Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution

Bashiri, Mahdi; Mirzaei, Masoud; Randall, Marcus

doi:10.1016/j.apm.2012.07.018

Cited by 61 publications

(30 citation statements)

References 27 publications

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“…Totally, 14 articles used the VIKOR technique in the fields of Construction Management (Table 3). Bashiri, et al [128] Fuzzy VIKOR Utilized Transportation systems…”

Section: Construction Managementmentioning

confidence: 99%

VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications

Mardani

Zavadskas

Govindan³

et al. 2016

Sustainability

286

137

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Abstract:The main objective of this paper is to present a systematic review of the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method in several application areas such as sustainability and renewable energy. This study reviewed a total of 176 papers, published in 2004 to 2015, from 83 high-ranking journals; most of which were related to Operational Research, Management Sciences, decision making, sustainability and renewable energy and were extracted from the "Web of Science and Scopus" databases. Papers were classified into 15 main application areas. Furthermore, papers were categorized based on the nationalities of authors, dates of publications, techniques and methods, type of studies, the names of the journals and studies purposes. Theresults of this study indicated that more papers on VIKOR technique were published in 2013 than in any other year. In addition, 13 papers were published about sustainability and renewable energy fields. Furthermore, VIKOR and fuzzy VIKOR methods, had the first rank in use. Additionally, the Journal of Expert Systems with Applications was the most significant journal in this study, with 27 publications on the topic. Finally, Taiwan had the first rank from 22 nationalities which used VIKOR technique.

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“…Totally, 14 articles used the VIKOR technique in the fields of Construction Management (Table 3). Bashiri, et al [128] Fuzzy VIKOR Utilized Transportation systems…”

Section: Construction Managementmentioning

confidence: 99%

VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications

Mardani

Zavadskas

Govindan³

et al. 2016

Sustainability

286

137

View full text Add to dashboard Cite

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“…However, they did not take into account the capacity restrictions of the hubs. Furthermore, Bashiri, Mirzaei, and Randall () proposed a hybrid approach for a p ‐hub centre problem using both qualitative and quantitative parameters to minimize the longest travel time. They utilized the fuzzy VIKOR to model a hybrid solution and solved the problem using a genetic algorithm (GA).…”

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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“…After a while, Liang (2013) analyzed the hardness of the star p-hub center problem. Bashiri et al (2013) considered a number of qualitative parameters for the hub location problem and proposed a GA based heuristic to solve this problem under capacitated constraints. In order to deal with uncertainty and qualitative parameters, fuzzy systems were utilized.…”

Section: Fig 1 a Single Allocation (Left Hand Side) And Multiple Almentioning

confidence: 99%

“…2012 (Yaman & Elloumi, 2012) star p-hub center and star p-hub median problems with bounded path lengths are introduced. 2013 (Bashiri et al, 2013) A fuzzy capacitated p-hub center problem is introduced and a genetic algorithm solution is presented. 2013 (Farahani et al, 2013) A comprehensive survey of hub location problems is presented.…”

Section: Fig 1 a Single Allocation (Left Hand Side) And Multiple Almentioning

confidence: 99%

Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing

Rabbani¹,

Kazemi²

2015

10.5267/j.ijiec

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In the existing literature, there are a huge number of studies focused on p-hub median problems and inventing heuristic or metaheuristic algorithms for solving them. But such analogous body of literature does not exist for its counterpart problem; p-hub center problem. In fact, since p-hub center has been lately introduced and has a particular objective function, minimizing the maximum cost between origin-destination nodes, there are few studies investigating the problem and the challenges for solving it. In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets. These two algorithms are one single solution-based algorithm, Simulated Annealing (SA), and one population-based metaheuristic, Genetic Algorithm (GA). Because of the particular nature of the problem, Dijkstra's algorithm has been incorporated in the fitness function calculation part of the proposed methods. The numerical results of running the GA and SA for standard test problems show that for smaller scale test problems, single solution-based SA shows greater performance versus GA but for larger scales of data sets the GA generally yield more desirable solutions.

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Modeling fuzzy capacitated p-hub center problem and a genetic algorithm solution

Cited by 61 publications

References 27 publications

VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications

VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications

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

Solving uncapacitated multiple allocation p-hub center problem by Dijkstra’s algorithm-based genetic algorithm and simulated annealing

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