×

Two phase algorithm for bi-objective relief distribution location problem. (English) Zbl 07856489

Summary: The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods.

MSC:

90B06 Transportation, logistics and supply chain management
90B80 Discrete location and assignment
90C29 Multi-objective and goal programming
Full Text: DOI

References:

[1] Abazari, SR; Aghsami, A.; Rabbani, M., Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters, Socio-Economic Planning Sciences, 74, 2021 · doi:10.1016/j.seps.2020.100933
[2] Banomyong, R.; Varadejsatitwong, P.; Oloruntoba, R., A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016, Annals of Operations Research, 283, 1, 71-86, 2019 · doi:10.1007/s10479-017-2549-5
[3] Behl, A.; Dutta, P., Humanitarian supply chain management: A thematic literature review and future directions of research, Annals of Operations Research, 283, 1, 1001-1044, 2019 · doi:10.1007/s10479-018-2806-2
[4] Boonmee, C.; Arimura, M.; Asada, T., Facility location optimization model for emergency humanitarian logistics, International Journal of Disaster Risk Reduction, 24, 485-498, 2017 · doi:10.1016/j.ijdrr.2017.01.017
[5] Breman, J., The pandemic in India and its impact on footloose labour, The Indian Journal of Labour Economics, 63, 4, 901-919, 2020 · doi:10.1007/s41027-020-00285-8
[6] Burkart, C.; Nolz, PC; Gutjahr, WJ, Modelling beneficiaries’ choice in disaster relief logistics, Annals of Operations Research, 256, 1, 41-61, 2017 · Zbl 1380.90241 · doi:10.1007/s10479-015-2097-9
[7] Chowdhury, P.; Paul, SK; Kaisar, S.; Moktadir, MA, COVID-19 pandemic related supply chain studies: A systematic review, Transportation Research Part e: Logistics and Transportation Review, 2021 · doi:10.1016/j.tre.2021.102271
[8] Cui, T.; Ouyang, Y.; Shen, ZJM, Reliable facility location design under the risk of disruptions, Operations Research, 58, 4-1, 998-1011, 2010 · Zbl 1231.90266 · doi:10.1287/opre.1090.0801
[9] Deb, K., An introduction to genetic algorithms, Sadhana, 24, 4-5, 293-315, 1999 · Zbl 1075.90565 · doi:10.1007/BF02823145
[10] Devi, Y., Patra, S., & Singh, S. P. (2021). A location-allocation model for influenza pandemic outbreaks: A case study in India. Operations Management Research, 1-16.
[11] Dönmez, Z.; Kara, BY; Karsu, Ö.; Saldanha-da-Gama, F., Humanitarian facility location under uncertainty: Critical review and future prospects, Omega, 2021 · doi:10.1016/j.omega.2021.102393
[12] Dubey, R.; Gunasekaran, A.; Childe, SJ; Roubaud, D.; Wamba, SF; Giannakis, M.; Foropon, C., Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain, International Journal of Production Economics, 210, 120-136, 2019 · doi:10.1016/j.ijpe.2019.01.023
[13] Dubey, R.; Gunasekaran, A.; Papadopoulos, T., Disaster relief operations: Past, present and future, Annals of Operations Research, 283, 1, 1-8, 2019 · Zbl 1433.90008 · doi:10.1007/s10479-019-03440-7
[14] Dubey, R.; Gunasekaran, A.; Bryde, DJ; Dwivedi, YK; Papadopoulos, T., Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting, International Journal of Production Research, 58, 11, 3381-3398, 2020 · doi:10.1080/00207543.2020.1722860
[15] Hu, S.; Dong, ZS, Supplier selection and pre-positioning strategy in humanitarian relief, Omega, 83, 287-298, 2019 · doi:10.1016/j.omega.2018.10.011
[16] Duhamel, C.; Santos, AC; Brasil, D.; Châtelet, E.; Birregah, B., Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations, Annals of Operations Research, 247, 2, 693-713, 2016 · Zbl 1360.90138 · doi:10.1007/s10479-015-2104-1
[17] Elluru, S.; Gupta, H.; Kaur, H.; Singh, SP, Proactive and reactive models for disaster resilient supply chain, Annals of Operations Research, 283, 1, 199-224, 2019 · doi:10.1007/s10479-017-2681-2
[18] Farrokhizadeh, E., Seyfi-Shishavan, S. A., & Satoglu, S. I. (2021). Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent. Annals of Operations Research, 1-41. · Zbl 1509.90182
[19] Ghavamifar, A.; Makui, A.; Taleizadeh, AA, Designing a resilient competitive supply chain network under disruption risks: A real-world application, Transportation Research Part e: Logistics and Transportation Review, 115, 87-109, 2018 · doi:10.1016/j.tre.2018.04.014
[20] Ghorbanzadeh, M.; Kim, K.; Ozguven, EE; Horner, MW, Spatial accessibility assessment of COVID-19 patients to healthcare facilities: A case study of Florida, Travel Behaviour and Society, 24, 95-101, 2021 · doi:10.1016/j.tbs.2021.03.004
[21] Goldschmidt, KH; Kumar, S., Reducing the cost of humanitarian operations through disaster preparation and preparedness, Annals of Operations Research, 283, 1, 1139-1152, 2019 · doi:10.1007/s10479-017-2587-z
[22] Gösling, H.; Geldermann, J., A framework to compare OR models for humanitarian logistics, Procedia Engineering, 78, 22-28, 2014 · doi:10.1016/j.proeng.2014.07.034
[23] Guo, N.; Yang, Z.; Wang, L.; Ouyang, Y.; Zhang, X., Dynamic model updating based on strain mode shape and natural frequency using hybrid pattern search technique, Journal of Sound and Vibration, 422, 112-130, 2018 · doi:10.1016/j.jsv.2018.02.013
[24] Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2021). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 1-60.
[25] Gutjahr, WJ; Dzubur, N., Bi-objective bilevel optimization of distribution center locations considering user equilibria, Transportation Research Part e: Logistics and Transportation Review, 85, 1-22, 2016 · doi:10.1016/j.tre.2015.11.001
[26] Habib, MS; Lee, YH; Memon, MS, Mathematical models in humanitarian supply chain management: A systematic literature review, Mathematical Problems in Engineering, 2016 · doi:10.1155/2016/3212095
[27] Ivanov, D. (2021a). Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains. Annals of Operations Research, 1-18.
[28] Ivanov, D., Introduction to supply chain resilience: management, modelling, technology, 2021, New York: Springer, New York · doi:10.1007/978-3-030-70490-2
[29] Ivanov, D.; Dolgui, A., OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications, International Journal of Production Economics, 232, 2021 · doi:10.1016/j.ijpe.2020.107921
[30] Jia, H.; Ordonez, F.; Dessouky, MM, Solution approaches for facility location of medical supplies for large-scale emergencies, Computers & Industrial Engineering, 52, 2, 257-276, 2007 · doi:10.1016/j.cie.2006.12.007
[31] Jha, P. K., Ghorai, S., Jha, R., Datt, R., Sulapu, G., & Singh, S. P. (2021). Forecasting the impact of epidemic outbreaks on the supply chain: modelling asymptomatic cases of the COVID-19 pandemic. International Journal of Production Research, 1-26.
[32] Kaur, H.; Singh, SP, Sustainable procurement and logistics for disaster resilient supply chain, Annals of Operations Research, 283, 1, 309-354, 2019 · doi:10.1007/s10479-016-2374-2
[33] Kharroubi, S.; Saleh, F., Are lockdown measures effective against COVID-19?, Frontiers in Public Health, 8, 610, 2020 · doi:10.3389/fpubh.2020.549692
[34] Kınay, ÖB; Saldanha-da-Gama, F.; Kara, BY, On multi-criteria chance-constrained capacitated single-source discrete facility location problems, Omega, 83, 107-122, 2019 · doi:10.1016/j.omega.2018.02.007
[35] Lai, MC; Sohn, HS; Tseng, TLB; Chiang, C., A hybrid algorithm for capacitated plant location problem, Expert Systems with Applications, 37, 12, 8599-8605, 2010 · doi:10.1016/j.eswa.2010.06.104
[36] Li, S.; Teo, KL, Post-disaster multi-period road network repair: Work scheduling and relief logistics optimization, Annals of Operations Research, 283, 1, 1345-1385, 2019 · doi:10.1007/s10479-018-3037-2
[37] Liu, Y.; Cui, N.; Zhang, J., Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service, Transportation Research Part e: Logistics and Transportation Review, 128, 1-16, 2019 · doi:10.1016/j.tre.2019.05.008
[38] Maghfiroh, MF; Hanaoka, S., Multi-modal relief distribution model for disaster response operations, Progress in Disaster Science, 6, 2020 · doi:10.1016/j.pdisas.2020.100095
[39] Manopiniwes, W.; Irohara, T., Stochastic optimization model for integrated decisions on relief supply chains: Preparedness for disaster response, International Journal of Production Research, 55, 4, 979-996, 2017 · doi:10.1080/00207543.2016.1211340
[40] MirHassani, SA; Raeisi, S.; Rahmani, A., Quantum binary particle swarm optimization-based algorithm for solving a class of bi-level competitive facility location problems, Optimization Methods and Software, 30, 756-768, 2015 · Zbl 1336.90060 · doi:10.1080/10556788.2014.973875
[41] Modgil, S., Singh, R. K., & Foropon, C. (2020). Quality management in humanitarian operations and disaster relief management: A review and future research directions. Annals of operations research, 1-54.
[42] Mohammadi, S.; Darestani, SA; Vahdani, B.; Alinezhad, A., A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns, Computers & Industrial Engineering, 148, 2020 · doi:10.1016/j.cie.2020.106734
[43] Mondal, T.; Boral, N.; Bhattacharya, I.; Das, J.; Pramanik, P., Distribution of deficient resources in disaster response situation using particle swarm optimization, International Journal of Disaster Risk Reduction, 41, 2019 · doi:10.1016/j.ijdrr.2019.101308
[44] Muggy, L.; Stamm, JLH, Dynamic, robust models to quantify the impact of decentralization in post-disaster health care facility location decisions, Operations Research for Health Care, 12, 43-59, 2017 · doi:10.1016/j.orhc.2017.01.002
[45] Munyaka, JCB; Yadavalli, VSS, Decision support framework for facility location and demand planning for humanitarian logistics, International Journal of System Assurance Engineering and Management, 12, 1, 9-28, 2021
[46] Nagurney, A., Supply chain game theory network modeling under labor constraints: Applications to the Covid-19 pandemic, European Journal of Operational Research, 293, 3, 880-891, 2021 · Zbl 1487.90152 · doi:10.1016/j.ejor.2020.12.054
[47] Najafi, M.; Farahani, RZ; De Brito, MP; Dullaert, W., Location and distribution management of relief centers: A genetic algorithm approach, International Journal of Information Technology & Decision Making, 14, 4, 769-803, 2015 · doi:10.1142/S0219622014500382
[48] Oksuz, MK; Satoglu, SI, A two-stage stochastic model for location planning of temporary medical centers for disaster response, International Journal of Disaster Risk Reduction, 44, 2020 · doi:10.1016/j.ijdrr.2019.101426
[49] Özdamar, L.; Ertem, MA, Models, solutions and enabling technologies in humanitarian logistics, European Journal of Operational Research, 244, 1, 55-65, 2015 · Zbl 1346.90166 · doi:10.1016/j.ejor.2014.11.030
[50] Paul, NR; Lunday, BJ; Nurre, SG, A multiobjective, maximal conditional covering location problem applied to the relocation of hierarchical emergency response facilities, Omega, 66, 147-158, 2017 · doi:10.1016/j.omega.2016.02.006
[51] Plastria, F.; Vanhaverbeke, L., Aggregation without loss of optimality in competitive location models, Networks and Spatial Economics, 7, 3-18, 2007 · Zbl 1137.91337 · doi:10.1007/s11067-006-9004-5
[52] Praneetpholkrang, P.; Huynh, VN; Kanjanawattana, S., A multi-objective optimization model for shelter location-allocation in response to humanitarian relief logistics, The Asian Journal of Shipping and Logistics, 37, 2, 149-156, 2021 · doi:10.1016/j.ajsl.2021.01.003
[53] Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of operations research, 1-38.
[54] Ramshani, M.; Ostrowski, J.; Zhang, K.; Li, X., Two level uncapacitated facility location problem with disruptions, Computers & Industrial Engineering, 137, 2019 · doi:10.1016/j.cie.2019.106089
[55] Sanci, E.; Daskin, MS, An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief, Transportation Research Part b: Methodological, 145, 152-184, 2021 · doi:10.1016/j.trb.2021.01.005
[56] Sawik, T., Supply chain disruption management, 2020, Berlin: Springer, Berlin · Zbl 1484.90002 · doi:10.1007/978-3-030-44814-1
[57] Sharma, B.; Ramkumar, M.; Subramanian, N.; Malhotra, B., Dynamic temporary blood facility location-allocation during and post-disaster periods, Annals of Operations Research, 283, 1, 705-736, 2019 · doi:10.1007/s10479-017-2680-3
[58] Singh, S.; Kumar, R.; Panchal, R.; Tiwari, MK, Impact of COVID-19 on logistics systems and disruptions in food supply chain, International Journal of Production Research, 59, 7, 1993-2008, 2021 · doi:10.1080/00207543.2020.1792000
[59] Sun, H.; Wang, Y.; Xue, Y., A bi-objective robust optimization model for disaster response planning under uncertainties, Computers & Industrial Engineering, 155, 2021 · doi:10.1016/j.cie.2021.107213
[60] Tayal, A.; Singh, SP, Formulating multi-objective stochastic dynamic facility layout problem for disaster relief, Annals of Operations Research, 283, 1, 837-863, 2019 · doi:10.1007/s10479-017-2592-2
[61] Thomas, AS; Kopczak, LR, From logistics to supply chain management: The path forward in the humanitarian sector, Fritz Institute, 15, 1, 1-15, 2005
[62] Vahdani, B.; Veysmoradi, D.; Noori, F.; Mansour, F., Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty, International Journal of Disaster Risk Reduction, 27, 290-306, 2018 · doi:10.1016/j.ijdrr.2017.10.015
[63] Wang, X.; Ouyang, Y., A continuum approximation approach to competitive facility location design under facility disruption risks, Transportation Research Part B: Methodological, 50, 90-103, 2013 · doi:10.1016/j.trb.2012.12.004
[64] Wei, X.; Qiu, H.; Wang, D.; Duan, J.; Wang, Y.; Cheng, TCE, An integrated location-routing problem with post-disaster relief distribution, Computers & Industrial Engineering, 147, 2020 · doi:10.1016/j.cie.2020.106632
[65] Yahyaei, M.; Bozorgi-Amiri, A., Robust reliable humanitarian relief network design: An integration of shelter and supply facility location, Annals of Operations Research, 283, 1, 897-916, 2019 · doi:10.1007/s10479-018-2758-6
[66] Yáñez-Sandivari, L.; Cortés, CE; Rey, PA, Humanitarian Logistics and Emergencies Management: New perspectives to a sociotechnical problem and its optimization approach management, International Journal of Disaster Risk Reduction, 2020 · doi:10.1016/j.ijdrr.2020.101952
[67] Yegane, BY; Kamalabadi, IN; Farughi, H., A non-linear integer bi-level programming model for competitive facility location of distribution centers, International Journal of Engineering-Transactions b: Applications, 29, 1131-1140, 2016
[68] Zhang, B.; Peng, J.; Li, S., Covering location problem of emergency service facilities in an uncertain environment, Applied Mathematical Modelling, 51, 429-447, 2017 · Zbl 1480.90168 · doi:10.1016/j.apm.2017.06.043
[69] Zhen, L.; Wang, K.; Liu, HC, Disaster relief facility network design in metropolises, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45, 5, 751-761, 2014 · doi:10.1109/TSMC.2014.2364550
[70] Zhong, S.; Cheng, R.; Jiang, Y.; Wang, Z.; Larsen, A.; Nielsen, OA, Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand, Transportation Research Part e: Logistics and Transportation Review, 141, 2020 · doi:10.1016/j.tre.2020.102015
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.