برنامه‌ریزی تصادفی دومرحله‌ای برای طراحی شبکه زنجیره تأمین دارویی بهنگام: مدل‌سازی و الگوریتم حل

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشگاه بوعلی سینا.

2 دانشیار، دانشگاه بوعلی سینا.

چکیده

دارو محصولی حیاتی است که سلامت جامعه را رقم می­‌زند و تحویل به‌موقع آن به مصرف‌­کنندگان از اهمیت بالایی برخوردار بوده و در نتیجه نیازمند به برنامه‌­ریزی مناسبی برای تولید/توزیع آن هستیم. در این پژوهش یک مسئله زنجیره‌­تأمین دارویی دوسطحی چند­دوره‌ای ارائه شد که تقاضا در سطح دوم غیرقطعی است. برای مدل‌سازی مسئله یادشده از رویکرد برنامه‌ریزی تصادفی دومرحله‌ای استفاده شد. هدف مدل ارائه­‌شده شامل حداقل‌­کردن هزینه‌­های تولید، موجودی، انتقال، هزینه‌های زمان ارسال، زودکرد و دیرکرد است. با توجه به اینکه مدل با تابع هدف زودکرد و دیرکرد با موعد تحویل متفاوت یک مسئله NP-hard است و هرچه ابعاد مسئله افزایش یابد، روش دقیق توانایی حل مسئله را در زمان معقول ندارد؛ بنابراین برای این مسئله یک الگوریتم ژنتیک به همراه یک الگوریتم ترکیبی ژنتیک و جست­وجوی همسایگی متغیر ارائه شد. در حل این مدل با استفاده از برنامه‌­ریزی تصادفی، پنج سناریو مطالعه و شاخص «ارزش مورد­انتظار اطلاعات کامل» محاسبه و درنهایت نتایج آن با جواب مدل برنامه‌­ریزی تصادفی دومرحله‌ای مقایسه شد. همچنین روش برنامه‌ریزی تصادفی، الگوریتم ترکیبی و الگوریتم ژنتیک در ­نظر ­گرفتن سناریوهای مختلف با یکدیگر مقایسه شدند. نتایج نشان داد که از لحاظ تابع هدف الگوریتم ترکیبی کارایی بسیار خوبی در مقایسه با الگوریتم ژنتیک دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Stochastic Bilevel Programing to Design of A JIT Pharmaceutical Supply Chain Network: Modeling and Algorithm

نویسندگان [English]

  • Maryam Hajibabaie 1
  • Javad Behnamian 2
1 Msc, Bu-Ali Sina University.
2 Associate Professor, Bu-Ali Sina University.
چکیده [English]

In the pharmaceutical supply chain, pharmaceutical products must be distributed among consumers with good quality at the right time and in the right place. Medicineis a product which affects the health of society and its timely delivery to consumers is of great importance. Therefore, it requires proper planning for its production and distribution. In this paper, we developed a model that minimize the cost of production, inventory, delivery, earliness and tardiness. We also assumed the uncertainty of demand and solved the linear mathematical model using stochastic programming and we solved the problem with stochastic programming. Also, due to the fact that the model with the objective function of earliness and tardiness with different delivery times of NP-hard problem for this problem, a hybrid genetic and variable neighborhood search algorithm were presented. Here, five scenarios were considered, the expected value of perfect information (EVPI) was measured and the obtained results were compared with the two-stage random-scheduling model. The computational results showed the efficiency of the developed model. Also, the results of the proposed hybrid algorithm were compared with the genetic algorithm, and the results showed that in terms of objective function, the hybrid algorithm has a much better performance compared to the genetic algorithm.

کلیدواژه‌ها [English]

  • Pharmaceutical Supply Chain؛ Stochastic Programming؛ Hybrid Algorithm؛ Just-In-Time
  • Genetic Algorithm
  1.  

    1. Alem-Tabriz, A., Zandieh, , & Mohammad-Rahimi, A. (2013). Metaheuristic algorithms in combinatorial optimization (Genetic, Neural Network, Simulated Annealing, Tabu search and Ant Colony optimization). Saffar Publication. (In Persian)
    2. Behnamian, J. (2016). Solving Complex Optimization Problems Methods and Algorithms. Bu-Ali Sina University Press.
    3. Chen, X., Yang, H., & Wang, X. (2019). Effects of price cap regulation on the pharmaceutical supply chain. Journal of Business Research, 97, 281-290.
    4. Chiu, H., & Huang, H. (2003). A multi-echelon integrated JIT inventory model using the time buffer and emergency borrowing policies to deal with random delivery lead times, International Journal of Production Research, 41(13), 2911-2931.
    5. Daher, M. (2001). Health Systems: Improving Performance. The World Health Report 2000. Le Journal medical libanais. The Lebanese medical journal, 49(1), 22-24.
    6. Eshghi, K., Karimi-Nasab, M. (2016). Analysis of Algorithms and Design of Metaheuristics. Sharif University Press.
    7. Feizollahi, S., Soltanpanah, H., Farughi, H., & Rahimzadeh, A. (2019). Development of Multi Objective Multi Period Closed-Loop Supply Chain Network Model Considering Uncertain Demand and Capacity. Journal of Industrial Management Perspective, 8(32), 61-95. (In Persian)
    8. Haial, A., Berrado, A., & Benabbou, L. (2016). A Framework for Designing a Transportation Strategy: the case of a pharmaceutical supply chain. 3rd International Conference on Logistics Operations Management (GOL) 1-6.
    9. Janatyan, N., Zandieh, M., Alem Tabriz, A., & Rabieh, M. (2019). Optimizing Sustainable Pharmaceutical Distribution Network Model with Evolutionary Multi-objective Algorithms (Case Study: Darupakhsh Company). Production and Operations Management, 10(18), 133-153. (In Persian)
    10. Jeong, S. (2017). The Production-Distribution Problem in the Supply Chain Network using Genetic Algorithm. International Journal of Applied Engineering Research, 12(23), 13570-13581.
    11. Jouyban, F., yousefi, M., & Neyshaboori, E. (2018). Presenting a bi objective stochastic pharmaceutical supply chain model considering time and cost. Journal of Industrial Management Faculty of Humanities, 13(44), 15-28. (In Persian)
    12. Kalantari, M., & Pishvaee, M.S. (2016). A Robust Possibilistic Programming Approach to Drug Supply Chain Master Planning. Journal of Industrial Engineering Research in Production Systems, 4(7), 49-67. (In Persian)
    13. Karuppasamy, S.K., & Uthayakumar, R. (2019). Coordination of a three-level supply chain with variable demand and order size dependent trade credit in healthcare industries. International Journal of System Assurance Engineering and Management, 10, 285-298.
    14. Kazemzadeh, N., Ryan, S., & Hamzeei, M. (2017). Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation. Energy system, 10(1), 1-25.
    15. Khaireddin, M., Abu Assab, M., & Nawafleh, S. (2015). Just-in-Time Manufacturing practices and Strategic Performance: An Empirical Study Applied on Jordanian Pharmaceutical Industries. International Journal of Statistics and Systems, 10(2), 287-307.
    16. Low, Y., Halim, I., AdhityaL, L., Chew, W., & Sharratt, P. (2016). Systematic Framework for Design of Environmentally Sustainable Pharmaceutical Supply Chain Network. Journal of Pharmaceutical Innovation, 11, 250-263.
    17. Mashayekhi Nezamabadi, E., & Alem Tabriz, A. (2017). The Impact of integration of Upstream and Downstream of Supply Chain on Quality Performance and Quality Program. Journal of Industrial Management Perspective, 6(4), 35-57. (In Persian)
    18. Mehralian, GH., Zarenezhad, F., & Rajabzadeh Ghatari, A. (2015). Developing a model for an agile supply chain in pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 9(1), 74-91.
    19. Mir Hassani, S. A. (2018). Stochastic Programming. Amirkabir University of Technology Publication.
    20. Mohammadi, M., & Soleimani, H. (2020). Investigating Open Loop and Closed-Loop Supply Chain under Uncertainty (Case Study: Iran Teransfo Company), Journal of Industrial Management Perspective, 10(38), 33-53. (In Persian)
    21. Mousazadeh, M., Torabi, S.A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers and Chemical Engineering, 82, 115-128.
    22. Nasrollahi, M., & Razmi, J. (2021). A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty. Operational Research, 21(1), 525-552.
    23. Pinedo, M. L (2016). Scheduling-Theory, Algorithms and Systems. (Mohammad Hossein Fazel Zarandi- Seyyed Abolfazl Soltani - Ali Yoosefelahi - Hamed Davari Ardekani - Hamed Soleymani). Amirkabir University of Technology Publication.
    24. Privett, N., & Gonsalvaz, D. (2014). The top ten global health supply chain issues: Perspectives from the field, Operations Research for Health Care, 3(4), 226-230.
    25. Priyan, S., and Uthayakumar, R. (2014). Optimal inventory management strategies for pharmaceutical company and hospital supply chain in a fuzzy-stochastic environment. Operations Research for Health Care, 3(4), 177-190.
    26. Rezaeenour, J., Hashempour, M., & Akbari, A.H. (2020). A Four-Echelon Supply Chain Considering Economic, Social and Regions Satisfaction Goals. Journal of Industrial Engineering Research in Production Systems, 7(15), 199-217. (In Persian).
    27. Savadkoohi, E., Mousazadeh, & Torabi, A. (2018). A possibilistic location-inventory model for multi-period perishable pharmaceutical supply chain network design, Chemical Engineering Research and Design, 138, 490-505.
    28. Stadtler, H., Kilger, Ch. (2016). Supply Chain Management. (Nasrin Asgari - Reza Zanjerani Farahani). Amirkabir University of Technology Publication.
    29. Zahiri, B., Jula, P., & Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283.
    30. Zarenezhad Ashkezari, F. (2011). Designing Agile Supply Chain Management in Pharmaceutical Industry of Iran. Degree of Master, University of Science and Culture. (In Persian).
  • تاریخ دریافت: 14 فروردین 1399
  • تاریخ بازنگری: 28 تیر 1400
  • تاریخ پذیرش: 19 مرداد 1400
  • تاریخ اولین انتشار: 25 مرداد 1400
  • تاریخ انتشار: 01 دی 1400