طراحی یک زنجیره‌تأمین‌ چهار سطحی دارو با در نظر گرفتن اهداف اقتصادی، اجتماعی و رضایت مناطق

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه مهندسی صنایع، مدیر فناوری اطلاعات دانشگاه قم

2 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران

3 کارشناس ارشد مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه صنعتی قم، قم، ایران

10.22084/ier.2020.19597.1875

چکیده

در این مقاله یک مدل جدید برنامه‌ریزی چندهدفه برای طراحی یک شبکه زنجیره­تأمین‌‌ چهار سطحی دارو در چند دوره و برای چند محصول فاسدشدنی توسعه داده می­شود. سطوح زنجیره شامل تأمین‌‌کنندگان، تولیدکنندگان، مراکز توزیع و خرده‌فروشان است. این مدل به تصمیم­گیری یکپارچه مسائل مکان­یابی­ مراکز تولید و مراکز توزیع دارو، تخصیص بهینه آن‌ها به یکدیگر به‌منظور حمل‌ونقل مناسب داروها در بین سطوح، تعیین مقدار بهینه­ی تولید و حمل‌ونقل در بین تسهیلات و نیز تعداد بهینه­ی استخدام و اخراج نیروی کار برای تولید بهینه محصولات دارویی کمک می­کند. همچنین مراکز تولید و توزیع دارای سطح تکنولوژی مختلف جهت تأسیس هستند. اهداف مسئله شامل کاهش هزینه­های زنجیره همراه با کاهش اختلاف بیکاری و تأمین‌ دارو در بین مناطق مختلف و افزایش رضایت مناطق مختلف با توجه به اهمیت تأمین‌ هرچه بیشتر دارو است. به‌دلیل NP-hard بودن مسئله و عدم کارایی روش­های دقیق، یک روش فراابتکاری مبتنی بر الگوریتم ژنتیک برای حل مسئله معرفی و عملکرد آن در طیف گسترده­ای از مسائل نمونه­ای تک­هدفه و دوهدفه بررسی می­شود. نتایج نشان می­دهد وجود هدف بیشینه کردن رضایت مناطق و کاهش اختلاف آن بین مناطق مختلف اهمیت بالایی در زنجیره­تأمین‌‌ دارو دارد. علاوه بر آن، کاهش اختلاف بیکاری بین مناطق مختلف باعث بهبود سطح اشتغال و وجود تعادل در مسئولیت­های اجتماعی زنجیره می­شود. همچنین الگوریتم پیشنهادی قادر است مسائلی با سایز بزرگ را هم به‌صورت تک­هدفه و هم دوهدفه درزمانی کم و جوابی کارا حل کند

کلیدواژه‌ها


عنوان مقاله [English]

A four-echelon supply chain considering economic, social and regions satisfaction goals

نویسندگان [English]

  • Jalal Rezaeenour 1
  • Motahhare Hashempoor 2
  • Amir Hosein Akbari 3
1 Assistant professor, Department of Industrial Engineering, Faculty of Technology and Engineering, University of Qom, Head of ICT Center, University of Qom
2 MSc student, Department of Industrial engineering, university of Qom, Qom, Iran
3 MSc, Department of Industrial engineering, qom university of technology, Qom, Iran
چکیده [English]

This study develops a new multi-objective programming model to design a four-echelon pharmaceutical supply chain (PSC) network for several perishable products over multiple time periods. Supply chain consists of four echelons, including suppliers, manufacturers, distribution centers, and retailers. This model proposes an integrated decision-making approach for the location of facilities (pharmaceutical production and distribution sites) and their most suitable allocation to each other for a reliable transportation of products between echelons. It also determines the optimal amount of production and transportation among facilities and the required number of labours. A varying level of technological expertise is required for the establishment of production and distribution systems. The problem aims to reduce costs and unemployment and pharmaceutical supply gap between regions and to increase their satisfaction rate with an emphasis on the importance of providing a large supply of pharmaceutical products. Given the fact that the problem is a NP-hard one and accurate methods are inefficient, a genetic algorithm-based meta-heuristic is developed for problem-solving and its performance is analyzed on a wide range of single- and two-objective problem instances. The results show that an increase in the satisfaction rate of regions and a reduction in its gap between regions as two objectives are of great importance in pharmaceutical supply chain. Moreover, a reduction in unemployment gap between regions improves the level of employment, and it provides a right balance between social responsibilities. The developed algorithm also provides an optimal solution for large-sized single- and two-objective problems in a short time period.

کلیدواژه‌ها [English]

  • Pharmaceutical Supply Chain
  • Sustainable Development
  • Social Responsibility
  • Genetic Algorithm
  • social inequality
[1] Sabouhi, F., Pishvaee, M. S. and Jabalameli, M. S. (2018). “Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain”, Computers & Industrial Engineering, vol. 126: 657-672.

[2] Jafar Nejad, H.A., Safari, A. Azar, S.A. Ebrahimi (2015). “Manage supply chain orders based on traditional costing approach and cost based on activity and compare them”, IQBQ, 2015.

[3] Meijboom, B., Obel, B. (2007). “Tactical coordination in a multi-location and multi-stage operations structure: A model and a pharmaceutical company case”, Omega, vol. 35, no. 3: 258-273.

[4] Eberle, L. G., Sugiyama, H. and Schmidt, R. (2014). “Improving lead time of pharmaceutical production processes using Monte Carlo simulation”, Computers & chemical engineering, vol. 68: 255-263.

[5] Zahiri, B., Jula, P. and Tavakkoli-Moghaddam,  R. (2018). “Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products”, Information Sciences, vol. 423: 257-283.

[6] Savadkoohi, E., Mousazadeh, M. and Torabi, S. A. (2018). “A possibilistic location-inventory model for multi-period perishable pharmaceutical supply chain network design”, Chemical Engineering Research and Design, vol. 138: 490-505.

[7] Hansen, K. R. N. and Grunow M. (2015). “Planning operations before market launch for balancing time-to-market and risks in pharmaceutical supply chains”, International Journal of Production Economics, vol. 161: 129-139.

[8] Jabbarzadeh, A., Haughton, M. and Pourmehdi, F. (2018). “A robust optimization model for efficient and green supply chain planning with postponement strategy”, International Journal of Production Economics, vol. 214: 266-283.

[9] Settanni, E., Harrington, T. S. and Srai, J. S. (2017). “Pharmaceutical supply chain models: A synthesis from a systems view of operations research”, Operations Research Perspectives, vol. 4: 74-95.

[10] Safarzadeh, S. and Rasti-Barzoki, M. (2019). “A game theoretic approach for assessing residential energy-efficiency program considering rebound, consumer behavior, and government policies”, Applied Energy, vol. 233: 44-61.

[11] Mele, F. D., Kostin, A. M., Guillén-Gosálbez, G. and Jiménez, L. (2011). “Multiobjective model for more sustainable fuel supply chains. A case study of the sugar cane industry in Argentina”, Industrial & Engineering Chemistry Research, vol. 50, no. 9: 4939-4958.

[12] Rashidi, K. and Saen, R. F. (2018). “Incorporating dynamic concept into gradual efficiency: Improving suppliers in sustainable supplier development”, Journal of Cleaner Production, vol. 202: 226-243.

[13] Rusnac, C.-M., Baboli, A., Moyaux, T. and Botta-Genoulaz, V. (2012). “Downstream pharmaceutical supply chain reorganization by considering the sustainable development criteria”, IFAC Proceedings Volumes, vol. 45, no. 6: 528-533.

[14] Pishvaee, M. S., Razmi, J. and Torabi, S. A. (2012). “Robust possibilistic programming for socially responsible supply chain network design: A new approach”, Fuzzy sets and systems, vol. 206: 1-20.

[15] Bojarski, A. D., Laínez, J., Espuña, M. A. and Puigjaner, L. (2009). “Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach”, Computers & Chemical Engineering, vol. 33, no. 10: 1747-1759.

[16] Borumand, A. and Beheshtinia, M. A. (2018). “A developed genetic algorithm for solving the multi-objective supply chain scheduling problem”, The international journal of system, cybernetics and management science. vol. 47, no. 7: 1401-1419.

[17] Sousa, R. T.,  Shah, N. and Papageorgiou, L. G. (2005). “Global supply chain network optimisation for pharmaceuticals”, Computer Aided Chemical Engineering, vol. 20: 189-1194.

[18] Susarla, N. and Karimi, I. A. (2012). “Integrated supply chain planning for multinational pharmaceutical enterprises”, Computers & Chemical Engineering, vol. 42: 168-177.

[19] Beheshtinia,  M. A. and Ghasemi, A. (2017). “A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system”, Engineering Optimization. vol. 50, no. 9: 1415-1433

[20] Soysal, M., Bloemhof-Ruwaard, J. Haijema, M. R. and van der Vorst, J. G. (2015). “Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty” , International Journal of Production Economics, vol. 164: 118-133.

[21] Taheri, S. M. R. and Beheshtinia, M. A. (2019). “A Genetic Algorithm Developed for a Supply Chain Scheduling Problem”, Iranian Journal of Management Studies (IJMS). vol. 12, no. 2: 107-132.

[22] Stellingwerf, H., Kanellopoulos, M. A., van der Vorst, J. G. and Bloemhof, J. M. (2018). “Reducing CO 2 emissions in temperature-controlled road transportation using the LDVRP model”, Transportation Research Part D: Transport and Environment, vol. 58: 80-93.

[23] Beheshtinia, M. A., Ghasemi, A. and Farokhnia, M. (2018). “Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company)”, Journal of modelling in managemevol. 13, no.1: 27-49.

[24] Cardoso, S. R., Barbosa-Póvoa, A. P. F. and Relvas, S. (2013). “Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty”, European Journal of Operational Research, vol. 226, no. 3: 436-451.

[25] Saedi, S., Kundakcioglu, O. E. and Henry, A. C. (2016). “Mitigating the impact of drug shortages for a healthcare facility: an inventory management approach”, European Journal of Operational Research, vol. 251, no. 1: 107-123.

[26] Timajchi, A., Al-e-Hashem, S. M. M. and Rekik, Y. (2018). “Inventory routing problem for hazardous and deteriorating items in the presence of accident risk with transshipment option”, International Journal of Production Economics. vol. 209: 302-315

[27] Sousa, R. T., Liu, S. L., Papageorgiou, G. and Shah, N. (2011). “Global supply chain planning for pharmaceuticals”, chemical engineering research and design, vol. 89, no. 11: 2396-2409.

[28] Sundaramoorthy, A., Li, X., Evans, J. M. and Barton, P. I. (2012). “Capacity planning for continuous pharmaceutical manufacturing facilities”, in Computer Aided Chemical Engineering, vol. 31: Elsevier, 1135-1139.

[29] Nematollahi, M. Hosseini-Motlagh, S.-M., Ignatius, Goh, J. M. and Nia, M. S. (2018). “Coordinating a socially responsible pharmaceutical supply chain under periodic review replenishment policies”, Journal of Cleaner Production, vol. 172: 2876-2891.

[30] Fatemeh Jafarkhan, S. Y. (2015). “A Robust Mathematical Model and Heuristic Solution Algorithm for Integrated Production-Routing-Inventory Problem Of Perishable Products with Lateral Transshipment”, Journal of Industrial Engineering Research in Production Systems vol. 4, no.8: 195-211.

[31] Raj, A., Biswas, I. and Srivastava, S. K. (2018). “Designing supply contracts for the sustainable supply chain using game theory”, Journal of Cleaner Production, vol. 185: 275-284.

[32] Padhi, S. S., Pati, R. K., and Rajeev, A. (2018). “Framework for selecting sustainable supply chain processes and industries using an integrated approach”, Journal of Cleaner Production, vol. 184: 969-984.

[33] Dehghanian, F., and Mansour, S. (2009). “Designing sustainable recovery network of end-of-life products using genetic algorithm”, Resources, Conservation and Recycling, vol. 53, no. 10: 559-570.

[34] Fahimnia, B., and Jabbarzadeh, A. (2016). “Marrying supply chain sustainability and resilience: A match made in heaven”, Transportation Research Part E: Logistics and Transportation Review, vol. 91: 306-324.

[35] Bhinge, R., Moser, R., Moser, E., Lanza, G., and Dornfeld, D. (2015). “Sustainability optimization for global supply chain decision-making”, Procedia CIRP, vol. 26: 323-328.

[36] Kaur, H., Singh, S. P., Garza-Reyes, J. A., and Mishra, N. (2018). “Sustainable stochastic production and procurement problem for resilient supply chain”, Computers & Industrial Engineering. vol. 139: 105508

[37] Hutchins, M. J. and Sutherland, J. W. (2008). “An exploration of measures of social sustainability and their application to supply chain decisions”, Journal of Cleaner Production, vol. 16, no. 15: 1688-1698.

[38] Mani, V., Gunasekaran, A. and Delgado, C. (2018). “Enhancing supply chain performance through supplier social sustainability: An emerging economy perspective”, International Journal of Production Economics, vol. 195: 259-272.

[39] Mirzapour, Al-E-Hashem, S., Malekly, H. and Aryanezhad, M. (2011). “A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty”, International Journal of Production Economics, vol. 134, no. 1: 28-42.

[40] Gholamian, N., Mahdavi, I., Tavakkoli-Moghaddam R. and Mahdavi-Amiri, N. (2015). “Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty”, Applied soft computing, vol. 37: 585-607.

216

[41] Song, H., Huang H.-C. (2008). “A successive convex approximation method for multistage workforce capacity planning problem with turnover”, European Journal of Operational Research, vol. 188, no. 1: 29-48.

[42] Ibaraki, T. K., N. (1988). “Resource allocation problems: algorithmic approaches”, MIT press.

[43] H. J. H. (1975). “Adaptation in natural and artificial systems: an introductoryanalysis with applications to biology, control, and artificial intelligence”, USA:University of Michigan.

[44] Holland, G. D., J. (1989). “Genetic algorithms in search, optimization andmachine learning”, Reading, MA: Addison-Wesley, vol. 44, no. 4: 397-406.

[45] Devika, A. J. K., Nourbakhsh, V. (2014). “Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques”, European Journal of Operational Research, vol, 235, no. 3: 594-615.

[46] Saman Hassanzadeh Amin, G. Z. (2013). “A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return”, Applied Mathematical Modelling. vol. 37, no. 6: 4165-4176.