A new approach to optimize a hub covering location problem with a queue estimation component using genetic programming

Hasanzadeh, Hamid; Bashiri, Mahdi; Amiri, Amirhossein

doi:10.1007/s00500-016-2398-1

Cited by 18 publications

(3 citation statements)

References 33 publications

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“…Moreover, hybridization of genetic operators and local searches with other meta-heuristics such as particle swarm optimization (PSO) helps to prevent premature convergence and increase the performance of proposed algorithms (see Yang et al, 2013;Gao and Qin, 2016). Some recent papers that employed the principals of GA in solving the HLPs are Kratica et al (2011), Bashiri et al (2013, Mohammadi et al (2013), Kratica et al (2007), Bashiri et al (2013), Yang et al (2013), Damgacioglu et al (2015), Ebrahimi-Zade et al (2016), Gao and Qin (2016), Qin and Gao (2017), Hasanzadeh et al (2016), Bashiri et al (2017), andLüer-Villagra et al (2019). In the current paper, we aim to study two improved meta-heuristics by taking the following features into account:…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid evolutionary algorithms and Lagrangian relaxation for multi-period star hub median problem considering financial and service quality issues

Tikani

Ramezanian

Setak

et al. 2021

Engineering Applications of Artificial Intelligence

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid evolutionary algorithms and Lagrangian relaxation for multi-period star hub median problem considering financial and service quality issues

Tikani

Ramezanian

Setak

et al. 2021

Engineering Applications of Artificial Intelligence

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“…Gao and Qin (2016) propose a hybrid intelligent algorithm by combining the principle of nearby with the GA. Ghezavati and Hosseinifar (2018) apply GA and particle swarm optimisation (PSO) and used a Taguchi design to determine the appropriate parameter settings. They found that PSO performed better than GA. Hasanzadeh et al (2018) administered PSO as a solution method along with genetic programming to determine the lower bound for the proposed model. Mohammadi et al (2016) developed an evolutionary algorithm based on the game theory and invasive weed optimisation to solve the hub location problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Diversification-based learning simulated annealing algorithm for hub location problems

Rathore

Nandi

Pandey

et al. 2019

BIJ

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Purpose The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems. Design/methodology/approach This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets. Findings This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates. Practical implications Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time. Originality/value To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

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“…As the demand for freight increases with population growth, the importance of the proper design of transportation networks in uncertain conditions becomes apparent (Davari et al, 2013). The use of hubs in transportation networks not only significantly reduces operating costs and network structure preparation costs and delivery time, but also causes better services to customers and reduces the harmful effects of environmental pollution (Hasanzadeh et al, 2018). Therefore, the main concept of hub covering location-routing in the design of a transportation network is that a number of decisions must be made, including the location of hub facilities and non-hub nodes, the allocation of all source and destination nodes (non-hub) directly to the hub, and flow routing.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Objective Model of the Hub Covering Location-Routing Problem with Simultaneous Delivery and Pickup Under Uncertainty

Ghahremani-Nahr,

Nozari,

Samadi Parviznejad

et al. 2023

Preprint

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In this paper, a model of the hub covering the location-routing problem with simultaneous delivery and pickup should be designed in order to minimize the total costs, minimize the maximum vehicles travel time and minimize the amount of CO_2 gas emissions. Two-stage stochastic programming is used in this paper to control the uncertainty parameters of the problem. The mathematical model aims to decide the appropriate location of hubs and vehicle routing for simultaneous delivery and pickup of products. In this regard, the results obtained from the LP-Metric method show that there is a conflict between the objective functions, and with the reduction of the amount of CO_2 gas emissions and the reduction of the maximum vehicles travel time, the total costs have increased. On the other hand, by examining the economical factor, it was observed that by reducing this factor, the robustness factors are changed and the total costs of the network are reduced. MOIWO and MOALO were used to solve the problem in large scale, the result of which was the existence of a significant difference between the calculation time averages. Also, the efficiency of these methods was very high compared to the LP-Metric method and the maximum relative difference was less than 2.83%. The results of numerical examples show a higher efficiency of the MOALO than the MOIWO.

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A new approach to optimize a hub covering location problem with a queue estimation component using genetic programming

Cited by 18 publications

References 33 publications

Hybrid evolutionary algorithms and Lagrangian relaxation for multi-period star hub median problem considering financial and service quality issues

Hybrid evolutionary algorithms and Lagrangian relaxation for multi-period star hub median problem considering financial and service quality issues

Diversification-based learning simulated annealing algorithm for hub location problems

The Multi-Objective Model of the Hub Covering Location-Routing Problem with Simultaneous Delivery and Pickup Under Uncertainty

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