طراحی مدل تاب‌آوری سیستم توزیع فرآورده‌های نفتی با رویکرد شبیه‌سازی عامل‌بنیان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی و تکنولوزی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران

2 استادیار، گروه مدیریت صنعتی و تکنولوزی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران

3 دانشیار، گروه مدیریت صنعتی و تکنولوزی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران

4 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

5 دکتری مهندسی شیمی، دانشکده علم و صنعت ایران، مدیریت تأمین و توزیع، شرکت ملی پخش فرآورده‌های نفتی ایران، تهران، ایران.

چکیده

سیستم توزیع فرآورده‌های نفتی به‌عنوان آخرین بخش از زنجیره تأمین نفت و حلقه اتصال به بازار مصرف سوخت است. اهمیت تاب‌آور بودن آن به‌دلیل گسترده بودن این زنجیره، عدم‌قطعیت بازار، ماهیت خطرآفرینی و اشتعال‌زائی و نقش حیاتی آن در جریان جابجایی بار و مسافر در سطح کشور می‌باشد. در این پژوهش مدل‌سازی تاب‌آوری با نرم‌افزار شی‌ء‌گرای انی‌لاجیک و ترکیب شبیه‌سازی عامل‌بنیان و سیستم اطلاعات جغرافیائی(GIS) صورت گرفته و 4 عامل جایگاه سوخت، انبار اصلی، انبار جایگزین و ناوگان حمل و نقل تعریف شد. شناخت فرایند توزیع و جمع‌آوری داده‌های واقعی در شرکت ملی پخش فرآورده‌های نفتی ایران در منطقه قم صورت گرفت. با مرور ادبیات و استفاده از نظرات متخصصین صنعت نفت، شاخص‌های سنجش تاب‌‌آوری تدوین شد؛ الگوی تقاضا و جریان ورود به انبار توسط تابع چگالی احتمالی مثلثی مدل‌سازی شده و روابط و رفتار بین عامل‌ها با زبان برنامه‌نویسی جاوا تعریف شده است. سه سناریو کاهش تعداد ناوگان حمل و نقل، کاهش ظرفیت انبار اصلی و زمان دریافت سفارش جهت سنجش اثر اختلال بر تاب‌آوری سیستم، تعریف و نتایج آن با شرایط واقعی مقایسه شده است. نتایج تحقیق نشان می‌دهد با کاهش 4 برابری زمان سفارش نسبت به میزان موجودی جایگاه‌های سوخت، میزان فروش از دست رفته بیش از 9 برابر افزایش یافته است. . در حقیقت سناریوی سوم بسیار چشمگیرتر از دو سناریوی دیگر در اثرگذاری بر تاب‌آوری بوده و اهمیت زمان دریافت سفارش نسبت به میزان موجودی جایگاه سوخت، در افزایش تاب‌آوری سیستم توزیع را نشان می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Designing a Resilience Model for Petroleum Products Distribution System by Agent-Based Simulation Approach

نویسندگان [English]

  • fahime norouzzade 1
  • Meisam Shahbazi 2
  • tooraj karimi 3
  • Adel Azar 4
  • samira farzam 5
1 PhD student, Department of Industrial and Technological Management, Faculty of Management and Accounting, Farabi School, University of Tehran, Qom, Iran
2 Assistant Professor, Department of Industrial and Technological Management, Faculty of Management and Accounting, Farabi School, University of Tehran, Qom, Iran
3 Associate Professor, Department of Industrial and Technological Management, Faculty of Management and Accounting, Farabi School, University of Tehran, Qom, Iran
4 Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
5 PhD in Chemical Engineering, Iran Faculty of Science and Technology, Supply and Distribution Management, Iran National Oil Products Distribution Company, Tehran, Iran.
چکیده [English]

The petroleum products distribution system is the last part of oil supply chain and link in the oil consumption market. Due to this huge supply chain, market uncertainty, the high-risk and ignition nature of oil products and its vital role in the transportation of cargo and passengers, the resilience of this system is remarkable. In this research, resilience is modeled by object-oriented software (Any-Logic) and with a combination of agent-based modeling and GIS. Four agents were defined, which are gas station, main storage, alternative storage tanks and oil tankers. Identification of the distribution process and real data collection has been done in N.I.O.P.D.C in Qom region. By reviewing the existing literature and the oil industry experts’ opinions, resilience measurement indicators have been developed and the demand pattern and storage flow rate have been modeled with a triangular probability density function and the behavior of agents has been defined in the Java programming language. Three scenarios are defined to investigate the effects of disruptions on the system resilience: reducing the number of trucks, the capacity of the main storage and the time to receive orders. The results show that by decreasing the order time by 4 times, the lost sales have increased more than 9 times. In fact, the third scenario is much more effective on resilience than the other two scenarios. This shows the importance of ordering time relative to inventory of gas station in increasing the resilience of the distribution system.

کلیدواژه‌ها [English]

  • Resilience
  • Distribution System
  • Oil Products
  • Multi-agent Simulation
  • Geographic Information System (GIS)

مراجع

[1]    Alfaqiri, A., Hossain, N.U.I., Jaradat, R., Abutabenjeh, S., Keating, C.B., Khasawneh, M.T. and Pinto, C.A.  ‘A systemic approach for disruption risk assessment in oil and gas supply chains’, Int. J. Critical Infrastructures,  . (2019) ,Vol. 15, No. 3, pp.230–259.     
[2]    Al-Othman, W. B. E., & Lababidi, H. M. S. Alatiqi, I.M & Al-Shayji, Kh. “Supply chain optimization of petroleum organization under uncertainty in market demands and prices”, (2008). 189, 822–840. https://doi.org/10.1016/j.ejor.2006.06.081.          
[3]    Azar, A., Shahbazi, M., Yazdani, H. & Mahmoodian, O., "Assessing the supply chain resilience of the electricity industry in Iran: a fuzzy approach", Quarterly Journal of Energy Policy and Planning Research, Fifth Year, (2019). No. pp. 7-28.
[4]    Baqerzadeh A., M., Jafarnejad, A. "Designing a Conceptual Model of Supply Chain Resilience of the National Iranian Oil Company", PhD Thesis in Industrial Management, University of Tehran, Faculty of Management, (2017).        
[5]    Blos  M. F. & Miyagi P. E. "Modeling the supply Chain Disruptions : A Study based on the Supply Chain Interdependencies", (2015). https://doi.org/10.1016/j.ifacol.2015.06.391.   
[6]    Borschev A. "Introduction of agent-based simulation models", translated by Azimi, p. Pouroziri, H., Ghanbari, M.R., Sahraei, J & Mohammadian, M. (2018). Islamic Azad University Printing and Publishing Organization. (2013). Second Edition. 
[7]    Cardoso, S. R., Barbosa-póvoa, A. P., Relvas, S., & Novais, A. Q. “Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty”,  Omega, . (2015). 56, 53–73. https://doi.org/10.1016/j.omega.2015.03.008    
[8]    Carvalho, H. Azevedo, S. G. and Cruz Machado, V. “Agile and resilient approaches to supply chain management: Influence on performance and competitiveness”,  Logistics Research, (2012). 4, pp. 49–62. [51]. 
[9]    Chen, X., & Ong, Y. S."Agent-Based Modeling and Simulation for Supply Chain Risk Management – A Survey of the State-of"  (2013). (May 2014). https://doi.org/10.1109/SMC.2013.224.     
[10]    Christopher, M & Peck H. "Building the Resilient Supply Chain", International Journal ofLogisticsManagement, .(2004). Vol. 15, No. 2, pp1-13, https://doi.org/10.1108/09574090410700275.
[11]    Chopra, S. and Meindl, P. “Supply Chain Management - Strategy, Planning, and Operation” third Ed. Prentice Hall,  (2007), Upper Saddle River, NJ, USA      
[12]    Datta Partha Priya, Christopher Martin & Allen Peter , “Agent-based modelling of complex production/distribution systems to improve resilience” International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, (2007).  10:3, 187-203. http://dx.doi.org/10.1080/13675560701467144.
[13]    Djennas. M and Benbouziane M & Djennas M." Agent-Based Modeling in Supply Chain Management : A Genetic Algorithm and Fuzzy Logic Approach", International Journal of Artificial Intelligence & Applications (IJAIA), (2012), Vol.3, No.5.    
[14]    Elleuch, H.  Dafaoui. E.  Elmhamedi . A Chabchoub. H.. "Resilience and Vulnerability in Supply Chain : Literature revie,. IFAC-PapersOnLine, (2016). 49(12), 1448–1453. https://doi.org/10.1016/j.ifacol.2016.07.775.    
[15]    Emenike, S. N., & Falcone, G. "A review on energy supply chain resilience through optimization". Renewable and Sustainable Energy Reviews, (2020). 134(September), 110088. https://doi.org/10.1016/j.rser.2020.110088.    
[16]    Fernandes José, L., Relvas, S., & Barbosa-póvoa, A. P. "Strategic network design of downstream petroleum supply chains : Single versus multi-entity participation" Chemical Engineering Research and Design, (2013), 91(8), 1557–1587. https://doi.org/10.1016/j.cherd.2013.05.028.   
[17]    Gallopin, G.C." Linkages between vulnerability, resilience, and adaptive capacity" Global Environmental Change (2006) 16.293-303.   
[18]    Ghavamifar, A., Makui, A., & Taleizadeh Allah, A. "Designing a resilient competitive supply chain network under disruption risks : A real-world application". Transportation Research Part E, (2018). 115(April 2017), 87–109. https://doi.org/10.1016/j.tre.2018.04.014.   
[19]    Gibson. C & Tarrant. A, "A ‘conceptual models’ approach to organizational resilience."The Australian Journal of Emergency Management, (2010).Volume 25, No. 02.  
[20]    Gill A. " A supply chain design approach to petroleum distribution". Int Bus Res Manag. (2011). 2:(1)-33-44.    
[21]    Ghatee, M., & Hashemi, S. M. " Optimal network design and storage management in petroleum distribution network under uncertainty"Engineering Applications of Artificial Intelligence, (2009). 22(4–5), 796–807. https://doi.org/10.1016/j.engappai.2009.01.003.   
[22]    Grigorvev I., "Introduction to modeling with anylogic * 8 simulation". Translated by Izadbakhsh, H., Shayani Mehr, P., Heshmati, M., Cheraghali, M. & Parvin, M. (2019). Tehran: Kharazmi University Press. (2015), first edition.
[23]    Gunasekaran, A., Subramanian, N., & Rahman, S. "Supply chain resilience : role of complexities and strategies", International Journal of Production Research, (2015). 7543, 0. https://doi.org/10.1080/00207543.2015.1093667.   
[24]    Heidari Noghabi H., Ataei, M. and Khaloo Kakai, R. "Modeling and simulation of transportation system reliability (Case study: Zarmehr Torbat Heydariyeh gold mine)." Master Thesis. Mining Engineering Group. School of Mining Engineering, Geophysics and Petroleum. Shahroud University, (2015).      
[25]    Ho, W., Zheng, T., Yildiz, H., & Talluri, S. "Supply chain risk management : a literature review", International Journal of Production Research (2015), 37–41. https://doi.org/10.1080/00207543.2015.1030467.    
[26]    Holling, C.S. "Resilience and stability of ecological systems. Annual Review of Ecology and Systematics", )1973( . 4, 1–23.    
[27]    Hollnagel, E., Paries, J., Woods, D., Wreathall, J. "Resilience Engineering in Practice: A Guidebook. Prologue: the scope of resilience engineering', (2011), https://books.google.com.  
[28]    Iakovou, E., Vlachos, D. and Xanthopoulos, A. ‘an analytical methodological framework for the optimal design of resilient supply chains’. (2007). Int. J. Logistics Economics and Globalisation, Vol. 1, No. 1, pp.1–20.
[29]    Jabbarzadeh, A., Fahimnia, B., & Sheu, J. "Designing a supply chain resilient to major disruptions and supply / demand interruptions'.  Transportation Research Part B, (2016). 94, 121–149. https://doi.org/10.1016/j.trb.2016.09.004.   
[30]    Jafarnjad Chagoshi Ahmad, Alia Kazemi and Alireza Arab. "Identification and prioritization of customer resilience evaluation indicators based on the best-worst method". (2015) Industrial Management Perspective, No. 23, pp. 159-186.
[31]    Karimi, T., & Shahbazi, M., "Theory of gray systems and its applications", Negah Danesh Publications. (2018), first edition.
[32]    Kazemi, Y., & Szmerekovsky, J. "Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning. Transportation Research " (2015). Part E: Logistics and Transportation Review, 83, 111–125. https://doi.org/10.1016/j.tre.2015.09.004.    
[33]    Khanzadi, M., Nasirzadeh, F., Mir, M. and Nojedehi, P.  "Prediction and improvement of labor productivity using hybrid system dynamics and agent-based modeling approach", Construction Innovation, (2019), Vol. 18 No. 1, pp. 2-19. https://doi.org/10.1108/CI-06-2015-0034.    
[34]    Lima, C., Relvas, S. & Barbosa-Póvoa, A. P. F. D. "Downstream oil supply chain management: A critical review and future directions", Computers and Chemical Engineering, (2016). 92, 78–92. https://doi.org/10.1016/j.compchemeng.2016.05.002.   
[35]    Marcellino, F. J. M., & Sichman, J. S." Oil Industry Supply Chain Management as a Holonic Agent Based Distributed Constraint Optimization Problem. (2010). https://www.semanticscholar.org/paper.   
[36]    Macal C.M & North M,J. "agent-based modeling & simulation", Proceedings of the 2009 Winter Simulation Conference M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls,  (2009).eds. 978-1-4244-5771-7/09/$26.00 ©2009 IEEE.   
[37]    Macal C,M. "Everything you need to know about agent-based modeling and simulation", Journal of Simulation (2016) 10, 144–156 © 2016 Operational Research Society Ltd. All rights reserved. 1747-7778/16.   
[38]    Menhat, Nur, M, S. "Performance measurement framework for the oil and gas supply chain"(2017). A thesis submitted in partial fulfilment for the requirements for the degree of Doctor of Philosophy at the University of Central Lancashire.    
[39]    Mensah, P., & Merkuryev, Y." Developing a resilient supply chain. Procedia - Social and Behavioral Sciences",(2014). 110, 309–319. https://doi.org/10.1016/j.sbspro.2013.12.875.   
[40]    Mirhasani S. An operational planning models for petroleum products logestics underc uncertainty.Apple Math Comput. (2008). 196:744-51.  
[41]    Moradi Nasab, N., & Amin-Naseri, M. R. "Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain" Energy, (2016). 114, 708–733. https://doi.org/10.1016/j.energy.2016.07.140.     
[42]    Namdar, J., Li, X., Sawhney, R., & Pradhan, N. Supply chain resilience for single and multiple sourcing in the presence of disruption risks. (2017).International Journal of Production Research, 7543(September), 1–22. https://doi.org/10.1080/00207543.2017.1370149.    
[43]    National Iranian Oil Products Distribution Company. "Documentation of performance report of the National Iranian Oil Products Distribution Company”, (2019).
[44]    Peck, H. “Drivers of Supply Chain Vulnerability: An Integrated Framework.” International Journal of Physical Distribution & Logistics Management )2005(. 35 (4): 210–232. 
[45]    Pettit, T.J., Fiksel, J. and Croxton, K.L." Ensuringsupply chain resilience: Development of a conceptualframework". (2010). Journal of Business Logistics, 31(1), pp. 01-21.
[46]    Qomi Oveili M., Jalali Naeini, S. GH. R, Tavakoli Moghaddam, R., Jabarzadeh, A. "Designing a closed-loop supply chain network under conditions of disruption and uncertainty, with product quality and resilience strategy", Journal of Engineering and Quality Management, (2015). V.6, No.2.        
[47]    Ruiz-benitez, R., & Lopez, C., Real, J. C.," Environmental benefits of lean , green and resilient supply chain management ”, (2017). The case of the aerospace sector, Journal of Cleaner Production 167, 850–862. https://doi.org/10.1016/j.jclepro.2017.07.201.   
[48]    Saunders, M., Lewis, P., & Thornhill, A. "Research methods for business students ".(2009). (4th ed.). Essex: Person Education Limited.    
[49]    Sabouhi, F., Pishvaee, M. S., & Jabalameli, M. S. "Computers & Industrial Engineering Resilient supply chain design under operational and disruption risks considering quantity discount : A case study of pharmaceutical supply chain". Computers & Industrial Engineering, (2018).126(June), 657–672. https://doi.org/10.1016/j.cie.2018.10.001.  
[50]    Sinha, A. K., Aditya, H. K., Tiwari, M. K., & Chan, F. T. S. "Agent oriented petroleum supply chain coordination: Co-evolutionary Particle Swarm Optimization based approach", Expert Systems with Applications, (2011). 38(5), 6132–6145. https://doi.org/10.1016/j.eswa.2010.11.004.     
[51]    Tang, C. S. Perspectives in Supply Chain Risk Management. International Journal of Production Economics. (2006), 103: 451-488.      
[52]    Tang C S." Robust strategies for mitigating supply chain disruptions", International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, (2006b). 9:1, 33-45: http://dx.doi.org/10.1080/13675560500405584.     
[53]    Torabi, S. A., Baghersad, M., & Mansouri, S. A. Resilient supplier selection and order allocation under operational and disruption risks. TRANSPORTATION RESEARCH (2015). PART E, 79, 22–48. https://doi.org/10.1016/j.tre.2015.03.005.    
[54]    Zahiri, B., Zhuang, J., & Mohammadi, M.(2020). Toward an integrated sustainable-resilient supply chain : a pharmaceutical casestudy. Transportation Research Part E, 103(2017), 109–142. https://doi.org/10.1016/j.tre.2017.04.009.     
[55]    Zsidisin, G.A. and Bob, R. Supply Chain Risk, International Series in Operations Research &Management Science, (2008). volume 124, New York: S      
پژوهش های نوین در تصمیم گیری
دوره 8، شماره 1
اردیبهشت 1402
صفحه 28-56

فایل ها

هم رسانی

ارجاع به این مقاله

آمار