KaydedilenlerDerginizi ekleyin

Dergiler / Parion Akademik Bakış Dergisi / 2023 / Cilt: 2 Sayı: 2

SIMULATION APPLICATIONS IN DISASTER MANAGEMENT: A SYSTEMATIC REVIEW FOR SUPPLY CHAIN MANAGEMENT AND LOGISTICS

Çağdaş Ateş(SAKARYA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, UYGULAMALI BİLİMLER FAKÜLTESİ)0000-0002-2590-1935
Sayfa
18–34
Yayın Tarihi
2023-07-26
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In addition, it has been tried to reveal which subjects are emphasized in the studies carried out according to simulation techniques.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Disasters have severe damage to social life and economies, especially human losses.
📊
Temel Bulgular & Veri:As a result of the search made on the SCOPUS database within the scope of the research carried out to achieve the aim, 82 studies were found in the first stage.
💡
Sonuç & Katkı:In addition, it has been tried to reveal which subjects are emphasized in the studies carried out according to simulation techniques.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Disaster Management · Simulation · Supply Chain Management

Özet

Disasters have severe damage to social life and economies, especially human losses. In order to prevent these losses and damages, disaster management is highly critical. Operations such as facility location selection, stock pre-positioning, disaster mitigation, and supplying products and services to the disaster area in disaster management, which generally has two stages, pre-disaster and post-disaster, are considered supply chain management and logistics operations. In this context, publications using various simulation techniques have emerged to improve these operations. Therefore, this research aims to present simulation studies related to supply chain management and logistics activities in disaster management through a systematic literature review. As a result of the search made on the SCOPUS database within the scope of the research carried out to achieve the aim, 82 studies were found in the first stage. Only journal articles and conference proceedings were considered among these studies, and irrelevant studies were excluded by examining these publications in detail. Afterward, citation and co-occurrence analyzes were performed for these 56 publications. Finally, the publications using simulation techniques such as monte carlo, system dynamics, agent-based and discrete event used in these publications were analyzed. In addition, it has been tried to reveal which subjects are emphasized in the studies carried out according to simulation techniques.

🔖

Bu Makaleye Atıf Yap

Ateş, Ç. (2023). SIMULATION APPLICATIONS IN DISASTER MANAGEMENT: A SYSTEMATIC REVIEW FOR SUPPLY CHAIN MANAGEMENT AND LOGISTICS. Parion Akademik Bakış Dergisi, Cilt: 2 Sayı: 2, 18–34. https://ajindex.com/articles/977679

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (79)

  1. Abualkhair, H., Lodree, E. J., & Davis, L. B. (2020). Managing volunteer convergence at disaster relief centers. International Journal of Production Economics, 220, 107399. https://doi.org/10.1016/j.ijpe.2019.05.018
  2. Albino, V., Carbonara, N., & Giannoccaro, I. (2006). Innovation in industrial districts: An agent-based simulation model. International Journal of Production Economics, 104(1), 30-45. https://doi.org/10.1016/j.ijpe.2004.12.023
  3. Altay, N., & Pal, R. (2014). Information diffusion among agents: Implications for humanitarian operations. Production and Operations Management, 23(6), 1015-1027. https://doi.org/10.1111/poms.12102
  4. Amundson, J., Brown, A., Grabowski, M., & Badurdeen, F. (2014). Life-cycle risk modeling: Alternate methods using bayesian belief networks. Procedia CIRP, 17, 320-325. https://doi.org/10.1016/j.procir.2014.02.029
  5. Apte, A. (2010). Humanitarian logistics: A new field of research and action. Foundations and Trends in Technology, Information and Operations Management, 3(1), 1-100. https://doi.org/10.1561/0200000014
  6. Aros, S. K., & Gibbons, D. E. (2018). Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation. European Journal of Operational Research, 269(2), 451-465. https://doi.org/10.1016/j.ejor.2018.02.013
  7. Ataseven, C., Nair, A. and Ferguson, M. (2020), "The role of supply chain integration in strengthening the performance of not-for-profit organizations: evidence from the food banking industry", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 10 No. 2, pp. 101-123. https://doi.org/10.1108/JHLSCM-04-2019-0024
  8. Aydin, N. Y., Duzgun, H. S., Heinimann, H. R., Wenzel, F., & Gnyawali, K. R. (2018). Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction, 31, 832-843. https://doi.org/10.1016/j.ijdrr.2018.07.022
  9. Baharmand, H., Comes, T., & Lauras, M. (2020). Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters: A case study of the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 45, 101455. https://doi.org/10.1016/j.ijdrr.2019.101455
  10. Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13. https://doi.org/10.1016/j.seps.2011.04.004
  11. Chandra-Sekaran, A.K., Stefansson, G., Kunze, C., Müller-Glaser, K.D., & Weisser, P. (2009). A Range-Based Monte Carlo Patient Localization during Emergency Response to Crisis. Fifth Advanced International Conference on Telecommunications. https://doi.org/10.1109/AICT.2009.10
  12. Chandra-Sekaran, A.K., Weisser, P., Müller-Glaser, K.D., & Kunze, C. (2009). A Comparison of Bayesian Filter Based Approaches for Patient Localization during Emergency Response to Crisis. Third International Conference on Sensor Technologies and Applications. https://doi.org/10.1109/SENSORCOMM.2009.104
  13. Chen, Y., Ponsignon, T., Weixlgartner, R., & Ehm, H. (2017). Simulating recovery strategies to enhance the resilience of a semiconductor supply network. 2017 Winter Simulation Conference (WSC), 4477-4478. https://doi.org/10.1109/WSC.2017.8248170
  14. Cherkesly, M., & Maïzi, Y. (2020). A simulation model for short and long term humanitarian supply chain operations management. 2020 Winter Simulation Conference (WSC), 1360-1371. https://doi.org/10.1109/WSC48552.2020.9384003
  15. Chong, M., Lazo Lazo, J. G., Pereda, M. C., & Machuca De Pina, J. M. (2019). Goal programming optimization model under uncertainty and the critical areas characterization in humanitarian logistics management. Journal of Humanitarian Logistics and Supply Chain Management, 9(1), 82-107. https://doi.org/10.1108/JHLSCM-04-2018-0027
  16. Cohen, J., Quilenderino, J., Bubulka, J., & Paulo, E. P. (2013). Linking a throughput simulation to a systems dynamics simulation to assess the utility of a US Navy foreign humanitarian aid mission. Defense & Security Analysis, 29(2), 141-155. https://doi.org/10.1080/14751798.2013.787795
  17. Condeixa, L. D., Leiras, A., Oliveira, F., & de Brito, I. (2017). Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation. International Journal of Disaster Risk Reduction, 25, 238-247. https://doi.org/10.1016/j.ijdrr.2017.09.007
  18. Diaz, R., Behr, J. G., & Acero, B. (2022). Coastal housing recovery in a postdisaster environment: A supply chain perspective. International Journal of Production Economics, 247, 108463. https://doi.org/10.1016/j.ijpe.2022.108463
  19. Diedrichs, D.R., Phelps, K. and Isihara, P.A. (2016). Quantifying communication effects in disaster response logistics: A multiple network system dynamics model. Journal of Humanitarian Logistics and Supply Chain Management, Vol. 6 No. 1, pp. 24-45. https://doi.org/10.1108/JHLSCM-09-2014-0031
  20. D’Uffizi, A., Simonetti, M., Stecca, G., & Confessore, G. (2015). A simulation study of logistics for disaster relief operations. Procedia CIRP, 33, 157-162. https://doi.org/10.1016/j.procir.2015.06.029
  21. Dulam, R., Furuta, K., & Kanno, T. (2021). Consumer panic buying: Realizing its consequences and repercussions on the supply chain. Sustainability, 13(8), 4370. https://doi.org/10.3390/su13084370
  22. Ehlen, M. A., Sun, A. C., Pepple, M. A., Eidson, E. D., & Jones, B. S. (2014). Chemical supply chain modeling for analysis of homeland security events. Computers & Chemical Engineering, 60, 102-111. https://doi.org/10.1016/j.compchemeng.2013.07.014
  23. Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/s11192-015-1645-z
  24. Espejo-Díaz, J. A., & Guerrero, W. J. (2021). A multiagent approach to solving the dynamic postdisaster relief distribution problem. Operations Management Research, 14(1-2), 177-193. https://doi.org/10.1007/s12063-021-00192-1
  25. Fereiduni, M., & Shahanaghi, K. (2017). A robust optimization model for distribution and evacuation in the disaster response phase. Journal of Industrial Engineering International, 13(1), 117-141. https://doi.org/10.1007/s40092-016-0173-7
  26. Fikar, C., Gronalt, M., & Hirsch, P. (2016). A decision support system for coordinated disaster relief distribution. Expert Systems with Applications, 57, 104-116. https://doi.org/10.1016/j.eswa.2016.03.039
  27. Fikar, C., Hirsch, P., & Nolz, P. C. (2018). Agent-based simulation optimization for dynamic disaster relief distribution. Central European Journal of Operations Research, 26(2), 423-442. https://doi.org/10.1007/s10100-017-0518-3
  28. Giedelmann-L, N., Guerrero, W. J., & Solano-Charris, E. L. (2022). System dynamics approach for food inventory policy assessment in a humanitarian supply chain. International Journal of Disaster Risk Reduction, 81, 103286. https://doi.org/10.1016/j.ijdrr.2022.103286
  29. Green, J.L., de Weck, O.L. and Suarez, P. (2013), "Evaluating the economic sustainability of sanitation logistics in Senegal", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 3 No. 1, pp. 7-21. https://doi.org/10.1108/20426741311328484
  30. Gude, V., Corns, S., Dagli, C., & Long, S. (2020). Agent based modeling for flood inundation mapping and rerouting. Procedia Computer Science, 168, 170-176. https://doi.org/10.1016/j.procs.2020.02.279 Harrison, R. L., Granja, C., & Leroy, C. (2010). Introduction to monte carlo simulation. 17-21. https://doi.org/10.1063/1.3295638
  31. Hezam, I. M., & Nayeem, M. k. (2020). A systematic literature review on mathematical models of humanitarian logistics. Symmetry, 13(1), 11. https://doi.org/10.3390/sym13010011
  32. Inoue, H., & Todo, Y. (2019). Firm-level propagation of shocks through supply-chain networks. Nature Sustainability, 2(9), 841-847. https://doi.org/10.1038/s41893-019-0351-x
  33. Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149. https://doi.org/10.1016/j.trb.2016.09.004
  34. Jamshidieini, B., & Rezaie, K. (2016). Estimating resiliency of energy distribution supply chain by Monte Carlo simulation. CIRED Workshop 2016, 156 (4) https://doi.org/10.1049/cp.2016.0756
  35. Jin, S., Zhuang, J., & Liu, Z. (2010). Monte Carlo simulation-based supply chain disruption management for wargames. Proceedings of the 2010 Winter Simulation Conference. https://doi.org/10.1109/WSC.2010.5678964
  36. Jing, P., Hu, H., Zhan, F., Chen, Y., & Shi, Y. (2020). Agent-based simulation of autonomous vehicles: A systematic literature review. IEEE Access, 8, 79089-79103.
  37. Katsoras, E., & Georgiadis, P. (2022). A dynamic analysis for mitigating disaster effects in closed loop supply chains. Sustainability, 14(9), 4948. https://doi.org/10.3390/su14094948
  38. Katsoras, E., & Georgiadis, P. (2022). An integrated system dynamics model for closed loop supply chains under disaster effects: The case of covid-19. International Journal of Production Economics, 253, 108593. https://doi.org/10.1016/j.ijpe.2022.108593
  39. K.E.K, V., Nadeem, S. P., Ravichandran, M., Ethirajan, M., & Kandasamy, J. (2022). Resilience strategies to recover from the cascading ripple effect in a copper supply chain through project management. Operations Management Research, 15(1-2), 440-460. https://doi.org/10.1007/s12063-021-00231-x
  40. Khan, M., Parvaiz, G. S., Tohirovich Dedahanov, A., Iqbal, M., & Junghan, B. (2022). Research trends in humanitarian logistics and sustainable development: A bibliometric analysis. Cogent Business & Management, 9(1), 2143071. https://doi.org/10.1080/23311975.2022.2143071
  41. Kogler, C., & Rauch, P. (2020). Contingency plans for the wood supply chain based on bottleneck and queuing time analyses of a discrete event simulation. Forests, 11(4), 396. https://doi.org/10.3390/f11040396
  42. Kunz, N., & Reiner, G. (2012). A meta‐analysis of humanitarian logistics research. Journal of Humanitarian Logistics and Supply Chain Management, 2(2), 116-147. https://doi.org/10.1108/20426741211260723
  43. Lang, S., Reggelin, T., Müller, M., & Nahhas, A. (2021). Open-source discrete-event simulation software for applications in production and logistics: An alternative to commercial tools? Procedia Computer Science, 180, 978-987. https://doi.org/10.1016/j.procs.2021.01.349
  44. Lant, T., Jehn, M., Christensen, C., Araz, O. M., & Fowler, J. W. (2008). Simulating pandemic influenza preparedness plans for a public university: A hierarchical system dynamics approach. Proceedings of the 40th Conference on Winter Simulation, 1305-1313.
  45. Lau, H. C., Li, Z., Du, X., Jiang, H., & De Souza, R. (2012). Logistics orchestration modeling and evaluation for humanitarian relief. Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, 25-30. https://doi.org/https://doi.org/10.1109/SOLI.2012.6273499
  46. Lee, E. K., Pietz, F. H., Chen, C.-H., & Liu, Y. (2017). An interactive web-based decision support system for mass dispensing, emergency preparedness, and biosurveillance. Proceedings of the 2017 International Conference on Digital Health, 137-146. https://doi.org/10.1145/3079452.3079473
  47. Li, H., Pedrielli, G., Lee, L. H., & Chew, E. P. (2017). Enhancement of supply chain resilience through inter-echelon information sharing. Flexible Services and Manufacturing, 29(2), 260-285.
  48. Lu, J., Zhang, Q., & Fan, W. (2021). Comparison research on different mitigation strategies of supply chain under supply disruption scenarios. Journal of Physics: Conference Series, 1744(4), 042157. https://doi.org/10.1088/1742-6596/1744/4/042157
  49. Maharjan, R., & Hanaoka, S. (2018). A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response. Journal of Humanitarian Logistics and Supply Chain Management, 8(1), 2-21. https://doi.org/10.1108/JHLSCM-08-2017-0040
  50. Mansur, A., Istianah, S., Kurniawan, W., Albab, M. U., & Suryoputro, M. R. (2017). Logistics Procurement Planning of Medicine to Mitigate Flood Disaster Impact. Journal of Engineering and Applied Sciences, 12(3), 6426-6433. http://dx.doi.org/10.36478/jeasci.2017.6426.6433
  51. Martyn, J. (1975), Citation Analysis, Journal of Documentation, Vol. 31 No. 4, pp. 290-297. https://doi.org/10.1108/eb026610
  52. Min, P., & Hong, C. (2011). System dynamics analysis for the impact of dynamic transport and information delay to disaster relief supplies. International Conference on Management Science & Engineering 18th Annual Conference Proceedings. https://doi.org/10.1109/ICMSE.2011.6069948
  53. Mustapha, K., Mcheick, H., & Mellouli, S. (2013). Modeling and simulation agent-based of natural disaster complex systems. Procedia Computer Science, 21, 148-155. https://doi.org/10.1016/j.procs.2013.09.021
  54. Namany, S., Govindan, R., Alfagih, L., McKay, G., & Al-Ansari, T. (2020). Sustainable food security decision-making: An agent-based modelling approach. Journal of Cleaner Production, 255, 120296. https://doi.org/10.1016/j.jclepro.2020.120296
  55. Noreña, D., Yamín, L., Akhavan-Tabatabaei, R., & Ospina, W. (2011). Using discrete event simulation to evaluate the logistics of medical attention during the relief operations in an earthquake in Bogota. In Proceedings of the Winter Simulation Conference (pp. 2666–2678).
  56. Oliver, E., Mazzuchi, T., & Sarkani, S. (2022). A resilience systemic model for assessing critical supply chain disruptions. Systems Engineering, 25(5), 510-533. https://doi.org/10.1002/sys.21633
  57. Oloruntoba, R., Sridharan, R., & Davison, G. (2018). A proposed framework of key activities and processes in the preparedness and recovery phases of disaster management. Disasters, 42(3), 541-570. https://doi.org/10.1111/disa.12268
  58. Ottenburger, S., & Bai, S. (2017). Simulation based strategic decision making in humanitarian supply chain management. 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 1-7. https://doi.org/10.1109/ICT-DM.2017.8275683
  59. Özpolat, K., Rilling, J., Altay, N. and Chavez, E. (2015), "Engaging donors in smart compassion: USAID CIDI’s Greatest Good Donation Calculator", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 5 No. 1, pp. 95-112. https://doi.org/10.1108/JHLSCM-11-2013-0041
  60. Paciarotti, C., Piotrowicz, W.D. and Fenton, G. (2021), "Humanitarian logistics and supply chain standards. Literature review and view from practice", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 11 No. 3, pp. 550-573. https://doi.org/10.1108/JHLSCM-11-2020-0101
  61. Peng, M., Chen, H., & Zhou, M. (2014). Modelling and simulating the dynamic environmental factors in post-seismic relief operation. Journal of Simulation, 8(2), 164-178. https://doi.org/10.1057/jos.2013.27
  62. Peng, M., Peng, Y., & Chen, H. (2014). Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning. Computers & Operations Research, 42, 14-24. https://doi.org/10.1016/j.cor.2013.03.003
  63. Qiu, Y., Shi, M., Zhao, X., & Jing, Y. (2021). System dynamics mechanism of cross-regional collaborative dispatch of emergency supplies based on multi-agent game. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00303-2
  64. Rojas Trejos, C. A., Meisel, J. D., & Adarme Jaimes, W. (2022). Humanitarian aid distribution logistics with accessibility constraints: A systematic literature review. Journal of Humanitarian Logistics and Supply Chain Management. https://doi.org/10.1108/JHLSCM-05-2021-0041
  65. Saffarian, M., Barzinpour, F., & Eghbali, M. A. (2015). A robust programming approach to bi-objective optimization model in the disaster relief logistics response phase. International Journal of Supply and Operations Management, 2(1), 595-616. https://doi.org/10.22034/2015.1.04
  66. Salam, M. A., & Khan, S. A. (2020). Lessons from the humanitarian disaster logistics management: A case study of the earthquake in Haiti. Benchmarking: An International Journal, 27(4), 1455-1473. https://doi.org/10.1108/BIJ-04-2019-0165
  67. Seipp, B., Budhraja, K., & Oates, T. (2018). Optimizing transitions between abstract abm demonstrations. 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 100-109. https://doi.org/10.1109/SASO.2018.00021
  68. Shirley, T., Padmanabha, B., Murray-Tuite, P., Huynh, N., Comert, G., & Shen, J. (2021). Exploring the potential of using privately-owned, self-driving autonomous vehicles for evacuation assistance. Journal of Advanced Transportation, 2021, e2156964. https://doi.org/10.1155/2021/2156964
  69. Siebers, P. O., Macal, C. M., Garnett, J., Buxton, D., & Pidd, M. (2010). Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation, 4(3), 204-210. https://doi.org/10.1057/jos.2010.14
  70. Statista, the statistics portal, Retrieved November 7, 2022, Annual number of natural disaster events globally from 2007 to 2021, by continent and type: https://www.statista.com/statistics/510959/number-of-natural-disasters-events-globally/
  71. Suárez-Moreno, J. D., Osorio-Ramírez, C., & Adarme-Jaimes, W. (2016). Agent-based model for material convergence in humanitarian logistics. Revista Facultad de Ingeniería Universidad de Antioquia, 81, 24-34. https://doi.org/10.17533/udea.redin.n81a03
  72. Tako, A.A., Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decisions Support Systems, 52 (4), 802–815.
  73. Timperio, G., Tiwari, S., Lee, C. K., Samvedi, A., & de Souza, R. (2020). Integrated decision support framework for enhancing disaster preparedness: A pilot application in Indonesia. International Journal of Disaster Risk Reduction, 51, 101773. https://doi.org/10.1016/j.ijdrr.2020.101773
  74. Tong, Z., Zhang, Q., Zhu, J., Liu, X., & Yan, F. (2017). A stochastic distribution center location model for earthquake relief supplies based on monte carlo simulation. The Open Cybernetics & Systemics Journal, 11(1). https://doi.org/10.2174/1874110X01711010024
  75. Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
  76. Wang, X., & Huang, X. (2010). Research on Efficiency of Disaster Supply Chain Based on Collaborative Management. 3rd International Conference on Information Management, Innovation Management and Industrial Engineering. https://doi.org/10.1109/ICIII.2010.537
  77. Wang, Z., & Zhang, J. (2019). Agent-based evaluation of humanitarian relief goods supply capability. International Journal of Disaster Risk Reduction, 36, 101105. https://doi.org/10.1016/j.ijdrr.2019.101105
  78. Xu, Y., Dong, J., Ren, R., Yang, K., & Chen, Z. (2022). The impact of metro-based underground logistics system on city logistics performance under COVID-19 epidemic: A case study of Wuhan, China. Transport Policy, 116, 81-95. https://doi.org/10.1016/j.tranpol.2021.10.020
  79. Yang, T. (2008). Financial performance analysis of e-collaboration supply chain under transportation disruptions. 2008 International Symposium on Computer Science and Computational Technology, 2, 435-439. https://doi.org/10.1109/ISCSCT.2008.372