Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach
Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of t...
- Autores:
-
Syah, Rahmad
Elveny, Marischa
Soerjati, Enni
Grimaldo Guerrero, John William
Read Jowad, Rawya
Suksatan, Wanich
Aravindhan, Surendar
Yuryevna Voronkova, Olga
Mavaluru, Dinesh
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9430
- Acceso en línea:
- https://hdl.handle.net/11323/9430
https://doi.org/10.2478/fcds-2022-0010
https://repositorio.cuc.edu.co/
- Palabra clave:
- Hub
Reinforced epsilon constraint method
Multilevel services
Queue theory
Multi-objective optimization
Location-assignment
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| title |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| spellingShingle |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach Hub Reinforced epsilon constraint method Multilevel services Queue theory Multi-objective optimization Location-assignment |
| title_short |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| title_full |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| title_fullStr |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| title_full_unstemmed |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| title_sort |
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach |
| dc.creator.fl_str_mv |
Syah, Rahmad Elveny, Marischa Soerjati, Enni Grimaldo Guerrero, John William Read Jowad, Rawya Suksatan, Wanich Aravindhan, Surendar Yuryevna Voronkova, Olga Mavaluru, Dinesh |
| dc.contributor.author.spa.fl_str_mv |
Syah, Rahmad Elveny, Marischa Soerjati, Enni Grimaldo Guerrero, John William Read Jowad, Rawya Suksatan, Wanich Aravindhan, Surendar Yuryevna Voronkova, Olga Mavaluru, Dinesh |
| dc.subject.proposal.eng.fl_str_mv |
Hub Reinforced epsilon constraint method Multilevel services Queue theory Multi-objective optimization Location-assignment |
| topic |
Hub Reinforced epsilon constraint method Multilevel services Queue theory Multi-objective optimization Location-assignment |
| description |
Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer. |
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2022 |
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2022-08-04T14:25:14Z |
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2022-08-04T14:25:14Z |
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2022 |
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Syah,R.,Elveny,M.,Soerjati,E.,Guerrero,J.,Jowad,R.,Suksatan,W.,Aravindhan,S.,Voronkova,O. & Mavaluru,D.(2022).Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach. Foundations of Computing and Decision Sciences,47(2) 177-192. https://doi.org/10.2478/fcds-2022-0010 |
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0867-6356 |
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https://hdl.handle.net/11323/9430 |
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https://doi.org/10.2478/fcds-2022-0010 |
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10.2478/fcds-2022-0010 |
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2300-3405 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Syah,R.,Elveny,M.,Soerjati,E.,Guerrero,J.,Jowad,R.,Suksatan,W.,Aravindhan,S.,Voronkova,O. & Mavaluru,D.(2022).Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach. Foundations of Computing and Decision Sciences,47(2) 177-192. https://doi.org/10.2478/fcds-2022-0010 0867-6356 10.2478/fcds-2022-0010 2300-3405 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9430 https://doi.org/10.2478/fcds-2022-0010 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Foundations of Computing and Decision Sciences |
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[1] Li, K., Li, X., Qiao, D., Ding, Y., & Wang, L. Message Queue Optimization Model Based on Periodic Execution and Category Priority. In Journal of Physics: Conference Series (Vol. 1486, No. 2, p. 022046). IOP Publishing, 2020, April. [2] Darestani, S. A., & Hemmati, M. Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system. Computers & Industrial Engineering, 136, 277-292, 2019. [3] Chen, X., Xu, C., Wang, M., Wu, Z., Zhong, L., & Grieco, L. A. Augmented Queuebased Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration. IEEE Transactions on Circuits and Systems for Video Technology, 2020. [4] Aboolian, R.,Berman, O.and Drezner,Z. The multiple server center location problem. Annals of Operations Research, 167(1), pp.337-352, 2009. [5] Aghaei,J., Amjady,N.and Shayanfar, H.A. Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Applied Soft Computing, 11(4), pp.3846-3858, 2011. [6] Araz, O.M., Fowler,J.W.and Nafarrate, A.R. Optimizing service times for a public health emergency using a genetic algorithm: Locating dispensing sites and allocating medical staff.IIE Transactions on Healthcare Systems Engineering, 4(4), pp.178-190, 2014. [7] Bhat, U.N. An Introduction to Queueing Theory:Modeling and Analysis in Applications, 2nd edition, Birkhäuser Basel, 2015. [8] Cooper, L. Location-allocation problems. Operations Research,11, 331–344, 1963. [9] Cooper, R.B. Introduction to Queuing Theory. 2nd Edition, New York: Elsevier North Holland, 1981. [10] Daskin.M.S. Network and discrete location:models, algorithms, and applications.John Wiley & Sons, 2011. [11] Hajipour, V.,Fattahi, P.,Tavana, M. and Di Caprio, D. Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based metaheuristics.Applied Mathematical Modelling,40(7), pp.4948-4969, 2016. [12] Harewood,S.I. Emergency ambulance deployment in Barbados: a multi-objective approach.Journal of the Operational Research Society,53(2), pp. 185-192, 2002. [13] Heragu, S.S. Facilities design.CRC Press, 2008. [14] Hodgson, M.J. A Flow-Capturing Location-Allocation Model.Geographical Analysis, 22(3), pp. 270-279, 1990. [15] Larson, R.C. A hypercube queuing model for facility location and redistricting in urban emergency services, Computers and Operations Research, 1:67-95, 1974. [16] Marianov, V. and Serra, D. Hierarchical location-allocation models for congested systems.European Journal of Operational Research, 135(1), pp. 195-208, 2001. [17] Mavrotas, G. Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems.Appl Math Comput, 2 13:455-465,2009. [18] Myerson, P. Supply chain and logistics management made easy.methods and applications for planning operations, integration.control and improvement, and network design.Pearson Education, 2015. [19] Owen,S.H. and Daskin, M.S. Strategic facility location:A review.European Journal of operational research,111(3), pp.423447, 1998. [20] Pasandideh,S.H.R. and Niaki,S.T.A. Genetic application in a facility location problem with random demand within queuing framework.Journal of Intelligent Manufacturing, 23(3), pp.651-659, 2012. [21] Pasandideh.S.H.R., Niaki.S.T.A. and Hajipour, V. A multi-objective facility location model with batch arrivals:two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24(2), pp.331-348, 2013. [22] Porter, A.L. Forecasting and management of technology (Vol. 18).John Wiley & Sons, 1991. [23] Rahmati,S.H.A., Hajipour, V.and Niaki, S.T.A. A soft-computing Pareto-based metaheuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing.13(4) pp. 1728-1740, 2013. [24] ReVelle,C.S.and Eiselt, H.A. Location analysis: A synthesis and survey. European Journal of Operational Research, 165(1),pp.1-19, 2005. [25] Syam, S.S. A multiple server location-allocation model for service system design. Computers & Operations Research, 35(7), pp.2248-2265, 2008. [26] Tavakkoli-Moghaddam, R., Vazifeh-Noshafagh,S.,Talei zadeh, A.A., Hajipour,V.and Mahmoudi, A. Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems.Engineering Optimization, 49(1), pp. 136- 160, 2017. [27] Wang, Q., Batta, R. and Rump.C.M. Algorithms for a facility location problem with stochastic customer demand and immobile servers.Annals of operations Research,111(1-4), pp.17-34, 2002. [28] Fakhrzad, M. B., Amir M. G., and Farzaneh B., "A mathematical model for P-hub median location problem to multiple assignments between non-hub to hub nodes under fuzzy environment." JOURNAL OF MANAGEMENT AND ACCOUNTING STUDIES 3, no. 02 : 61-67, 2015. [29] Fatemeh, T., and Mahmoud V., "Green reverse supply chain management with locationrouting-inventory decisions with simultaneous pickup and delivery." Journal of Research in Science, Engineering and Technology 9, no. 02: 78-107,2021. [30] Hasani, A., Mokhtari, H., & Fattahi, M. A multi-objective optimization approach for green and resilient supply chain network design: a real-life Case Study. Journal of Cleaner Production, 278, pp. 123199, 2021. [31] Luo, L., Li, H., Wang, J., & Hu, J. Design of a combined wind speed forecasting system based on decomposition-ensemble and multi-objective optimization approach. Applied Mathematical Modelling, 89, pp. 49-72, 2021. [32] Fonseca, J. D., Commenge, J. M., Camargo, M., Falk, L., & Gil, I. D. Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach. Applied Energy, 290, 116746, 2021. [33] Mohammed, A., Naghshineh, B., Spiegler, V., & Carvalho, H. Conceptualising a supply and demand resilience methodology: A hybrid DEMATEL-TOPSIS-possibilistic multiobjective optimization approach. Computers & Industrial Engineering, 160, p. 107589, 2021. [34] Wang, C. H., & Chen, N. A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths. Transportation, 48(4), pp. 1967-1986, 2021. [36] Ghasemi, P., & Khalili-Damghani, K. A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning. Mathematics and computers in simulation, 179, pp. 69-95, 2021. [37] Khalili-Damghani, K., Tavana, M., & Ghasemi, P. A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, pp. 1-39, 2021. |
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Syah, RahmadElveny, MarischaSoerjati, EnniGrimaldo Guerrero, John WilliamRead Jowad, RawyaSuksatan, WanichAravindhan, SurendarYuryevna Voronkova, OlgaMavaluru, Dinesh2022-08-04T14:25:14Z2022-08-04T14:25:14Z2022Syah,R.,Elveny,M.,Soerjati,E.,Guerrero,J.,Jowad,R.,Suksatan,W.,Aravindhan,S.,Voronkova,O. & Mavaluru,D.(2022).Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach. Foundations of Computing and Decision Sciences,47(2) 177-192. https://doi.org/10.2478/fcds-2022-00100867-6356https://hdl.handle.net/11323/9430https://doi.org/10.2478/fcds-2022-001010.2478/fcds-2022-00102300-3405Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.16 páginasapplication/pdfengWalter de Gruyter GmbHGermany© 2022 Rahmad Syah et al., published by Sciendo This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approachArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://sciendo.com/es/article/10.2478/fcds-2022-0010Foundations of Computing and Decision Sciences[1] Li, K., Li, X., Qiao, D., Ding, Y., & Wang, L. Message Queue Optimization Model Based on Periodic Execution and Category Priority. In Journal of Physics: Conference Series (Vol. 1486, No. 2, p. 022046). IOP Publishing, 2020, April.[2] Darestani, S. A., & Hemmati, M. Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system. Computers & Industrial Engineering, 136, 277-292, 2019.[3] Chen, X., Xu, C., Wang, M., Wu, Z., Zhong, L., & Grieco, L. A. Augmented Queuebased Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration. IEEE Transactions on Circuits and Systems for Video Technology, 2020.[4] Aboolian, R.,Berman, O.and Drezner,Z. The multiple server center location problem. Annals of Operations Research, 167(1), pp.337-352, 2009.[5] Aghaei,J., Amjady,N.and Shayanfar, H.A. Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Applied Soft Computing, 11(4), pp.3846-3858, 2011.[6] Araz, O.M., Fowler,J.W.and Nafarrate, A.R. Optimizing service times for a public health emergency using a genetic algorithm: Locating dispensing sites and allocating medical staff.IIE Transactions on Healthcare Systems Engineering, 4(4), pp.178-190, 2014.[7] Bhat, U.N. An Introduction to Queueing Theory:Modeling and Analysis in Applications, 2nd edition, Birkhäuser Basel, 2015.[8] Cooper, L. Location-allocation problems. Operations Research,11, 331–344, 1963.[9] Cooper, R.B. Introduction to Queuing Theory. 2nd Edition, New York: Elsevier North Holland, 1981.[10] Daskin.M.S. Network and discrete location:models, algorithms, and applications.John Wiley & Sons, 2011.[11] Hajipour, V.,Fattahi, P.,Tavana, M. and Di Caprio, D. Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based metaheuristics.Applied Mathematical Modelling,40(7), pp.4948-4969, 2016.[12] Harewood,S.I. Emergency ambulance deployment in Barbados: a multi-objective approach.Journal of the Operational Research Society,53(2), pp. 185-192, 2002.[13] Heragu, S.S. Facilities design.CRC Press, 2008.[14] Hodgson, M.J. A Flow-Capturing Location-Allocation Model.Geographical Analysis, 22(3), pp. 270-279, 1990.[15] Larson, R.C. A hypercube queuing model for facility location and redistricting in urban emergency services, Computers and Operations Research, 1:67-95, 1974.[16] Marianov, V. and Serra, D. Hierarchical location-allocation models for congested systems.European Journal of Operational Research, 135(1), pp. 195-208, 2001.[17] Mavrotas, G. Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems.Appl Math Comput, 2 13:455-465,2009.[18] Myerson, P. Supply chain and logistics management made easy.methods and applications for planning operations, integration.control and improvement, and network design.Pearson Education, 2015.[19] Owen,S.H. and Daskin, M.S. Strategic facility location:A review.European Journal of operational research,111(3), pp.423447, 1998.[20] Pasandideh,S.H.R. and Niaki,S.T.A. Genetic application in a facility location problem with random demand within queuing framework.Journal of Intelligent Manufacturing, 23(3), pp.651-659, 2012.[21] Pasandideh.S.H.R., Niaki.S.T.A. and Hajipour, V. A multi-objective facility location model with batch arrivals:two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24(2), pp.331-348, 2013.[22] Porter, A.L. Forecasting and management of technology (Vol. 18).John Wiley & Sons, 1991.[23] Rahmati,S.H.A., Hajipour, V.and Niaki, S.T.A. A soft-computing Pareto-based metaheuristic algorithm for a multi-objective multi-server facility location problem. Applied Soft Computing.13(4) pp. 1728-1740, 2013.[24] ReVelle,C.S.and Eiselt, H.A. Location analysis: A synthesis and survey. European Journal of Operational Research, 165(1),pp.1-19, 2005.[25] Syam, S.S. A multiple server location-allocation model for service system design. Computers & Operations Research, 35(7), pp.2248-2265, 2008.[26] Tavakkoli-Moghaddam, R., Vazifeh-Noshafagh,S.,Talei zadeh, A.A., Hajipour,V.and Mahmoudi, A. Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems.Engineering Optimization, 49(1), pp. 136- 160, 2017.[27] Wang, Q., Batta, R. and Rump.C.M. Algorithms for a facility location problem with stochastic customer demand and immobile servers.Annals of operations Research,111(1-4), pp.17-34, 2002.[28] Fakhrzad, M. B., Amir M. G., and Farzaneh B., "A mathematical model for P-hub median location problem to multiple assignments between non-hub to hub nodes under fuzzy environment." JOURNAL OF MANAGEMENT AND ACCOUNTING STUDIES 3, no. 02 : 61-67, 2015.[29] Fatemeh, T., and Mahmoud V., "Green reverse supply chain management with locationrouting-inventory decisions with simultaneous pickup and delivery." Journal of Research in Science, Engineering and Technology 9, no. 02: 78-107,2021.[30] Hasani, A., Mokhtari, H., & Fattahi, M. A multi-objective optimization approach for green and resilient supply chain network design: a real-life Case Study. Journal of Cleaner Production, 278, pp. 123199, 2021.[31] Luo, L., Li, H., Wang, J., & Hu, J. Design of a combined wind speed forecasting system based on decomposition-ensemble and multi-objective optimization approach. Applied Mathematical Modelling, 89, pp. 49-72, 2021.[32] Fonseca, J. D., Commenge, J. M., Camargo, M., Falk, L., & Gil, I. D. Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach. Applied Energy, 290, 116746, 2021.[33] Mohammed, A., Naghshineh, B., Spiegler, V., & Carvalho, H. Conceptualising a supply and demand resilience methodology: A hybrid DEMATEL-TOPSIS-possibilistic multiobjective optimization approach. Computers & Industrial Engineering, 160, p. 107589, 2021.[34] Wang, C. H., & Chen, N. A multi-objective optimization approach to balancing economic efficiency and equity in accessibility to multi-use paths. Transportation, 48(4), pp. 1967-1986, 2021.[36] Ghasemi, P., & Khalili-Damghani, K. A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning. Mathematics and computers in simulation, 179, pp. 69-95, 2021.[37] Khalili-Damghani, K., Tavana, M., & Ghasemi, P. A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. 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