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Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
177--192
Opis fizyczny
Bibliogr. 37 poz., rys., tab., wykr.
Twórcy
autor
  • DS & CI Research Group Universitas Medan Area, Medan, Indonesia
  • DS & CI Research Group Universitas, Sumatera Utara, Medan
  • Department Information Technology Communication and Intellectual Property. Faculty of Law, Universitas Padjadjaran, Jawa Barat, Indonesia
  • Departamento de Energía, Universidad de la Costa, Barranquilla, Colombia
  • Radiology department, Hilla university college, Babylon, Iraq
  • RN Faculty of Nursing, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy Bangkok, Thailand
  • Department of Pharmacology, Saveetha dental College and hospital, Saveetha institute of medical and technical sciences, Chennai, India
  • Altai State University, Doctor of Economics, Professor of management, business organization and innovation, Department Barnaul, Russian Federation
  • Department of Information Technology, College of Computing and Informatics, Saudi Electronic University Saudi Arabia
Bibliografia
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  • [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 Queue-based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration. IEEE Transactions on Circuits and Systems for Video Technology, 2020.
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  • [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.
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  • [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 meta-heuristics. Applied Mathematical Modelling, 40(7), pp. 4948-4969, 2016.
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  • [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 meta-heuristic 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 location-routing-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 multi-objective 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.
Uwagi
PL
Bibliografia zgodna z oryginałem – brakuje jednego opisu bibliograficznego (poz. 35).
Typ dokumentu
Bibliografia
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