Hub location with flow economies of scale

O’Kelly, Morton E.; Bryan, Deborah

doi:10.1016/s0191-2615(98)00021-6

Cited by 218 publications

(73 citation statements)

References 12 publications

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“…Following the passage of the Airline Deregulation Act in 1978, a hub-and-spoke network structure became the dominant structure of the U.S. air transportation system, in which hubs served as intermediate vertices to consolidate traffic to and from spokes. In these structures, hubs are linked by high capacity interhub connections and spokes are linked to hubs through low capacity spoke-hub connections (Bryan and O'Kelly 1999;Goetz and Sutton 1997;Horner and O'Kelly 2001;Jaillet et al 1996;O'Kelly 1998;O'Kelly and Bryan 1998;O'Kelly and Miller 1994;Shaw 1993). Recently, the emergence of a number of successful low cost carriers has resulted in another geospatial structure-point-to-point connections that currently coexist within the hub-and-spoke structure in the U.S. air transportation network.…”

Section: Introductionmentioning

confidence: 99%

Exploring the structure of the U.S. intercity passenger air transportation network: a weighted complex network approach

Harriss

2008

GeoJournal

137

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The U.S. airline network is one of the most advanced transportation infrastructures in the world. It is a complex geospatial structure that sustains a variety of dynamics including commercial, public, and military operations and services. We study the U.S. domestic intercity passenger air transportation network using a weighted complex network methodology, in which vertices represent cities and edges represent intercity airline connections weighted by average daily passenger traffic, non-stop distance, and average oneway fares. We find that U.S. intercity passenger air transportation network is a small-world network accompanied by dissortative mixing patterns and rich-club phenomenon, implying that large degree cities (or hub cities) tend to form high traffic volume interconnections among each other and large degree cities tend to link to a large number of small degree cities. The interhub air connections tend to form interconnected triplets with high traffic volumes, long non-stop distances, and low average one-way fares. The structure of the U.S. airline network reflects the dynamic integration of pre-existing urban and national transportation infrastructure with the competitive business strategies of commercial airlines. In this paper we apply an emerging methodology to representing, analyzing, and modeling the complex interactions associated with the physical and human elements of the important U.S. national air transport and services infrastructure.

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Section: Introductionmentioning

confidence: 99%

Exploring the structure of the U.S. intercity passenger air transportation network: a weighted complex network approach

Harriss

2008

GeoJournal

137

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“…In the latter, the cost function results from unit transport cost and inter-hub transport is discounted. However, O'Kelly and Bryan (1998) claim that the inclusion of an exogenously determined discount applied to all inter-hub arcs regardless of the differences in the flows travelling across them, oversimplifies the problem. The authors claim that the cost has to be presented by a non-linear function such that marginal travel cost decreases as flows increase.…”

Section: Cost Function: Plainly Linearmentioning

confidence: 99%

Enriching the tactical network design of express service carriers with fleet scheduling characteristics

et al. 2010

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Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the Flex Serv Manuf J (2010) 22:3-35 DOI 10.1007 network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers' legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions. 123

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“…Alternative approaches are presented to model economies of scale; flow dependent discount factor [27] or hub arc models [9]. The studies also vary in their solution techniques.…”

Section: Introductionmentioning

confidence: 99%

The P-Hub maximal covering problem and extensions for gradual decay functions

Peker

Kara

2015

Omega

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a b s t r a c tThe p-hub maximal covering problem aims to find the best locations for hubs so as to maximize demands within a coverage distance with a predetermined number of hubs. Classically, the problem is defined in the framework of binary coverage only; an origin-destination pair is covered if the cost (time, etc.) is lower than the critical value, and not covered at all if the cost is greater than the critical value. In this paper, we extend the definition of coverage, introducing "partial coverage", which changes with distance. We present new and efficient mixed-integer programming models that are also valid for partial coverage for single and multiple allocations. We present and discuss the computational results with different data sets.

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Hub location with flow economies of scale

Cited by 218 publications

References 12 publications

Exploring the structure of the U.S. intercity passenger air transportation network: a weighted complex network approach

Exploring the structure of the U.S. intercity passenger air transportation network: a weighted complex network approach

Enriching the tactical network design of express service carriers with fleet scheduling characteristics

The P-Hub maximal covering problem and extensions for gradual decay functions

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