نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت تولید و عملیات، دانشگاه علامه طباطبائی، تهران، ایران

2 استاد گروه مدیریت عملیات و فناوری اطلاعات، دانشگاه علامه طباطبائی، تهران، ایران

چکیده

پژوهش حاضر طراحی زنجیره تامین چهار سطحی کالاهای فاسدشدنی شامل کارخانجات تولیدی، مراکز توزیع، عمده‌فروشان و خرده‌فروشان در شرایط عدم اطمینان در پارامترهای مهم را مدنظر قرار داده است و به منظور اتخاذ تصمیمات مهم در سطوح استراتژیک و تاکتیکال از جمله مکان، تعداد و اندازه مراکز توزیع و عمده‌فروشان، سطح موجودی در مراکز انبارش کالا، تعیین میزان جریان کالا میان تسهیلات در سطوح مختلف زنجیره تامین و همچنین انتخاب نوع وسیله حمل و نقل کالا میان تسهیلات از یک مدل ریاضی سه هدفه بهره می‌برد. اهداف شامل حداقل‌سازی هزینه کل مورد انتظار در زنجیره تامین، دستیابی به کمترین زمان سفر کالا در زنجیره و در عین حال حداقل نمودن میزان انحراف از تقاضای مشتریان می‌باشد. مدل ارائه شده تلاش می‌کند ضمن توجه به عدم اطمینان محیطی و در نظر گرفتن سناریوهای عملیاتی مختلف و همچنین رویکرد احتمالی در پارامترهای مهم، با در نظر گرفتن دوره عمر محصول، نرخ متفاوت فساد کالا در تسهیلات مختلف انبارش، ظرفیت متفاوت تسهیلات در سناریوهای مختلف و همچنین در نظر گرفتن روش‌های مختلف حمل و نقل محصول با نرخ‌های مختلف فساد کالا، نقصان تحقیقات قبلی در حوزه طراحی زنجیره تامین کالاهای فاسدشدنی را پوشش دهد. با توجه به چند هدفه بودن مدل و همچنین لزوم ایجاد انعطاف در تصمیم‌گیری برای تصمیم‌گیران، این پژوهش از تکینک محدوده میان‌بخشی نرمال (NBI) که به تصمیم‌گیرندگان امکان انتخاب مطلوب‌ترین راه‌حل با توجه به درجه اهمیت اهداف مختلف را می‌دهد استفاده نموده است. به منظور حل مدل ریاضی از نرم‌افزار GAMS 24 و حل کننده MILP استفاده شده است.

کلیدواژه‌ها

عنوان مقاله [English]

Designing multi period-multi level Supply Chain for Fixed Lifetime Perishable Products under Uncertainty

نویسندگان [English]

  • Ahmad Ebrahimi 1
  • laya olfat 2
  • Maghsood Amiri 2
  • Mohammad Taghi Taghavifard 2

1 Doctoral student of Production and Operations Management, Allameh Tabatabai University, Tehran, Iran

2 Professor of Operations Management and Information Technology Department, Allameh Tabatabai University, Tehran, Iran

چکیده [English]

The current research has considered the design of the four-level supply chain of perishable goods, including manufacturing factories, distribution centers, wholesalers, and retailers, in conditions of uncertainty in important parameters. The aim is to make strategic and tactical decisions, including the location, number, and size of distribution centers and wholesalers, stock levels in stocking centers, determining the flow of goods between facilities at different supply chain levels, and choosing the type of means of transporting goods between facilities. This is achieved through a three-objective mathematical model. The goals include minimizing the expected total cost in the supply chain, achieving the shortest travel time of goods in the chain, and at the same time minimizing the amount of deviation from customer demand. The presented model tries to pay attention to environmental uncertainty and consider different operational scenarios, as well as the possible approach in important parameters. This takes into account the product life cycle, the different rate of spoilage of the goods in different storage facilities, the different capacity of the facilities in different scenarios, and considering different methods of product transportation with different rates of product spoilage. All of this aims to cover the lack of previous research in the field of perishable goods supply chain design. Considering the multi-objective nature of the model and the need to create flexibility in decision-making for decision-makers, this research uses Normal Boundary Intersection (NBI), which allows decision-makers to choose the most optimal solution according to the importance of different goals. GAMS 24 software and MILP solver were used to solve the mathematical model.
Materials and Methods
This study presents a multiobjective model for designing a four-echelon supply chain (SC) in the strategic and tactical levels for fixed lifetime perishable products. The targeted SC levels include production plants, distribution centers (DC), wholesalers, and retailers. The locations of the plants and retailers are predetermined, while the locations of DCs and wholesalers will be selected from potential locations. The elaborated model seeks to minimize the total cost and product transportation time in the SC and minimize expected demand deviation as well. The Normal Boundary Intersection (NBI) method is employed for solving the model, and GAMS software is used to determine the optimal values of decision variables.
Results
This study utilizes a case study of an Iranian broad dairy company that produces eleven product groups. Data for the study were collected from historical company records and expert interviews. According to the opinions of the experts, three different operational scenarios have been extracted, and the data related to each scenario, especially the customer demand, has been estimated according to historical data as well as the corrective opinions of the managers. The results of the proposed mathematical programming model showed that changes in demand did not have unexpected effects on the values of the objective function and did not change the general trend of the answer to the problem. On the other hand, changes in the percentage of perishability of the product had far less impact on the values of the objective functions as well as the membership function. The overall result is normal, and as a result, in general, these changes represent the stability of the model against the fluctuations of important parameters. A comparison of optimal results and reality reveals that the examined SC needs a redesign of its DCs and wholesalers' locations, and hybrid transportation methods should be used.
Conclusion
Supply chain design (SCD) of fixed lifetime perishable products at the strategic and tactical levels is indeed an important issue. By considering the research gap, this study developed a multi-objective and multi-level model for SCD of fixed lifetime perishable products, and new concepts such as varying perishability rates in storage and transportation facilities are considered. On the other hand, with regard to environmental uncertainty, important parameters such as demand and capacity of facilities are considered as probable parameters. Adding environmental and social factors as new objectives, hybrid transportation methods, and horizontal interactions in the same SC levels can be considered for model development. In order to solve the proposed model, NBI has been used, which has significant advantages compared to other solution methods. By turning the answer of the optimization model into a kind of decision-making problem, this technique gives flexibility to the decision-maker to choose the best solution for their supply chain design according to the weight of each goal. Also, the decision-maker can redesign and increase the adaptability of the supply chain by changing the important parameters of the problem over time.

کلیدواژه‌ها [English]

  • Supply Chain Design
  • Perishable Product
  • Mathematical Modeling
  • Uncertainty
  • Normal Boundary Intersection
  1. فیضی، کامران؛ الفت، لعیا؛ تقوی‌فرد، محمدتقی؛ مرادی باستانی، محسن (1391). مدل رابطه همکاری میان سازمانی برای بهبود عملکرد زنجیره تأمین در صنعت فرش ماشینی ایران. فصلنامه علوم مدیریت ایران، 6(22)، 1-27.
  2. غلامی، منا؛ هنرور، محبوبه (1394). ارائة مدلی ریاضی با رویکرد مدیریت موجودی توسط فروشنده برای اقلام بهبودپذیر و فسادپذیر در طول یک زنجیرة تأمین سه‌سطحی. نشریه مهندسی صنایع، 49(2)، 237-256.
  3. محمدی، علی؛ خلیفه، مجتبی؛ عباسی، عباس؛ علیمحمدلو، مسلم؛ اقتصادی‏فرد، محمود. (1396). طراحی زنجیره تأمین و یکپارچه‏سازی رویکردهای مالی و عملیاتی. چشم‏انداز مدیریت صنعتی، 26، 139-168.
  4. محمدی، علی؛ خلیفه، مجتبی؛ علیمحمدلو، مسلم؛ عباسی، عباس؛ اقتصادی‏فرد، محمود. (1397). طراحی عملیاتی و مالی سیستم زنجیره تأمین چند سطحی در سطوح تصمیم‏گیری استراتژیک و تاکتیکال. پژوهش‏های نوین در تصمیم‏گیری، 3(1)، 267-298.

 

  1. Amorim, P. Meyr, H. Almeder, C. & Almada-Lobo, B. (2011). Managing perishability in production-distribution planning: A discussion and review. Flexible Services and Manufacturing Journal, 20, 1–25.
  2. Chen, X. & Zhong, C.y. (2013). An improved genetic algorithm for location problem of logistic distribution center for perishable products. International Asia conference on industrial engineering and management innovation (IEMI2012) proceedings (pp. 949–959).
  3. Cohen, M. A. & Lee, H. L. (1988). Strategic analysis of integrated
    production-distribution systems: Models and methods. Operations
    Research
    , 36, 216–228.
  4. De keizer, M. Akkerman, R. Grunow, M. Bloemhof-Ruwaard, J. Haijema, R. Van der Vorst, J. (2017). Logistics network design for perishable products with heterogeneous quality decay. European Journal of Operational Research. 262, 535-549.
  5. Diabat A. Jabbarzadeh, A. Khosrojerdi, A. (2018). A perishable product supply chain network design problem with reliability and disruption considerations, International Journal of Production Economics, 212, 121-138.
  6. Diatha, K. Karumanchi, R. & Garg, S. (2012). Mobile enabled operations management using multi-objective-based logistics planning for perishable products. Computers and Industrial Engineering, 42, 133–142.
  7. Di, W. Wang, J. Li, B. & Wang, M. (2011). A location-inventory model for perishable agricultural product distribution centers. 2011 2nd international conference on artificial intelligence, management science and electronic commerce, AIMSEC 2011 – proceedings (pp. 919–922).
  8. Drezner, Z. & Scott, C. H. (2013). Location of a distribution center for a perishable product. Mathematical Methods of Operational Research, 78(3), 301–314.
  9. Faisal, M. N. Banwet, D. K. Shankar, R. (2006). Supply chain risk mitigation: modeling the enablers. Business Process Management Journal, 12(4), 535-552.
  10. Feizi, K. Olfat, L. Taghavifard, M. Moradi Bastani, M. (2012). Collaborative inter-organizational relationship model to improve supply chain performance in Iranian machine-woven carpet industry. Iranian journal of management sciences (IAMS). 6(22), 1-27. [In Persian].
  11. Ferguson M, Ketzenberg ME. (2006). Information sharing to improve retail product freshness of perishables. Production and Operations Management, 15(1): 57–73.
  12. Firoozi, Z. Ismail, N. Ariafar, S. Tang, S. H. Ariffin, M. K. M. A. & Memariani, A. (2013). Distribution Network Design for Fixed Lifetime Perishable Products: A Model and Solution Approach, Journal of Applied Mathematics, 1-13
  13. Firoozi, Z. Ismail, N. Ariafar, S. Tang, S. H. Ariffin, M. K. M. A. & Memariani, A. (2014). Effects of integration on the cost reduction in distribution network design for perishable products. Mathematical Problems in Engineering, 1–10.
  14. Friesz, T.L. Lee, I. Lin, C.C. (2011). Competition and disruption in a dynamic urban supply chain. Transportation Research Part B. 45 (8), 1212–1231.
  15. Ghezavati, V. R. Hooshyar, S. & Tavakkoli-Moghaddam, R. (2017). A Benders' decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: A case study of tomato. Central European Journal of Operations Research, 1-26.
  16. Gholami, M. & Honarvar, M. (2015). Developing a Mathematical Model for Vendor Managed Inventory Considering Deterioration and Amelioration Items in a Three-Level Supply Chain. Journal of Industrial Engineering, 49(2), 237-256. [In Persian].
  17. Govindan, K. Jafarian, A. Khodaverdi, R. & Devika, K. (2014). Two-echelon multiplevehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 9–28.
  18. Hammami, R. & Frein, Y. (2013). An optimisation model for the design of
    global multi-echelon supply chains under lead time constraints. International Journal of Production Research, 51, 2760–2775.
  19. Harrison, T.P. (2004). Principles for the strategic design of supply chains. In: Harrison, T.P. Lee, H.L. Neale, J.J. (Eds.), the Practice of Supply Chain Management: Where Theory and Application Converge. Springer, New York, pp. 3–12
  20. Hasani, A. Zegordi, S. H. & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50 (16), 4649–4669.
  21. Hiassat A, Diabat A. (2011). A location-inventory-routing problem with perishable products. Proceedings of the 41st International Conference on Computers and Industrial Engineering.
  22. Javier Arturo Orjuela-Castro, Lizeth Andrea Sanabria-Coronado, Andrés Mauricio Peralta-Lozano, (2014). Coupling facility location models in the supply chain of perishable fruits, Research in Transportation Business & Management, Volume 24, 73-80.
  23. Jouzdani, J. Sadjadi, S. J. & Fathian, M. (2013). Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Applied Mathematical Modelling, 8467–8483.
  24. Kaveh, A. and M. Ghobadi (2017). "A Multistage Algorithm for Blood Banking Supply Chain Allocation Problem." International Journal of Civil Engineering 15(1): 103-112.
  25. Kozlenkova, V. Hult, T. M. Lund, D. J. Mena, J. A. Kekec, P. (2015). The role of marketing channels in supply chain management. Journal of Retailing, 91(4), 586–609.
  26. Krishnamoorthy, N. R. D. A. M. (2016). Facility location and routing decisions for a food delivery network. IEEE international conference on industrial engineering and engineering management, Bali.
  27. Melo, M. Nickel, S. & Saldanha da Gama, F. (2006). Dynamic multi-commodity capacitated facility location: A mathematical modeling framework for strategic supply chain planning. Computers & Operations Research, 181–208.
  28. Meng, Q. Huang, Y. & Cheu, R. L. (2009). Competitive facility location on decentralized supply chains. European, Journal of Operational Research, 487–499.
  29. Mohammadi, A. Abbasi, A. Alimohammadlou, M. Eghtesadifard, M. & Khalifeh, M. (2017). Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach. Computers & Industrial Engineering, 114, 297-315.
  30. Mohammadi, A. M. Khalifeh, Abbasi, A. Alimohammadlou, M. Eghtesadifard, M. (2018). Designing Operational and Financial Multi Echelon Supply Chain System in Strategic and Tactical Levels of Decision-Making, Journal of Modern Researches in Decision Making, 3(1), 267-298 .
  31. Mohammadi, A. M. Khalifeh, Abbasi, A. Alimohammadlou, M. Eghtesadifard, M. (2017). Supply chain design and financial and operational approaches integration. Journal of Industrial Management Perspective, 26, 139-168 .
  32. Mohammad Musavi, M. Bozorgi-Amiri, A. (2017). A multi-objective sustainable hub location scheduling problem for perishable food supply chain, Computers & Industrial Engineering, 113, 766-778.
  33. Mousazadeh, M. Torabi, S.A. & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering, 82, 115-128.
  34. Pishvaee, M. S. & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36, 3433–3446.
  35. Ramezani, M. Kimiagari, A. M. Karimi, B. (2014). Closed-loop supply chain network design: a financial approach. Applied Mathematical Modelling, 38(15/16), 4099-4119.
  36. Rohaninejad, M. Sahraeian, R. & Tavakkoli-Moghaddam, R. (2018). Multi-echelon supply chain design considering unreliable facilities with facility hardening possibility.
  37. Shahabi, M. Unnikrishnan, A. Jafari-Shirazi, E. D. Boyles, S. (2014). A three level location-inventory problem with correlated demand, Transportation Research Part B 69, 1–18
  38. Tang, K. Yang, C. & Yang, J. (2007). A supply chain network design model
    for deteriorating items. In International Conference on Computational
    Intelligence and Security 2007 (pp. 1020–1024).
  39. Who, Global Database on Blood Safety, Summary Report 2011, World Health Organization, 2011, http://www.who.int/blood-safety/global database/GDBS Summary Report 2011.pdf.
  40. Yu, M. & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224(2), 273-282.
  41. Zahiri B. S. Torabi, M. Mousazadeh, and S. Mansouri, (2015). Blood collection management: Methodology and application, Applied Mathematical Modelling, 39, 7680-7696.
  42. Zhao, X. & Lv, Q. (2011). Optimal design of agri-food chain network: An improved particle swarm optimization approach. International Conference on Management and Service Science, (8), 1–5.