Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution
| Main Authors: | Seifbarghy, Mehdi, Rashidi Kahag, Mahdi, Akhavan Niaki, Seyed Taghi, Zabihi, Sina |
|---|---|
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2018
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/3816658 |
| ctrlnum |
3816658 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Seifbarghy, Mehdi</creator><creator>Rashidi Kahag, Mahdi</creator><creator>Akhavan Niaki, Seyed Taghi</creator><creator>Zabihi, Sina</creator><date>2018-08-22</date><description>A new multi-objective intermodal hub-location-allocation problem is modeled in this paper in which both the origin and the destination hub facilities are modeled as an M/M/m queuing system. The problem is being formulated as a constrained bi-objective optimization model to minimize the total costs as well as minimizing the total system time. A small-size problem is solved on the GAMS software to validate the accuracy of the proposed model. As the problem becomes strictly NP-hard, an MOIWO algorithm with an efficient chromosome structure and a fuzzy dominance method is proposed to solve large-scale problems. Since there is no benchmark available in the literature, an NSGA-II and an NRGA are developed to validate the results obtained. The parameters of all algorithms are tuned using the Taguchi method and their performances are statistically compared in terms of some multi-objective metrics. Finally, the entropy-TOPSIS method is applied to show that MOIWO is the best in terms of simultaneous use of all the metrics.</description><identifier>https://zenodo.org/record/3816658</identifier><identifier>10.1007/s40092-018-0288-0</identifier><identifier>oai:zenodo.org:3816658</identifier><language>eng</language><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/2.0/legalcode</rights><source>Journal of Industrial Engineering International 15(2) 221-248</source><subject>Intermodal P-hub median problem, Queuing systems, Multi-objective invasive weed optimization, Entropy-TOPSIS method.</subject><subject>Closed-loop supply chain network design, Competitive location, Contract design, Facility disruptions, Fuzzy logic, Genetic Algorithm, Heuristic algorithm, Location-allocation problem, Lost sales, Maximal covering location problem, Multi-echelon, NSGA-II, Quota allocation, SCOR, Social responsibility, Supplier selection, Supply chain management, Sustainable supply chain network design, Tabu search, Vendor Managed Inventory.</subject><title>Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution</title><type>Journal:Article</type><type>Journal:Article</type><recordID>3816658</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:Journal |
| author |
Seifbarghy, Mehdi Rashidi Kahag, Mahdi Akhavan Niaki, Seyed Taghi Zabihi, Sina |
| title |
Bi-objective optimization of multi-server intermodal hub-location-allocation problem in congested systems: modeling and solution |
| publishDate |
2018 |
| isbn |
4009201802880 |
| topic |
Intermodal P-hub median problem Queuing systems Multi-objective invasive weed optimization Entropy-TOPSIS method Closed-loop supply chain network design Competitive location Contract design Facility disruptions Fuzzy logic Genetic Algorithm Heuristic algorithm Location-allocation problem Lost sales Maximal covering location problem Multi-echelon NSGA-II Quota allocation SCOR Social responsibility Supplier selection Supply chain management Sustainable supply chain network design Tabu search Vendor Managed Inventory |
| url |
https://zenodo.org/record/3816658 |
| contents |
A new multi-objective intermodal hub-location-allocation problem is modeled in this paper in which both the origin and the destination hub facilities are modeled as an M/M/m queuing system. The problem is being formulated as a constrained bi-objective optimization model to minimize the total costs as well as minimizing the total system time. A small-size problem is solved on the GAMS software to validate the accuracy of the proposed model. As the problem becomes strictly NP-hard, an MOIWO algorithm with an efficient chromosome structure and a fuzzy dominance method is proposed to solve large-scale problems. Since there is no benchmark available in the literature, an NSGA-II and an NRGA are developed to validate the results obtained. The parameters of all algorithms are tuned using the Taguchi method and their performances are statistically compared in terms of some multi-objective metrics. Finally, the entropy-TOPSIS method is applied to show that MOIWO is the best in terms of simultaneous use of all the metrics. |
| id |
IOS16997.3816658 |
| institution |
ZAIN Publications |
| institution_id |
7213 |
| institution_type |
library:special library |
| library |
Cognizance Journal of Multidisciplinary Studies |
| library_id |
5267 |
| collection |
Cognizance Journal of Multidisciplinary Studies |
| repository_id |
16997 |
| subject_area |
Multidisciplinary |
| city |
Stockholm |
| province |
INTERNASIONAL |
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1 |
| repoId |
IOS16997 |
| first_indexed |
2022-06-06T06:25:49Z |
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2022-06-06T06:25:49Z |
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dc |
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1734908851851886592 |
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17.610468 |