ارائه مدل برنامه ریزی تولید-توزیع یکپارچه زنجیره‌تأمین حلقه بسته محصولات کشاورزی بر اساس تصمیم گیری گروهی احتمالی و مسائل زیست محیطی

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکترای تحقیق در عملیات دانشگاه فردوسی مشهد، مشهد، ایران

2 دانشجوی دکتری مدیریت صنعتی، گروه مدیریت، دانشگاه فردوسی، مشهد، ایران،

3 مربی، مدیریت، دانشگاه پیام نور، تهران، ایران

4 کارشناس ارشد مهندسی صنایع، سنندج، ایران

چکیده

طراحی کار­آمد زنجیره‌تأمین باعث بهبود عملکرد در سازمان­ها می­شود. این موضوع در زنجیره­تأمین محصولات کشاورزی کمتر مورد توجه قرار گرفته است. در این مطالعه تلاش می­شود با رویکردی یکپارچه به بررسی برنامه­ریزی برای تأمین، تولید و توزیع پرداخته شود.  در این پژوهش یک مدل برنامه­ریزی عدد صحیح چند هدفه که به دنبال حداقل کردن هزینه­ها،  آثار زیست محیطی و حداکثر­کردن اهمیت تأمین­کنندگان می­باشد، ارائه شده است. در این پژوهش از ترکیبی از روش­های تصمیم­گیری چند معیاره برای اولویت­بندی تأمین­کنندگان استفاده شد. پس از آن وزن­های به دست آمده تحت ورودی­های مدل چند ­هدفه در نظر گرفته شدند. مدل پیشنهادی با در نظر گرفتن ترکیبی از معیارهای کیفی و کمی و با توازن برقرار کردن بین معیارها می­تواند ترکیبی از بهترین تأمین کنندگان را پیدا کند. مقایسه جواب­های حاصل از مدل ارائه شده با میزان واقعی متغیرها در بازه زمانی مورد مطالعه تفاوت آشکار در هزینه­ها را روشن ساخت و نتایج حاکی از آن است که مدل ارائه شده   می­تواند هزینه­ها را به میزان قابل توجهی کاهش دهد. با انجام تحلیل حساسیت بر روی یکی از پارامترهای کلیدی مدل (تقاضا)، اثر این پارامتر بر توابع هدف بررسی شد و نتایج نشان دادند در فاصله تغییرات 10 درصدی در میزان تقاضا تفاوت قابل ملاحظه­ای در توابع هدف مشاهده نمی­شود در حالی که  اگر تغییرات تقاضا در محدوده 20 درصدی تغییر کند تفاوت آشکاری در توابع هدف پدید می­آید. بنابراین می­توان گفت جواب­های حاصل از حل مدل و در زمان مناسب حاکی از کارایی و صحت مدل می­باشد و مبین قابلیت مدل مذکور برای پاسخگویی به شرایط واقعی است. جهت اعتبار­سنجی مدل ارائه شده جواب­های حاصل با میزان واقعی متغیرها در بازه زمانی مورد مطالعه مقایسه گردیده که نتایج حاکی از کاهش هزینه در مدل ارائه شده می­باشد. بنابراین می­توان گفت جواب­های حاصل از حل مدل و در زمان مناسب حاکی از کارایی و صحت مدل می­باشد و مبین قابلیت مدل مذکور برای پاسخگویی به شرایط واقعی است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Presenting the Integrated Production-Distribution Planning Model of the Closed-Loop Supply Chain for Agricultural Products Based on Probably Group Decision-Making and Environmental Issues

نویسندگان [English]

  • azam modares 1
  • vahideh bafandegan emroozi 2
  • zahra mohemmi 3
  • azade modares 4
1 phd student
2 phd student
3 sistan and baluchestan univercity
4 student
چکیده [English]

Efficient supply chain design improves performance in organizations. This issue has been given less attention in the supply chain of agricultural products. This study uses an integrated approach to planning for supply, production, and distribution. This research presents a  multi-objective integer programming model that seeks to minimize costs, environmental effects and maximize suppliers' importance. This research used a combination of             multi-criteria decision-making methods to prioritize suppliers. After that, the obtained weights were considered under the inputs of the multi-objective model. The proposed model can find a combination of the best suppliers by considering a variety of qualitative and quantitative criteria and balancing the criteria. Comparing the answers obtained from the presented model with the actual amount of variables in the studied period clarified the apparent difference in costs. The results indicate that the proposed model can reduce costs significantly. The effect of this parameter on the objective functions was investigated by performing a sensitivity analysis on one of the critical parameters of the model (demand). The results showed that there is no significant difference in the objective functions within the interval of 10% changes in the amount of demand. In comparison, if the demand changes within 20%, a noticeable difference in the objective functions appears. Therefore, it can be said that the answers obtained from solving the model at the right time indicate the model's efficiency and accuracy and show the model's ability to respond to actual condition.

کلیدواژه‌ها [English]

  • Supply Chain
  • Multi-Objective Optimization
  • L-P Metric
  • Integrated Logistics

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[1] A. Ahmadi Nadoshan, “Desihn of robust optimization mathematical model for logistic chain,” M.S. Thesis, Tarbiat Modares Univ, 2012.  [In Persian]
 [2] C. Alzaman, Z. H. Zhang, and A. Diabat, “Supply chain network design with direct and indirect production costs: Hybrid gradient and local search based heuristics,” Int. J. Prod. Econ., vol. 203, pp. 203–215, 2018.
[3] M. Agha Mohammadali Kermani, M. Hakimi, and M. H. Ali Ahmadi, “Supplier selection in supply chain: Fuzzy approach,” Modiriat Farda, vol. 21, pp. 74–88, 2009. [In Persian]
[4] A. Modares, V. Bafandegan Emroozi and Z. mohemmi, “Evaluate and control the factors affecting the equipment reliability with the approach Dynamic systems simulation, Case study: Ghaen Cement Factory,” Journal of Quality Engineering and Management, vol. 11, pp.  89-106, 2021. [In Persian]
[5] M. S. Pishvaee, F. Jolai, and J. Razmi, “A stochastic optimization model for integrated forward/reverse ogistics network design,” J. Manuf. Syst., vol. 28, no. 4, pp. 107–114, 2009
[6] A. Modares, N. Motahari Farimani, and V. B. Emroozi, “A new model to design the suppliers portfolio in newsvendor problem based on product reliability,” J. Ind. Manag. Optim., vol. 19, no. 6, pp. 4112-4151, 2023.
[7] Ayers, janse B, “ Handbook of supply chain in management. (1st ed.). CRC Press.
 [8] V. Bafandegan Emroozi, A. Modares, and Z. mohemmi, “Presenting a model for diagnosing the implementation of total quality management based on performance expansion model (Case: Simorgh Rail Transportation Company) ,” Road, 2022. [In Persian].
[9] R. Cruz-Rivera and J. Ertel, “Reverse logistics network design for the collection of End-of-Life Vehicles in Mexico,” Eur. J. Oper. Res., vol. 196, no. 3, pp. 930–939, 2009
 [10] V. Bafandegan Emroozi, and A. Fakoor. “A new approach to human error assessment in financial service based on the modified CREAM and DANP,” Journal of Industrial and Systems Engineering, 2023.
[11] J. B. Yang, “Gradient projection and local region search for multiobjective optimisation,” Eur. J. Oper. Res., vol. 112, no. 2, pp. 432–459, 1999.
 [12] A. Diabat, T. Abdallah, and A. Henschel, “A closed-loop location-inventory problem with spare parts consideration,” Comput. Oper. Res., vol. 54, pp. 245–256, 2015.
 [13] B. Fahimnia, J. Sarkis, and H. Davarzani, “Green supply chain management: A review and bibliometric analysis,” Int. J. Prod. Econ., vol. 162, pp. 101–114, 2015.
[14] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002.
 [15]J. Ghahremani Nahr,  And A, Ghodratnama, “ Designing a multi-product and multi-cycle green supply chain network with consideration of discounts in conditions of uncertainty,” Industrial Engineering in Production Systems, vol, 6, no. 13. pp. 119-137, 2018. [In Persian]
[16] F. H. F. Liu and H. L. Hai, “The voting analytic hierarchy process method for selecting supplier,” Int. J. Prod. Econ., vol. 97, no. 3, pp. 308–317, 2005.
 [17] A. Haji Mirzajan, M. A. Piraish , and  F.  Dehghanian, “Presenting a supply chain planning model for perishable crops,” production and operations management, Vol, 6, No, 11pp. 35-60, 2015.
[18] A. Modares, N. M. Farimani, and V. B. Emroozi, “A vendor-managed inventory model based on optimal retailers’ selection and reliability of supply chain,” J. Ind. Manag. Optim., vol. 19, no. 5, pp. 3075–3106, 2023.
 [19] A. Modares, N. M. Farimani, and V. B. Emroozi, “Developing a Newsvendor model based on the relative competence of suppliers based on probabilities group decisions,”Industrial Management Journal, vol. 14, pp.115-142, 2022.
 [20] E. H. Sabri and B. M. Beamon, “A multi-objective approach to simultaneous strategic and operational planning in supply chain design,” Omega, vol. 28, no. 5, pp. 581–598, 2000
 [21] G. C. Stevens, “International Journal of Physical Distribution & Logistics Management Emerald Article: Integrating the Supply Chain,” Int. J. Phys. Distrib. Logist. Manag., vol. 19, no. 8, pp. 3–8, 1989.
 [22] M. Talaei, B. Farhang Moghaddam, M. S. Pishvaee, A. Bozorgi-Amiri, and S. Gholamnejad, “A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry,” J. Clean. Prod., vol. 113, pp. 662–673, 2016
 [23] F. Wang, X. Lai, and N. Shi, “A multi-objective optimization for green supply chain network design,” Decis. Support Syst., vol. 51, no. 2, pp. 262–269, 2011.
[24] A.S. Mohammadi, A. Alam Tabriz. And M.S. Pishvaei, “Design of a closed-loop green supply chain network with financial decisions in conditions of uncertainty,” Journal of Industrial Management, vol. 10, pp. 61–88, 2018. [In Persian].  
[25] A. M. F. Saghih and A. Modares, “a New Dynamic Model To Optimize the Reliability of the Series-Parallel Systems Under Warm Standby Components,” J. Ind. Manag. Optim., vol. 19, no. 1, pp. 376–401, 2023.  
[26] T. Santoso, S. Ahmed, M. Goetschalckx, and A. Shapiro, “A stochastic programming approach for supply chain network design under uncertainty,” Eur. J. Oper. Res., vol. 167, no. 1, pp. 96–115, 2005.
 
[27] M. A, Ramazanian and A. Modares, “Application of particle swarm optimization algorithm to aggregate production planning,”. Asian Journal of Business Management Studies, 2(2), pp44-54, 2011.
[28] V. Bafandegan Emroozi, P. Roozkhosh, A. Modares and F. Roozkhosh, “Selecting Green Suppliers by Considering the Internet of Things and CMCDM Approach,”. Process Integr Optim Sustain. 2023. Doi: 10.1007/s41660-023-00336-9.
[29] P. Roozkhosh and N. Motahari Farimani, “Designing a new model for the hub location-allocation problem with considering tardiness time and cost uncertainty,” Int. J. Manag. Sci. Eng. Manag., 2022.
[30] M. Rahmani, Z. Sazour, and A. Abzorgi Amiri, “Presenting a three-objective mathematical model for sustainable planning of the supply chain of perishable agricultural materials,” Industrial Engineering and Management, pp31-25, 2015.
[31] H. Soleimani, K. Govindan, H. Saghafi, and H. Jafari, “Fuzzy multi-objective sustainable and green closed-loop supply chain network design,” Comput. Ind. Eng., vol. 109, pp. 191–203, 2017.  
[32] T. Paksoy, T. Bektaş, and E. Özceylan, “Operational and environmental performance measures in a multi-product closed-loop supply chain,” Transp. Res. Part E Logist. Transp. Rev., vol. 47, no. 4, pp. 532–546, 2011.
[33] V. Özkir and H. Başligil, “Multi-objective optimization of closed-loop supply chains in uncertain environment,” J. Clean. Prod., vol. 41, pp. 114–125, 2013.
[34] S. Nayeri, M. M. Paydar, E. Asadi-Gangraj, and S. Emami, “Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design,” Comput. Ind. Eng., vol. 148, no. August, pp. 106716, 2020.
[35] A. Modares, M. Kazemi, V. Bafandegan Emroozi, and P. Roozkhosh, “A new supply chain design to solve supplier selection based on internet of things and delivery reliability,” Journal of Industrial and Management Optimization., vol. 19, no. 11, pp. 7993-8028, 2023.
[36] S. Rajabi, P. Roozkhosh, and N. M. Farimani, “MLP-based Learnable Window Size for Bitcoin price prediction,” Appl. Soft Comput., vol. 129, p. 109584, 2022.
[37] T.-H. Hejazi and P. Roozkhosh, “Partial inspection problem with double sampling designs in multi-stage systems considering cost uncertainty,” J. Ind. Eng. Manag. Stud., vol. 6, no. 1, pp.      1–17, 2019.  
[38] R. Sadeghi Rad and N. Nahavandi, “A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount,” J. Clean. Prod., vol. 196, pp. 1549–1565, 2018.
 [39] N. M. Farimani, A. Modares, and N.  Jahanara. “Providing a framework for the acquisition of experts' tacit knowledge to identify environmental opportunities and threats,” Transformation Management Journal, vol. 13(2), pp. 91-124, 2022.
[40] Rezaei, J., 2016. Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, vol. 64, pp. 126-130.
 [41] A. Modares, N. Motahari Farimani, and V. B. Emroozi, “Applying a multi-criteria group decision-making method in a probabilistic environment for supplier selection (Case study: Urban railway in Iran). Journal of Optimization in Industrial Engineering, vol. 16, no. 1, pp.129-140, 2023.
[42] V. Bafandegan Emroozi, A. Faezian, K. Seffati, H. Ebrahimi, B. Dadakhani, “Evaluation Commercialization Challenges and Resolutions in SMEs Using ML-FCM (Case study: Sanat Prozheh Toos) . Journal of Systems Thinking in Practice, vol. 2, no. 1, pp. 39-55, 2023.
 [43] M. Mohammadi and J. Rezae i, “Bayesian best-worst method: A probabilistic group decision making model,” Omega (United Kingdom), vol. 96, 2020
 [44] S. H. Huang and H. Keskar, “Comprehensive and configurable metrics for supplier selection,” Int. J. Prod. Econ., vol. 105, no. 2, pp. 510–523, 2007. 
[45] P. Roozkhosh, A. Pooya, and R. Agarwal, “Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach,” Oper. Manag. Res., no. 0123456789, 2022.
[46] P. Roozkhosh, M. Kazemi, “Application of Internet of Things in Green Supply Chain and Investigating the Effective Factors for Selecting a Green Supplier: A Case Study: Mashhad Rubber Factory. Supply Chain Management Journal, vol. 75, pp. 61-73,2022 [In Persian].
 [47] Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.