v) ∈ E has non-negative capacity c(u, v) ≥ 0. We require that if (u, v) ∈ E, then (v, u) / ∈ E. That is, if an edge exists, then the edge between the same vertices going the reverse direction does not exist. Every flow network has a source s and a sink t, and we assume that for every v ∈ V , there is some path s→ · · · → v → · · · → t. Note that this implies that flow networks are connected. Informally, the intuition behind network flow is to think of the edges as pipes and the weights on the edges as the capacity its corresponding pipe per unit of time. The question we are trying to ask is: how many units of water can we send from the source to the sink per unit of time? Formally, a flow in G is a function f : V × V → R that satisfies the following: • Capacity constraint. For all u, v ∈ V , we require 0 ≤ f(u, v) ≤ c(u, v). Our pipe cannot hold more than is allowed as dictated by its capacity. • Flow conservation. For u ∈ V − {s, t}, we require ∑

Network Flows

We discuss formulations of integer programs with a huge number of variables and their solution by column generation methods, i.e., implicit pricing of nonbasic variables to generate new columns or to prove LP optimality at a node of the branch-and-bound tree. We present classes of models for which this approach decomposes the problem, provides tighter LP relaxations, and eliminates symmetry. We then discuss computational issues and implementation of column generation, branch-and-bound algorithms, including special branching rules and efficient ways to solve the LP relaxation. We also discuss the relationship with Lagrangian duality.

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Branch-And-Price: Column Generation for Solving Huge Integer Programs

This article examines how consumer decision making is influenced by automatically evoked task-induced affect and by cognitions that are generated in a more controlled manner on exposure to alternatives in a choice task. Across two experiments respondents chose between two alternatives: one (chocolate cake) associated with more intense positive affect but less favorable cognitions, compared to a second (fruit salad) associated with less favorable affect but more favorable cognitions. Findings from the two experiments suggest that if processing resources are limited, spontaneously evoked affective reactions rather than cognitions tend to have a greater impact on choice. As a result, the consumer is more likely to choose the alternative that is superior on the affective dimension but inferior on the cognitive dimension (e.g., chocolate cake). In contrast, when the availability of processing resources is high, cognitions related to the consequences of choosing the alternatives tend to have a bigger impact on choice compared to when the availability of these resources is low. As a result, the consumer is more likely to choose the alternative that is inferior on the affective dimension but superior on the cognitive dimension (e.g., fruit salad). The moderating roles of the mode of presentation of the alternatives and of a personality variable related to impulsivity are also reported.

Heart and Mind in Conflict: The Interplay of Affect and Cognition in Consumer Decision Making

In this article, the authors examine how consumer choice between hedonic and utilitarian goods is influenced by the nature of the decision task. Building on research on elaboration, the authors propose that the relative salience of hedonic dimensions is greater when consumers decide which of several items to give up (forfeiture choices) than when they decide which item to acquire (acquisition choices). The resulting hypothesis that a hedonic item is relatively preferred over the same utilitarian item in forfeiture choices than in acquisition choices was supported in two choice experiments. In a subsequent experiment, these findings were extended to hypothetical choices in which the acquisition and forfeiture conditions were created by manipulating initial attribute-level reference states instead of ownership. Finally, consistent with the experimental findings, a field survey showed that, relative to market prices, owners of relatively hedonic cars value their vehicles more than do owners of relat...

Consumer choice between hedonic and utilitarian goods.

Consumers' attempts to control their unwanted consumption impulses influence many everyday purchases with broad implications for marketers' pricing policies. Addressing theoreticians and practitioners alike, this paper uses multiple empirical methods to show that consumers voluntarily and strategically ration their purchase quantities of goods that are likely to be consumed on impulse and that therefore may pose self-control problems. For example, many regular smokers buy their cigarettes by the pack, although they could easily afford to buy 10-pack cartons. These smokers knowingly forgo sizable per-unit savings from quantity discounts, which they could realize if they bought cartons; by rationing their purchase quantities, they also self-impose additional transactions costs on marginal consumption, which makes excessive smoking overly difficult and costly. Such strategic self-imposition of constraints is intuitively appealing yet theoretically problematic. The marketing literature lacks operationalizations and empirical tests of such consumption self-control strategies and of their managerial implications. This paper provides experimental evidence of the operation of consumer self-control and empirically illustrates its direct implications for the pricing of consumer goods. Moreover, the paper develops a conceptual framework for the design of empirical tests of such self-imposed constraints on consumption in consumer goods markets. Within matched pairs of products, we distinguish relative "virtue" and "vice" goods whose preference ordering changes with whet her consumers evaluate immediate or delayed consumption consequences. For example, ignoring long-term health effects, many smokers prefer regular (relative vice) to light (relative virtue) cigarettes, because they prefer the taste of the former. However, ignoring these short-term taste differences, the same smokers prefer light to regular cigarettes when they consider the long-term health effects of smoking. These preference orders can lead to dynamically inconsistent consumption choices by consumers whose tradeoffs between the immediate and delayed consequences of consumption depend on the time lag between purchase and consumption. This creates a potential self-control problem, because these consumers will be tempted to over consume the vices they have in stock at home. Purchase quantity rationing helps them solve the self-control problem by limiting their stock and hence their consumption opportunities. Such rationing implies that, per purchase occasion, vice consumers will be less likely than virtue consumers to buy larger quantities in response to unit price reductions such as quantity discounts. We first test this prediction in two laboratory experiments. We then examine the external validity of the results at the retail level with a field survey of quantity discounts and with a scanner data analysis of chain-wide store-level demand across a variety of different pairs of matched vice (regular) and virtue (reduced fat, calorie, or caffeine, etc.) product categories. The analyses of these experimental, field, and scanner data provide strong convergent evidence of a characteristic crossover in demand schedules for relative vices and virtues for categories as diverse as, among others, potato chips, chocolate chip cookies, cream cheese, beer, soft drinks, ice cream and frozen yogurt, chewing gum, coffee, and beef andturkey bologna. Vice consumers' demand increases less in response to price reductions than virtue consumers' demand, although their preferences are not generally weaker for vices than for virtues. Constraints on vice purchases are self-imposed and strategic rather than driven by simple preferences. We suggest that rationing their vice inventories at the point of purchase allows consumers to limit subsequent consumption. As a result of purchase quantity rationing, however, vice buyers forgo savings from price reductions through quantity discounts, effectively paying price premiums for the opportunity to engage in self-control. Thus, purchase quantity rationing vice consumers are relatively price in sensitive. From a managerial and public policy perspective, our findings should offer marketing practitioners in many consumer goods industries new opportunities to increase profits through segmentation and price discrimination based on consumer self-control. They can charge premium prices for small sizes of vices, relative to the corresponding quantity discounts for virtues. Virtue consumers, on the other hand, will buy larger amounts even when quantity discounts are relatively shallow. A key conceptual contribution of this paper lies in showing how marketing researchers can investigate a whole class of strategic self-constraining consumer behaviors empirically. Moreover, this research is the first to extend previous, theoretical work on impulse control by empirically demonstrating its broader implications for marketing decision making.

Consumption Self-Control by Rationing Purchase Quantities of Virtue and Vice

List of Contributors. Series Preface. Notes on Contributors. Acknowledgements. 1 Introduction and Overview (P eter P. Belobaba and Amedeo Odoni). 1.1 Introduction: The Global Airline Industry. 1.2 Overview of Chapters. References. 2 The International Institutional and Regulatory Environment (Amedeo Odoni). 2.1 Introduction. 2.2 Background on the International Regulatory Environment. 2.3 Airline Privatization and International Economic Regulation. 2.4 Airports. 2.5 Air Traffic Management. 2.6 Key Organizations and Their Roles. 2.7 Summary and Conclusions. References. 3 Overview of Airline Economics, Markets and Demand (Peter P. Belobaba). 3.1 Airline Terminology and Definitions. 3.2 Air Transportation Markets. 3.3 Origin-Destination Market Demand. 3.4 Air Travel Demand Models. 3.5 Airline Competition and Market Share. 3.6 Chapter Summary. References. 4 Fundamentals of Pricing and Revenue Management ( Peter P. Belobaba). 4.1 Airline Prices and O-D Markets. 4.2 Airline Differential Pricing. 4.3 Airline Revenue Management. References. 5 Airline Operating Costs and Measures of Productivity ( Peter P. Belobaba). 5.1 Airline Cost Categorization. 5.2 Operating Expense Comparisons. 5.3 Comparisons of Airline Unit Costs. 5.4 Measures of Airline Productivity. References. 6 The Airline Planning Process (Peter P. Belobaba). 6.1 Fleet Planning. 6.2 Route Planning. 6.3 Airline Schedule Development. 6.4 The Future: Integrated Airline Planning. References. 7 Airline Schedule Optimization (Cynthia Barnhart). 7.1 Schedule Optimization Problems. 7.2 Fleet Assignment. 7.3 Schedule Design Optimization. 7.4 Crew Scheduling. 7.5 Aircraft Maintenance Routing and Crew Pairing Optimization. 7.6 Future Directions for Schedule Optimization. References. 8 Airline Flight Operations (Alan H. Midkiff, R. John Hansman and Tom G. Reynolds). 8.1 Introduction. 8.2 Regulation and Scheduling. 8.3 Flight Crew Activities During a Typical Flight. 8.4 Summary. 8.5 Appendix: List of Acronyms. References. 9 Irregular Operations: Schedule Recovery and Robustness (Cynthia Barnhart). 9.1 Introduction. 9.2 Irregular Operations. 9.3 Robust Airline Scheduling. 9.4 Directions for Ongoing and Future Work on Schedule Recovery from Irregular Operations. References. 10 Labor Relations and Human Resource Management in the Airline Industry (Jody Hoffer Gittell, Andrew von Nordenflycht, Thomas A. Kochan, Robert McKersie and Greg J. Bamber). 10.1 Alternative Strategies for the Employment Relationship. 10.2 Labor Relations in the US Airline Industry. 10.3 Labor Relations in the Airline Industry in Other Countries. 10.4 Human Resource Management at Airlines. 10.5 Conclusions. References. 11 Aviation Safety and Security (Arnold Barnett). 11.1 Safety. 11.2 Security. References. 12 Airports (Amedeo Odoni). 12.1 Introduction. 12.2 General Background. 12.3 Physical Characteristics. 12.4 Capacity, Delays and Demand Management. 12.5 Institutional, Organizational and Economic Characteristics. References. 13 Air Traffic Control (R. John Hansman and Amedeo Odoni). 13.1 Introduction. 13.2 The Generic Elements of an ATC System. 13.3 Airspace and ATC Structure. 13.4 ATC Operations. 13.5 Standard Procedures. 13.6 Capacity Constraints. 13.7 Congestion and Air Traffic Management. 13.8 Future ATC Systems. References. 14 Air Transport and the Environment (Karen Marais and Ian A. Waitz). 14.1 Introduction. 14.2 Limiting Aviation's Environmental Impact: The Role of Regulatory Bodies. 14.3 Airport Water Quality Control. 14.4 Noise. 14.5 Surface Air Quality. 14.6 Impact of Aviation on Climate. 14.7 Summary and Looking Forward. References. 15 Information Technology in Airline Operations, Distribution and Passenger Processing (Peter P. Belobaba, William Swelbar and Cynthia Barnhart). 15.1 Information Technology in Airline Planning and Operations. 15.2 Airline Distribution Systems. 15.3 Distribution Costs and E-commerce Developments. 15.4 Innovations in Passenger Processing. References. 16 Critical Issues and Prospects for the Global Airline Industry (William Swelbar and Peter P. Belobaba). 16.1 Evolution of US and Global Airline Markets. 16.2 Looking Ahead: Critical Challenges for the Global Airline Industry. References. Index.

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The global airline industry

Given a flight schedule and set of aircraft, the fleet assignment problem is to determine which type of aircraft should fly each flight segment. This paper describes a basic daily, domestic fleet assignment problem and then presents chronologically the steps taken to solve it efficiently. Our model of the fleet assignment problem is a large multi-commodity flow problem with side constraints defined on a time-expanded network. These problems are often severely degenerate, which leads to poor performance of standard linear programming techniques. Also, the large number of integer variables can make finding optimal integer solutions difficult and time-consuming. The methods used to attack this problem include an interior-point algorithm, dual steepest edge simplex, cost perturbation, model aggregation, branching on set-partitioning constraints and prioritizing the order of branching. The computational results show that the algorithm finds solutions with a maximum optimality gap of 0.02% and is more than two orders of magnitude faster than using default options of a standard LP-based branch-and-bound code.

The fleet assignment problem: Solving a large-scale integer program

We present a column-generation model and branch-and-price-and-cut algorithm for origin-destination integer multicommodity flow problems. The origin-destination integer multicommodity flow problem is a constrained version of the linear multicommodity flow problem in which flow of a commodity (defined in this case by an origin-destination pair) may use only one path from origin to destination. Branch-and-price-and-cut is a variant of branch-and-bound, with bounds provided by solving linear programs using column-and-cut generation at nodes of the branch-and-bound tree. Because our model contains one variable for each origin destination path, for every commodity, the linear programming relaxations at nodes of the branch-and-bound tree are solved using column generation, i.e., implicit pricing of nonbasic variables to generate new columns or to prove LP optimality. We devise a new branching rule that allows columns to be generated efficiently at each node of the branch-and-bound tree. Then, we describe cuts (cover inequalities) that can be generated at each node of the branch-and-bound tree. These cuts help to strengthen the linear programming relaxation and to mitigate the effects of problem symmetry. We detail the implementation of our combined column and- cut generation method and present computational results for a set of test problems arising from telecommunications applications. We illustrate the value of our branching rule when used to find a heuristic solution and compare branch-and-price and branch-and-price-and-cut methods to find optimal solutions for highly capacitated problems.

Cynthia Barnhart

Papers

Branch-And-Price: Column Generation for Solving Huge Integer Programs

Consumption Self-Control by Rationing Purchase Quantities of Virtue and Vice

The global airline industry

The fleet assignment problem: Solving a large-scale integer program

Using Branch-and-Price-and-Cut to Solve Origin-Destination Integer Multicommodity Flow Problems