مهندسی سیستم و بهره‌وری

مهندسی سیستم و بهره‌وری

برنامه‌ریزی تولید داده‌محور زیست‌دارو با پیش‌بینی تقاضا مبتنی بر LSTM و بهینه‌سازی چندهدفه با الگوریتم ژنتیک و روش محدودیت اپسیلون

نوع مقاله : پژوهشی

نویسندگان
1 نویسنده مسئول: دانشجوی دکتری، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران
2 استاد، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران
3 دانشیار، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران
چکیده
در مواجهه با چالش‌های پیچیده صنعت زیست‌دارو نظیر نوسانات تقاضا، فرآیندهای زیستی حساس، الزامات سختگیرانه کیفیت و اهداف پایداری، توسعه مدل‌های هوشمند داده‌محور برای برنامه‌ریزی تولید امری حیاتی تلقی می‌شود. پژوهش حاضر یک مدل جامع و ترکیبی را با هدف بهینه‌سازی هم‌زمان سودآوری اقتصادی و پایداری زیست‌محیطی ارائه می‌کند. در این راستا، تقاضای دارویی با استفاده از شبکه عصبی حافظه طولانی کوتاه‌مدت (LSTM) پیش‌بینی گردید؛ مدلی که توانایی یادگیری وابستگی‌های پیچیده زمانی را داراست. دقت پیش‌بینی مدل برای ۹ داروی منتخب در سه سناریوی تقاضا (پایین، متوسط، بالا) مورد ارزیابی قرار گرفت و نتایج نشان‌دهنده هم‌راستایی بالا با مقادیر واقعی و پایداری عملکرد بود. سپس یک مدل ریاضی چند‌هدفه خطی عدد صحیح با لحاظ اهداف متضاد اقتصادی-زیست‌محیطی طراحی و با استفاده از الگوریتم ژنتیک (GA) و روش محدودیت اپسیلون (ε-Constraint) حل گردید. مدل پیشنهادی با داده‌های واقعی از شرکت‌های دارویی ایران اعتبارسنجی شد و نتایج بیانگر توان بالای رویکرد پیشنهادی در ایجاد توازن میان سود و آلودگی، مدیریت منابع، و تصمیم‌سازی بهینه در شرایط عدم‌قطعیت می‌باشد.

تازه های تحقیق

  • دستیابی به دقت پیش‌بینی بالا توسط مدل LSTM برای تقاضای دارو تحت سه سناریوی مختلف با استفاده از داده‌های واقعی شرکت‌های ایرانی
  • کاربرد موفق روش محدودیت اپسیلون برای استخراج جبهه پارتو و تحلیل مصالحه بین دو هدف متضاد حداکثرسازی سود و حداقل‌سازی آلایندگی

کلیدواژه‌ها
موضوعات

عنوان مقاله English

Data-driven Production Planning in the Biopharmaceutical Industry Using LSTM-Based Demand Forecasting and Multi-Objective Optimization via Genetic Algorithm and ε-Constraint Method

نویسندگان English

Seyyed Ghasem Salimi-Zaviyeh 1
Abolfazl Kazazi 2
Iman Raeesi Vanani 3
Soroush Ghazinoori 2
1 Corresponding author: Ph.D. Student, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
2 Professor, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
3 Associate Professor, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran
چکیده English

In confronting the complex challenges of the biopharmaceutical industry, such as demand fluctuations, sensitive biological processes, stringent quality requirements, and sustainability objectives, the development of intelligent data-driven models for production planning is deemed essential. The present study introduces a comprehensive hybrid model aimed at simultaneously optimizing economic profitability and environmental sustainability. To this end, pharmaceutical demand was forecasted using Long Short-Term Memory (LSTM) neural networks—a model capable of learning intricate temporal dependencies. The predictive accuracy of the model was evaluated for 9 selected drugs across three demand scenarios (low, medium, high), with results demonstrating high alignment with actual values and robust performance stability. Subsequently, a multi-objective mixed-integer linear programming (MILP) model incorporating conflicting economic-environmental objectives was designed and solved utilizing Genetic Algorithm (GA) and the ε-Constraint method. The proposed model was validated using real data from Iranian pharmaceutical companies, and the findings underscore the superior capability of the suggested approach in achieving a balance between profit and pollution, resource management, and optimal decision-making under uncertainty conditions.

کلیدواژه‌ها English

Data-Driven Production Planning
LSTM Neural Network
Genetic Algorithm (GA)
&‌‌‌epsilon
-Constraint Method
Multi-Objective Optimization

Copyright © Seyyed Ghasem Salimi-Zaviyeh, Abolfazl Kazazi, Iman Raeesi Vanani, Soroush Ghazinoori

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Ahmed, M. M., Salauddin Iqbal, S. M., Priyanka, T. J., Arani, M., Momenitabar, M., & Billal, M. M. (2020). An environmentally sustainable closed-loop supply chain network design under uncertainty: application of optimization. In International Online Conference on Intelligent Decision Science (pp. 343-358). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-66501-2_28
Avazpour, M., Zarei, J., & Alinezhad, E. (2025). Evaluation and prioritization of electricity generation technologies in Iran using a multi-criteria decision-making approach. System Engineering and Productivity, 5(3), 179-198. https://doi.org/10.22034/sep.2025.2063697.1333
Bahrami, M. R., Hashemzadeh, G. R., Shahmansoury, A., & Fathi Hafshejani, K. (2025). Analyzing effective components in industry 4.0 maturity for Iranian banking. System Engineering and Productivity, 5(1), 21-50. https://doi.org/10.22034/sep.2025.2047848.1246
Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
Dong, Y., Yang, T., Xing, Y., Du, J., & Meng, Q. (2023). Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes. Processes, 11(7), 2096. https://doi.org/10.3390/pr11072096
Eghbal, F., Ehsanifar, M., Mirhosseini, M., & Mazaheri, H. (2025). Identification and modeling of key factors significant to the financial performance of Iranian construction companies. System Engineering and Productivity, 4(4), 77-94. https://doi.org/10.22034/msb.2024.2034092.1218
Ehrgott, M. (2005). Multicriteria Optimization. Springer. https://doi.org/10.1007/3-540-27659-9
Emami, H., Radfar, R., & Emami, F. (2018). Commercialization modeling and processes in pharmaceutical industry: A case study of presenting an evaluation pattern using dynamic programming model. Hakim, 21(3), 211-220 (In Persian).
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
Fani, V., Antomarioni, S., Bandinelli, R., & Bevilacqua, M. (2023). Data-driven decision support tool for production planning: a framework combining association rules and simulation. Computers in Industry, 144, 103800. https://doi.org/10.1016/j.compind.2022.103800
Gen, M., & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization. John Wiley & Sons. https://doi.org/10.1002/9780470172261
Ghasemi, A., Farajzadeh, F., Heavey, C., Fowler, J., & Papadopoulos, C. T. (2024). Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap. Journal of Industrial Information Integration, 39, 100599. https://doi.org/10.1016/j.jii.2024.100599
Gholamian, S. A. (2024). Evaluation and selection of sustainable suppliers by providing a decision support system based on a new data envelopment analysis model and cumulative star utility. System Engineering and Productivity, 4(1), 1-13. https://doi.org/10.22034/msb.2024.2025845.1198
Grand View Research. (2023). Biopharmaceuticals Market Size, Share & Trends Analysis Report.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press. https://doi.org/10.7551/mitpress/1090.001.0001
Hong, M. S., Severson, K. A., Jiang, M., Lu, A. E., Love, J. C., & Braatz, R. D. (2018). Challenges and opportunities in biopharmaceutical manufacturing control. Computers and Chemical Engineering, 110, 106-114. https://doi.org/10.1016/j.compchemeng.2017.12.007
Ivanov, D., & Dolgui, A. (2021). Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319(1), 1411–1431. https://doi.org/10.1007/s10479-020-03640-6
Javaid, W., & Ullah, S. (2025). Data driven simulation-based optimization model for job-shop production planning and scheduling: an application in a digital twin shop floor. Journal of Simulation, 1–15. https://doi.org/10.1080/17477778.2025.2469687
Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017). LSTM Fully Convolutional Networks for Time Series Classification. IEEE Access, 6, 1662–1669. https://doi.org/10.1109/ACCESS.2017.2779939
Kashanian Monfared, N., Safaie, N., & Hosseininezhad, S. J. (2025). A decision-making model for the problem of designing the layout of medical centers considering uncertainty. System Engineering and Productivity, 5(2), 97-118. https://doi.org/10.22034/sep.2025.2049327.1252
Khaled, M. S., Shaban, I. A., Karam, A., Hussain, M., Zahran, I., & Hussein, M. (2022). An Analysis of Research Trends in the Sustainability of Production Planning. Energies, 15(2), 483. https://doi.org/10.3390/en15020483
Larizadeh, R., & Tosarkani, B. M. (2025). A novel data-driven rolling horizon production planning approach for the plastic industry under the uncertainty of demand and recycling rate. Expert Systems with Applications, 263, 125728. https://doi.org/10.1016/j.eswa.2024.125728
Lee, J., Bagheri, B., & Kao, H.-A. (2015). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38–41. https://doi.org/10.1016/j.mfglet.2013.09.005
Li, J., Ye, H., Lu, R., Zou, X., & Dong, H. (2024). An integrated data-driven modeling and gas emission constraints for large-scale refinery production planning framework. Process Safety and Environmental Protection, 182, 109-126. https://doi.org/10.1016/j.psep.2023.11.056
Luo, D., Guan, Z., Ding, L., Fang, W., & Zhu, H. (2025). A Data-Driven Methodology for Hierarchical Production Planning with LSTM-Q Network-Based Demand Forecast. Symmetry, 17(5), 655. https://doi.org/10.3390/sym17050655
Malhotra, P., Vig, L., Shroff, G. M., & Agarwal, P. (2015). Long Short-Term Memory Networks for Anomaly Detection in Time Series. In Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 89-94).
Mansouri Mosloo, F., Amiri, M., Taghovifard, M. T., & Haji Aghaei Keshtali, M. (2024). Design and planning of bioethanol supply chain network with a hybrid approach of robust data-driven optimization under discrete uncertainty sets. Decision Making and Operations Research, 9(2), 327-352. https://doi.org/10.22105/dmor.2024.461901.1849  
Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26, 369–395. https://doi.org/10.1007/s00158-003-0368-6
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455–465. https://doi.org/10.1016/j.amc.2009.03.037
Ning, C., & You, F. (2019). Optimization under Uncertainty in the Era of Big Data and Deep Learning. Computers & Chemical Engineering, 126, 507-525. https://doi.org/10.1016/j.compchemeng.2019.03.034
Papageorgiou, L. G. (2009). Supply chain optimization for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938. https://doi.org/10.1016/j.compchemeng.2009.06.014
Radmehr, M., Abdollahzadeh Sangrudi, H., & Saheb Jamnia, N. (2020). An integrated mathematical model of production and distribution planning for radiopharmaceutical products (Case study: Pars isotope company). Supply Chain Management, 22(67), 80-92 (In Persian). https://dor.isc.ac/dor/DOR:20.1001.1.20089198.1399.22.67.6.5
Rafiei, M., Movahedi Sobhani, F., & Hadji Molana, M. (2023). Sustainable supply chain planning of a pharmaceutical holding using supply hub. Journal of Industrial Management Perspective, 13(1), 241-279 (In Persian). https://doi.org/10.48308/jimp.13.1.241
Rathipriya, R., Abdul Rahman, A. A., Dhamodharavadhani, S., Meero, A., & Yoganandan, G. (2023). Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. Neural Computing and Applications, 35(2), 1945-1957. https://doi.org/10.1007/s00521-022-07889-9
Saldanha-da-Gama, F., & Wang, S. (2024). Data-Driven Robust Production Planning. In Facility Location Under Uncertainty: International Series in Operations Research & Management Science (Vol. 356, pp. 489-501). Springer. https://doi.org/10.1007/978-3-031-55927-3_16
Salmabadi, N., & Beheshtinia, M. A. (2020). A multi objective mathematical model for two-echelon production-inventory-routing problem of pharmaceutical products. Quarterly Journal of Transportation Engineering, 11(4), 793-818 (In Persian). https://doi.org/10.22119/jte.2020.79638
Saymanov, I. (2024). Models, methods and algorithms for monitoring environmental impact on agricultural production. arXiv. https://doi.org/10.48550/arXiv.2402.03346
Sazvar, Z., Tavakoli, M., Ghanavati-Nejad, M., & Nayeri, S. (2022). Sustainable-resilient supplier evaluation for high-consumption drugs during COVID-19 pandemic using a data-driven decision-making approach. Scientia Iranica. https://doi.org/10.24200/sci.2022.59789.6424
Su, X., Zeng, L., Shao, B., & Lin, B. (2025). Data-driven optimization for production planning with multiple demand features. Kybernetes, 54(1), 110-133. https://doi.org/10.1108/K-04-2023-0690
Sun, Q., Li, T., Tan, Y. Q., & Yang, Q. (2020). PlanningVis: A Visual Analytics Approach to Production Planning in Smart Factories. IEEE Transactions on Visualization and Computer Graphics, 26(1), 579-589. https://doi.org/10.1109/TVCG.2019.2934275
Walsh, G. (2018). Biopharmaceuticals: Biochemistry and Biotechnology. Wiley.

مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از 01 آبان 1404

  • تاریخ دریافت 21 مرداد 1404
  • تاریخ بازنگری 23 مهر 1404
  • تاریخ پذیرش 30 مهر 1404
  • تاریخ اولین انتشار 30 مهر 1404
  • تاریخ انتشار 01 آبان 1404