An M/M/c queue model for hub covering location problem

Mohammadi, Mehrdad; Jolai, Fariborz; Rostami, Hamideh

doi:10.1016/j.mcm.2011.06.038

Cited by 89 publications

(36 citation statements)

References 26 publications

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“…They consider the network as an M/D/c queuing system where the arrival rate of demand to hub facilities is stochastic and c servers in hub facilities process the demand in a deterministic time. Mohammadi et al (2011) extend that research by considering an M/M/c queuing system on the hub-covering model where processing time is stochastic. In both of these researches, a probabilistic constraint controls the congestion in the hub facilities.…”

Section: Introductionmentioning

confidence: 81%

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

Yahyaei

Bashiri

2017

J Ind Eng Int

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The hub location problem arises in a variety of domains such as transportation and telecommunication systems. In many real-world situations, hub facilities are subject to disruption. This paper deals with the multiple allocation hub location problem in the presence of facilities failure. To model the problem, a two-stage stochastic formulation is developed. In the proposed model, the number of scenarios grows exponentially with the number of facilities. To alleviate this issue, two approaches are applied simultaneously. The first approach is to apply sample average approximation to approximate the two stochastic problem via sampling. Then, by applying the multiple cuts Benders decomposition approach, computational performance is enhanced. Numerical studies show the effective performance of the SAA in terms of optimality gap for small problem instances with numerous scenarios. Moreover, performance of multi-cut Benders decomposition is assessed through comparison with the classic version and the computational results reveal the superiority of the multi-cut approach regarding the computational time and number of iterations.

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Section: Introductionmentioning

confidence: 81%

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

Yahyaei

Bashiri

2017

J Ind Eng Int

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“…In some papers, it is assumed that uncertain parameters follow a known distribution model. For example, Mariano and Serra [11] suggested a queuing model for hub location problems in airline industry where the arrivals of planes follow a Poisson process (see also [12] as an extension of this study). In another study, Sim et al [13] developed a stochastic model for p-hub median problem in package delivery networks and assumed that the travel time between the nodes follow normal distribution.…”

Section: Literature Reviewmentioning

confidence: 95%

A robust decision-making approach for p-hub median location problems based on two-stage stochastic programming and mean-variance theory: a real case study

Ahmadi

Karimi

Davoudpour

et al. 2014

Int J Adv Manuf Technol

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The stochastic location-allocation p-hub median problems are related to long-term decisions made in risky situations. Due to the importance of this type of problems in real-world applications, the authors were motivated to propose an approach to obtain more reliable policies in stochastic environments considering the decision makers' preferences. Therefore, a systematic approach to make robust decisions for the single location-allocation p-hub median problem based on mean-variance theory and twostage stochastic programming was developed. The approach involves three main phases, namely location modeling, risk modeling, and decision making, each including several steps. In the first phase, the pertinent location-allocation model of the problem is developed in the form of a two-stage stochastic model based on its deterministic version. A risk measure, based on total cost function and mean-variance theory, is derived in the second phase. Furthermore, two heterogeneous terms of the risk measure have been normalized and an innovative procedure has been proposed to significantly improve the calculation efficiency. In the third phase, the Pareto solution is obtained, the frontier curve is depicted to determine the decision maker's risk aversion coefficient, and a robust policy is obtained through optimization based on decision makers' preferences. Finally, a case study of an automobile part distribution system with stochastic demand is described to further illustrate our risk management and analysis approach.

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“…Also, a two-stage integrated TS heuristic is proposed to improve the location and the allocation components of a solution to the problem. An algorithm for hub covering location problem, under crowdedness, with constraints and random variables are considered such as the transportation time and rate of arrived trucks has been proposed [14]. Another technique used to solve the incapacitated single allocation p-hub center problem with stochastic travel times using cutting planes and Benders decomposition [15].…”

Section: Introductionmentioning

confidence: 99%

Application Rough Set Theory and Decision Network as a New Approach to Simplify Airline Hubs Network Location

Shaaban¹

2016

IJIES

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Airline hub location selection problems have become one of the most popular and important issues not only in the truck transportation and the marine transportation, but also in the air transportation. There are different methods for selecting hub location, however, they are mostly dependent on engineer decision making and need for high cost. In this paper, a method of rules extraction for site hub location based on rough set theory and decision network is proposed. The information system attributes are firstly reduced and a decision network with different reduced levels is constructed. The network's node is initialized with the attribute reduction sets and the decision rule sets are extracted according to the node of the decision network. In addition, the coverage degree and certainty factor are applied to filter noise and evaluate the extraction rules. A practical case of local 15 cities track network designing for airlines is given. The results are found to be comparable with the previous practical methods. Furthermore, the obtained results indicate that the proposed method saves in the time and cost of the calculation.

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An M/M/c queue model for hub covering location problem

Abstract: International audienc

Cited by 89 publications

References 26 publications

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

Scenario-based modeling for multiple allocation hub location problem under disruption risk: multiple cuts Benders decomposition approach

A robust decision-making approach for p-hub median location problems based on two-stage stochastic programming and mean-variance theory: a real case study

Application Rough Set Theory and Decision Network as a New Approach to Simplify Airline Hubs Network Location

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