GRASP for the uncapacitated r-allocation p-hub median problem

Peiró, Juanjo; Corberán, Ángel; Martı́, Rafael

doi:10.1016/j.cor.2013.08.026

Cited by 41 publications

(19 citation statements)

References 18 publications

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“…Recently, elaborative mathematical models based on the above-mentioned pieces of research have been devised. To know more, other research pieces [15][16][17][18] can also be referred to in this regard.…”

Section: Hub Median Problemmentioning

confidence: 98%

“…Constraint (16) enforces that the amount of raw materials or final products going from node j to hub m at period t in backward must be less than the capacity of vehicles to be used in the related link. The number of vehicle types v l passing through in forward and backward between nodes i and k in transportation mode l is given by constraint (17).…”

Section: Proposed Multi-objective Hub Location Problemmentioning

confidence: 99%

See 1 more Smart Citation

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

Ghodratnama

Tavakkoli–Moghaddam

Azaron

2015

Applied Soft Computing

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Section: Hub Median Problemmentioning

confidence: 98%

Section: Proposed Multi-objective Hub Location Problemmentioning

confidence: 99%

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

Ghodratnama

Tavakkoli–Moghaddam

Azaron

2015

Applied Soft Computing

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“…Recently, Peiró et al investigated the uncapacitated r ‐allocation p ‐hub median problem. The authors proposed a heuristic approach based on greedy randomized adaptive search procedure (GRASP) metaheuristic that employs 3 local search procedures, in which every node is assigned to r of the selected p hubs ( r ≤ p ); these procedures were carefully applied to obtain favorable solutions.…”

Section: Literature Review On Uncapacitated Single Allocation P‐hub Mmentioning

confidence: 99%

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

Fernandez

Ferone

Juan

et al. 2018

Trans Emerging Tel Tech

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The hub location problem has gained attention over the last decades. In telecommunications network, a hub is a place of concurrence in where the work of the network is centralized with the purpose of delivering out the data that arrives from one or more directions to other destinations. There are different versions of the hub location problem depending upon (1) the existence or not of restrictions on the capacity related to the volume of flow a hub is allowed to support, (2) the existence or not of a set‐up cost associated with selecting any node as a hub, etc. In these types of configurations, the hubs serve as connection point between 2 installations, allowing, in this way, to replace a large amount of direct connections between all pair of the nodes, therefore, minimizing the total transportation cost of the network. Thus, this work proposes a 2‐stage metaheuristic based on the combination of biased‐randomized technique with an iterated local search framework for solving the uncapacitated single allocation p‐hub median problem, with computational results that validate the methodology for large‐size instances from the literature.

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“…Kratica presented an electromagnetism‐like metaheuristic for the uncapacitated multiple allocation p‐hub median problem. Peiró et al developed a heuristic based on the GRASP (Greedy Randomized Adaptive Search Procedure) methodology for the uncapacitated r‐allocation p‐hub median problem. Martí et al presented a scatter search implementation for an uncapacitated p‐hub median problem.…”

Section: Introductionmentioning

confidence: 99%

“…Kratica 24 presented an electromagnetism-like metaheuristic for the uncapacitated multiple allocation p-hub median problem. Peiró et al 25…”

mentioning

confidence: 99%

p‐hub median location optimization of hub‐and‐spoke air transport networks in express enterprise

Zhang

Dong

Liu

2018

Concurrency and Computation

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Summary Based on the single allocation p‐hub median location problem in hub‐and‐spoke networks, this paper studies the aviation hubs location optimization of express enterprise, primarily using p‐median model and greedy dropping heuristic algorithm to solve the initial candidate locations of the aviation hubs by Lingo. Then, the integer program model of the location and allocation of the aviation hubs was set up, with the upper limit of the distribution distance as the main constraint. The improved locations were calculated by immune algorithm on Matlab. The numerical example of A express enterprise was presented. After analyzing the status and the problems of aviation hub location in East China, the two best hub locations Wuxi and Hangzhou were finally determined by above model with the total cost reduced by 19.6%. The optimization results show the validation of the model.

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GRASP for the uncapacitated r-allocation p-hub median problem

Cited by 41 publications

References 18 publications

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview

A 2‐stage biased‐randomized iterated local search for the uncapacitated single allocation p‐hub median problem

p‐hub median location optimization of hub‐and‐spoke air transport networks in express enterprise

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