برنامه‌ریزی منابع تولیدی در یک سیستم تولیدی هیبرد MTS/MTO تحت تقاضای احتمالی و با بکارگیری رویکرد برنامه‌ریزی تصادفی چندمرحله‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشگاه تهران.

2 استادیار، دانشگاه تهران.

3 دانشیار، دانشگاه تهران.

4 استادیار، دانشگاه خاتم.

چکیده

با توجه به تغییرات گسترده، مداوم و روزافزون در بازارهای جهانی و تقاضای مشتریان، به‌کارگیری ابزارهایی قدرتمند و قابل‌اطمینان برای پشتیبانی از تصمیم‌گیرندگان، اجتناب‌ناپذیر است. به این منظور در پژوهش حاضر، مسئله برنامه‌ریزی تولید در شرایط تقاضای احتمالی و برای یک سیستم تولیدی هیبرید MTS/MTO بررسی شده است. به‌کارگیری این رویکرد، مدل ارائه‌شده را برای یک سیستم تولیدی به‌طور کامل MTO یا MTS نیز سازگار کرده است. به‌طورمعمول برنامه‌ریزی تولیدی در یک بازه میان‌مدت صورت گرفته است و تصمیمات اخذشده در هر مرحله زمانی، در برنامه‌های دوره‌های آتی نیز تأثیرگذار خواهد بود؛ بنابراین در مواجهه با تقاضای احتمالی مشتریان، روش برنامه‌ریزی احتمالی چندمرحله‌ای به‌منظور اخذ تصمیمات با نگرش بر کل افق زمانی پیش‌رو به کار گرفته شده است. هدف مدل ارائه‌شده بیشینه‌سازی سود از طریق به‌کارگیری بهینه ظرفیت‌های تولیدی، سیاست‌های مناسب قیمت‌گذاری محصولات و برنامه‌ریزی احتیاجات مواد اولیه است. در انتها از طریق تحلیل‌های عددی مرسوم در روش برنامه‌ریزی احتمالی، مدل ارائه‌شده موردبررسی قرار گرفت. با توجه به ارزش حل احتمالی به‌دست‌آمده، نتایج نشان می‌دهد به‌کارگیری مدل ارائه‌شده می‌تواند باعث افزایش سطح سودآوری شود که باید در مقابل افزایش پیچیدگی مسئله موردتوجه قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Dynamic Production Planning Model Based on Optimization of a Hybrid Push/Pull System, Considering Demand Uncertainty

نویسندگان [English]

  • Reyhane Azizi Kharanghi 1
  • Hannan Amouzad Mahdiraji 2
  • Mohammadreza Taghizadeh Yazdi 3
  • Seyyed Hossein Razavi Haji Agha 4
1 Ph.D student, University of Tehran.
2 Assistant Professor, University of Tehran.
3 Associate Professor, University of Tehran.
4 Assistant Professor, Khatam University.
چکیده [English]

Given the vast, continuous and increasing changes in global markets and customer demand, the use of powerful and reliable tools to support decision makers is inevitable. For this purpose, in this research, we have tried to address the issue of production planning by considering the system dynamics and uncertainty in customer demand. The production system assumed in this model is a hybrid push-pull production system. By applying this approach, the proposed model is also adapted for a fully push or pull production system. Production planning is usually done in the medium term, and the decisions made at each stage of time will also affect future plans. Therefore, in the face of potential customer demand, the multi-stage stochastic programming method has been used in order to make decisions with a view to the entire time horizon. The purpose of the proposed model is to maximize profits through the optimal use of production capacity, appropriate pricing policies and material resource planning. Finally, the proposed model is examined through conventional numerical analyzes in the stochastic programming method.

کلیدواژه‌ها [English]

  • Hybrid Push/Pull Production System
  • Demand Uncertainty
  • Multi-Stage Stochastic Programming, Available to Promise, Production Planning

مراجع

  1. Aloulou, M.A., Dolgui, A. & Kovalyov, M.Y. (2014). A bibliography of non-deterministic lot-sizing models. International Journal of Production Research, 52(8), 2293-2310.
  2. Babai, M.Z. & Dallery, Y. (2009). Dynamic versus static control policies in single stage production-inventory systems. International Journal of Production Research, 47(2), 415-433.
  3. Charnsirisakskul, K., Griffin, P.M. & Keskinocak P. (2006). Pricing and scheduling decisions with leadtime flexibility. European Journal of Operational Research. 171(1), 153-169.
  4. Chen, C.-T. & Huang, S.-F. (2006). Order-fulfillment ability analysis in the supply-chain system with fuzzy operation times. International Journal of Production Economics, 101(1), 185-193.
  5. Chen, C.Y., Z. Zhao, & M.O. Ball (2002). A Model for Batch Advanced Available‐To‐ Production and Operations Management. 11(4), 424-440.
  6. Cheng, F., et al. (2012). A production–inventory model for a push–pull manufacturing system with capacity and service level constraints. Production and Operations Management, 21(4), 668-681.
  7. Cheng, C.-B. (2008). Solving a sealed-bid reverse auction problem by multiple-criterion decision-making methods. Computers & Mathematics with Applications, 56(12), 3261-3274.
  8. Chen-Ritzo, C.-H., et al. (2011). Component rationing for available-to-promise scheduling in configure-to-order systems. European Journal of Operational Research, 211(1), 57-65.
  9. Croson, R. & Donohue K. (2006). Behavioral causes of the bullwhip effect and the observed value of inventory information. Management science, 52(3), 323-336.
  10. De Oliveira Pacheco, E., S. Cannella, R. Lüders and A. P. Barbosa-Povoa (2017). Order-up-to-level policy update procedure for a supply chain subject to market demand uncertainty. Computers & Industrial Engineering, 113, 347-355.
  11. Dolgui, A. & Ivanov, D. (2020). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International journal of production economics., 229, 107886
  12. Duan, Y., Cao, Y. & Huo, J. (2018). Optimal pricing, production, and inventory for deteriorating items under demand uncertainty: The finite horizon case. Applied Mathematical Modelling, 58, 331-348.
  13. Duenyas, I. & Hopp, W.J. (1995). Quoting customer lead times. Management Science, 41(1), 43-57.
  14. Duran, T. Liu, D. Simchi-Levi and J. L. Swann (2007). Optimal production and inventory policies of priority and price-differentiated customers. IIE Transactions, 39(9), 845-861.
  15. Easton, F.F. & Moodie D.R. (1999). Pricing and lead time decisions for make-to-order firms with contingent orders. European Journal of operational research, 116(2), 305-318.
  16. Ebrahimi Mahmoudi, H., Pishvaei, M.S. & Teymouri, E. (2021). A Two-Stage Model for Rice Cultivation Preparation Considering Dynamic Uncertainty: A Case Study in Iran. The Journal of Industrial Management Perspective, 11(2), 145-176 (In Persian).
  17. Fleischmann, M., Van Nunen, J.A., & Gräve, B. (2003). Integrating closed-loop supply chains and spare-parts management at IBM. Interfaces, 33(6), 44-56.
  18. Ghrayeb, O., Phojanamongkolkij, N., & Tan, B.A. (2009). A hybrid push/pull system in assemble-to-order manufacturing environment. Journal of Intelligent Manufacturing, 20(4), 379-387.
  19. Golmohammadi, A., & Hassini, E. (2019). Capacity, pricing and production under supply and demand uncertainties with an application in agriculture. European Journal of Operational Research, 275(3), 1037-1049.
  20. Heitsch, H., & Romisch, W. (2005). Generation of multivariate scenario trees to model stochasticity in power management. IEEE Russia Power Tech, Pages: 1-7.
  21. Higle, J.L., & Kempf, K.G. (2010). Production planning under supply and demand uncertainty: A stochastic programming approach, in Stochastic Programming, Springer, 297-315.
  22. Hu, Z., & Hu, G. (2018). A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty. Computers & Industrial Engineering, 119, 157-166.
  23. Ivanov, S. Sethi, A. Dolgui and B. Sokolov (2018). A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annual Reviews in Control, 46, 134-147.
  24. Jeong, B., et al. (2002). An available-to-promise system for TFT LCD manufacturing in supply chain. Computers & Industrial Engineering, 43(1), 191-212.
  25. Kim, S.-H., et al. (2012). Improving the push–pull strategy in a serial supply chain by a hybrid push–pull control with multiple pulling points. International Journal of Production Research, 50(19), 5651-5668.
  26. Kira, D., Kusy, M., & Rakita, I. (1997). A stochastic linear programming approach to hierarchical production planning. Journal of the Operational Research Society, 48(2), 207-211.
  27. Mohaghar A., & Talaei, H.R. (2017). Dynamic Modeling Of A New Product Supply Chain Using System Dynamics Approach. Journal of industrial management perspective 2017 Vol. 6 Issue 4, Winter 2017 Pages 9-36 (In Persian).
  28. Mokhtari, G., & Bakhtiari, F. (2020). Robust Optimization of Multi-product and Multi-class Lot-sizing and Supplier Selection with Uncertain Demand. Journal of Industrial Management Perspective 2020 Vol. 10 Issue 4, Winter 2021 Pages 193-225 (In Persian).
  29. Palaka, K., Erlebacher, S., & Kropp, D.H. (1998). Lead-time setting, capacity utilization, and pricing decisions under lead-time dependent demand. IIE transactions, 30(2), 151-163.
  30. Pibernik, R., & Yadav, P. (2009). Inventory reservation and real-time order promising in a make-to-stock system. OR spectrum, 31(1), 281-307.
  31. Puchkova, A., Le Romancer, J., & McFarlane, D. (2016). Balancing push and pull strategies within the production system. IFAC-PapersOnLine, 49(2), 66-71.
  32. Ruszczyński, A., & Shapiro, A. (2003). Stochastic programming models. Handbooks in operations research and management science, 10, 1-64.
  33. T. Talluri, G. Van Ryzin and G. Van Ryzin (2004). The Theory and Practice of Revenue Management. International Series in Operations Research & Management Science, Springer 2004 Vol. 1.
  34. Sawik, T. (2008), Coordinated supply chain scheduling. International Journal of Production Economics. 120(2), 437-451.
  35. Shen, B., Qian, R., & Choi, T.-M. (2017). Selling luxury fashion online with social influences considerations: Demand changes and supply chain coordination. International Journal of Production Economics, 185, 89-99.
  36. So, K.C. & Song, J.-S. (1998). Price, delivery time guarantees and capacity selection. European Journal of operational research, 111(1), 28-49.
  37. Tako, A.A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision support systems, 52(4), 802-815.
  38. Taylor, S.G. & Plenert, G.J. (1999). Finite capacity promising. Production and Inventory Management Journal, 40, 50-56.
  39. Udenio, M., et al. (2017). Behavioral causes of the bullwhip effect: An analysis using linear control theory. Iise Transactions, 49(10), 980-1000.
  40. Wang, X., & Disney, S.M. (2016). The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3), 691-701.
  41. Webster, S. (2002). Dynamic pricing and lead‐time policies for make‐to‐order systems. Decision Sciences, 33(4), 579-600.
  42. Wei, Y., Chen, F., & Xiong, F. (2018). Dynamic complexities in a supply chain system with lateral transshipments. Complexity, 2018.
  43. Wei, Y., Wang, H., & Qi, C. (2013). On the stability and bullwhip effect of a production and inventory control system. International Journal of Production Research, 51(1), 154-171.
  44. Wei, Y., Wang, H., & Qi, C. (2013). The impact of stock-dependent demand on supply chain dynamics. Applied Mathematical Modelling, 37(18-19), 8348-8362.
  45. Whitin, T.M. (1955). Inventory control and price theory. Management science, 2(1), 61-68.
  46. Xiong, S., Feng, Y., & Huang, K. (2020). Optimal MTS and MTO Hybrid Production System for a Single Product under the Cap-And-Trade Environment. Sustainability, 12(6), 2426.
  47. Zhang, J., et al (2021). Which Strategy Is Better for Managing Multi-product Demand Uncertainty: Inventory Substitution or Probabilistic Selling? European Journal of Operational Research, 302(1), 79-95

فایل ها

هم رسانی

ارجاع به این مقاله

آمار