Star p-hub center problem and star p-hub median problem with bounded path lengths

Yaman, Hande; Elloumi, Sourour

doi:10.1016/j.cor.2012.02.005

Cited by 57 publications

(36 citation statements)

References 39 publications

(26 reference statements)

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“…They have also been extended to model more complex variants of HLPs including additional features of real applications. We refer to Campbell et al (2007), Alumur andKara (2009), Wagner (2008a), Ernst et al (2009), Yaman andElloumi (2012), Hwang and Lee (2013), and Lowe and Sim (2013) for formulations of p-hub center and hub covering problems.…”

Section: Formulating Hub Location Problemsmentioning

confidence: 98%

Hub Location Problems

Contreras

2015

Location Science

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Hub Location Problems (HLPs) lie at the heart of network design planning in transportation and telecommunication systems. They are a challenging class of optimization problems that focus on the location of hub facilities and on the design of hub networks. This chapter overviews the key distinguishing features, assumptions and properties commonly considered in HLPs. We highlight the role location and network design decisions play in the formulation and solution of HLPs. We also provide a concise overview of the main developments and most recent trends in hub location research. We cover various topics such as hub network topologies, flow dependent discounted costs, capacitated models, uncertainty, dynamic and multi-modal models, and competition and collaboration. We also include a summary of the most successful integer programming formulations and efficient algorithms that have been recently developed for the solution of HLPs.

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Section: Formulating Hub Location Problemsmentioning

confidence: 98%

Hub Location Problems

Contreras

2015

Location Science

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“…Yang, Liu, and Zhang () studied a p ‐hub centre problem with discrete random travel to minimize efficient time point. In addition, Yaman and Elloumi () introduced the star p ‐hub centre problem and the star p ‐hub median problem, where two mixed integer programming formulations with bounded path lengths were proposed for the first problem in order to minimize the total routing cost. It should be noted that Liang () showed the hardness of the star p ‐hub centre problem.…”

Section: Introductionmentioning

confidence: 99%

A knowledge‐based genetic algorithm for a capacitated fuzzy p‐hub centre network under uncertain information

2018

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In real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type. To facilitate this difficulty, in this paper, the travel times are assumed to be characterized by trapezoidal fuzzy variables to present a fuzzy capacitated single allocation p‐hub centre transportation (FCSApHCP) with uncertain information. The proposed FCSApHCP is redefined into its equivalent parametric integer non‐linear programming problem using credibility constraints. The aim is to determine the location of p capacitated hubs and the allocation of centre nodes to them in order to minimize the maximum travel time in a hub‐and‐centre network under uncertain environments. As the FCSApHCP is NP‐hard, a novel approach called knowledge‐based genetic algorithm (KBGA) is developed to solve it. This algorithm utilizes 2 knowledge modules to gain good and bad knowledge about hub locations and then saves them in a good and bad hub memory, respectively. As there is no benchmark available to validate the results obtained, a genetic algorithm with multiparent crossover is designed to solve the problem as well. Then, the algorithms are tuned to solve the problem, based on which their performances are analysed and then compared together statistically. The applicability of the proposed approach and the solution methodologies are demonstrated. Finally, sensitivity analyses on the discount factor in the network and the memory sizes of the proposed KBGA are conducted to provide more insights. The results show that appropriate values of memory sizes can enhance the convergence and save population diversity of KBGA simultaneously.

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“…The taxonomy of HLP suggests four ramifications of the original problems including Capacitated p-Median Problem Shamsipour et al (2012), HLP with star network structure Yaman (2008), Yaman & Elloumi (2012), p-hub center problem Campbell et al (2005a,b), and p-hub covering problem Kara & Tansel (2003). For a more detailed review of the literature one may refer to Alumur & Kara (2008), Farahani et al (2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Hub location under uncertainty: a minimax regret model for the capacitated problem with multiple allocations

Kazemian

Aref

2017

IJSCIM

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Abstract:In this paper the capacitated hub location problem is formulated by a minimax regret model, which takes into account uncertain setup cost and demand. We focus on hub location with multiple allocations as a strategic problem requiring one definite solution.Investigating how deterministic models may lead to sub-optimal solutions, we provide an efficient formulation method for the problem.A computational analysis is performed to investigate the impact of uncertainty on the location of hubs. The suggested model is also compared with an alternative method, seasonal optimization, in terms of efficiency and practicability. The results indicate the importance of incorporating stochasticity and variability of parameters in solving practical hub location problems. Applying our method to a case study derived from an industrial food production company, we solve a logistical problem involving seasonal demand and uncertainty. The solution yields a definite hub network configuration to be implemented throughout the planning horizon.

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Star p-hub center problem and star p-hub median problem with bounded path lengths

Cited by 57 publications

References 39 publications

Hub Location Problems

Hub Location Problems

A knowledge‐based genetic algorithm for a capacitated fuzzy p‐hub centre network under uncertain information

Hub location under uncertainty: a minimax regret model for the capacitated problem with multiple allocations

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