ارائه یک الگوریتم جدید برای یافتن کمینه های موضعی مساله بهینه سازی سیستم های کنترل موجودی چندسطحی با پارامترهای تصادفی
محورهای موضوعی :
مدیریت صنعتی
Fariborz Jolai
1
,
Sayyed Mohammad Reza Davoodi
2
,
Ali Mohaghar
3
,
Mohammad Reza Mehregan
4
1 - Professor, Department of Industrial Engineering, University of Tehran, Tehran, Iran
2 - Ph.D. Student, Department of Industrial Management, Dehaghan Branch, Islamic Azad University, Iran,
3 - Associate Professor,Department of Industrial Management, University of Tehran, Tehran, Iran
4 - Professor, Department of Industrial Management, University of Tehran, Tehran, Iran
تاریخ دریافت : 1396/10/16
تاریخ پذیرش : 1396/10/16
تاریخ انتشار : 1393/10/01
کلید واژه:
supply chain management,
مدیریت زنجیره تأمین,
بهینه سازی بر پایه شبیه سازی,
موجودی چند سطحی,
آزمون فرض‌های آماری,
کمینه موضعی,
Simulation-based Optimization,
Statistical hypotheses tests,
Local Optimization,
چکیده مقاله :
در این مقاله طراحی و مقایسه یک مدل شبیه سازی موجودی چند سطحی، چند محصولی که هر واحد آن از سیاست کنترل موجودی نقطه سفارش مرور مستمر (R,Q) استفاده می‌کند، ارائه می‌شود. مدل توزیع با چندین محصول نهایی وچندین محصول میانی و یک قلم محصول اصلی در نظر گرفته می‌شود این بهینه سازی شامل کمینه سازی تابع هزینه می‌باشد. سطح سرویس دهی واحدها با نرخ پرسازی سنجیده می‌شود که برای هر واحدی از مقدار مفروض کمینه‌ای بیشتر است. در الگوریتم ارائه شده با داشتن یک نقطه‌ی شدنی و موضعیسازی درجه دوم تابع هدف و موضعی سازی خطی قیود حول آن نقطه و استفاده از الگوریتم ژنتیک سعی در رسیدن به نقطه‌ی بهینه موضعی شده است. از آنجا که برآوردهای نقطهای تابع هدف و نرخ های پرسازی به کمک شبیه سازی انجام می‌گیرد از آزمون فرض‌های آمــاری برای بررسی شدنی و بهبود جواب ها استفاده می‌شود. در پایان با یک مثال عددی، الگوریتم روی یک شبکه‌ی سه سطحی پیاده سازی می‌شود. با توجه به این نکته که موضعی سازی خطی حالت خاصی از موضعی سازی درجه دوم است از این رو با اطمینان بیشتری میتوان انتظار داشت نقطه بدست آمده از این الگوریتم شدنی بهتر از حالت موضعی سازی خطی باشد.
چکیده انگلیسی:
The present study aimed to develop and compare a simulated model of multi-product and multi-level inventory systems. The model is developed for the final product, different medium products, and the main product. The main purpose of optimization is to minimize costs function. The servicing level of units is measured through backfilling rate that should be more than a minimum level. In the proposed algorithm, the local optimization was found through the genetic algorithm. Since point estimation of goal function and backfilling rates are done on the simulation in the present study based, the statistical methods were used for investing possibility of solutions. Finally, one example was presented in the three-level network. Because linear localization is an especial form of second-order localization, the difference between goal function and estimated volume was at the minimum level. Undoubtedly, it is expected that the estimated point of this algorithm is better than the estimated point of linear localization.
منابع و مأخذ:
Axsate, R. S. (2006). Inventory control, 2nd edition, New York: Spriner.
Deuermeyer, B. L., Schwarz, L.B. (1981). "A model for the analysis of system service level in warehouse-retailer· distribution systems: the identical retailer case". Presented in: Schwarz, L.B. (1981). Multilevel Production/Inventory Control systems: Theory and Practice, Elsevier science Ltd.
Graves, S. C. (1985). "A Multi-Echelon Inventory Model for a Repairable Item with one-for-one Replenishment". Management science, 31(10): 1247-1256.
Almeder, C., Preusser, M., & Hartl, R. F. (2009).Simulation and optimization of supply chains: alternative Or complementary approaches? OR Spectrum, 31, 95-119.
Amiri, M., Seif barghy, ·M,, Olfat, L., Razavi Hajiagha, S.H. (2012). "Determination of a desirable inventory policy in a three echelon multilayer supply chain with normal demand". International Journal of Industrial Engineering and Production Research, 23(1): 65-72.
Axsater, S. (1990). "Simple Solution Procedure for a Class of Two-Echelon Inventory Problem". Operatians Research, 38(1): 64-69.
Axsater, s. (2002). "Approximate optimization of a two-level distribution inventory system". International Journal of Production Economics, 81-82: 545-553.
Cachon, G.P. (2001). "Exact Evaluation of Batch-ordering Inventory Policies in Two-Echelon supply chains with Periodic Review". Operations Research: 49(1): 79-98.
Chu, Y., You, F., & Wassick, J. M. (2014). Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes. Computers & Chemical Engineering, 60, 277-296.
Chu, Y., You, F., Wass1ck, J.M., & Agarwal, A. (2014).Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method. Computers & chemical Engineering, DOI: 10. 1016 /j . compchemeng. 2014.02.023. With general network structure via agent-based modeling. AIChE Journal, 59, 2884-2906.
Chu,Y., You, F., Wasslck, J.M., & Agarwal.A. (2014). Simulation – based optimization framework for multi – echelon inventory systems under uncertainty. computer & chemical Engineering , 73, 1-16.
Clark, A. J., Scarf, H. (1960). Optimal policies for a multi-echelon inventory problem". Management science, 6(4):475-490.
Gao,J. , Wang, w. D. (2008). "Simulation-based optimization and its application in multi-echelon network stochastic inventory system". 7th International conference on system simulation and scientific computing, 10-12 October, china, Beijing, 1302-1307.
Ghiami, Y., Williams, T., & Wu, Y. (2013). A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints. European Journal of Operational Research.
Ivanov, D., Dolgui, A., & Sokolov, B. (2012). Applicability of optimal control theory to adaptive supply chain planning and scheduling. Annual Reviews in control, 36, 73-84.
Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G., & Eversdyk, D. (2008). Integrated safety stock management for multi-stage supply chains under production capacity constraints. Computers & chemical Engineering,32,2570-2581.
Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G., V. & Eversdyk, D. (2004). A Simulation based optimization approach to supply chain management under demand uncertainty. Computers & chemical Engineering, 28, 2087-2106.
Kochel, P., Nielander, U. (2005). "Simulation-based optimization of multi-echelon inventory systems".International Journal of Production Economics. 93-94(1): 505-513.
Mele, F. D., Guillen, G., Espuna, A., & Puigjaner, L.(2006).A simulation-based optimization framework for parameter optimization of supply-chain networks. Industrial & Engineering chemistry Research, 45, 3133- 3178.
Melouk, S., Freeman, N., Miller, D., Dunning, M.,(2013). Simulation optimization based decision support tool for steel manufacturing. Int. J. Prod. Econ. 141 (1), 269–276.
Nikolopoul, A., & Ierapetritou, M. G. (2012). Hybrid simulation based optimization approach for supply chain management. Computers & chemical Engineering, 47,183-193.
O’Donnell, T., Maguire, L., McIvor, R., & Humphreys, P. (2006). Minimizing the bullwhip effect in a supply chain using genetic algorithms. International Journal of Production Research, 44, 1523–1543.
Pasandideh, S. H. R., Niaki, S. T. A., & Nia, A. R. (2011). A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Systems with Applications, 38, 2708 –2716.
Perea-Lopez, E., Ydstie,B.E., & Grossmann, I.E.(2003). A model predictive control strategy for supply chain optimization. Computers & chemical Engineering, 27, 1201- 1218.
Silva,C.A., Sousa, J.M.C. Runkler, T.A., &Dacosta, J.(2006). Distributed optimization of a logistic system.
Sherbrook, C.C. (1968). "Metric: A Multi- Echelon Technique for Recoverable Item Control". Operations Research, 16(1): 122- 141.14- Schwartz,j.D., Wang, W.L.& Rivera, D.E.(2006). Simulation- based optimization of process control policies for inventory management in supply chains. Automatica, 42, 1311- 1320.
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Axsate, R. S. (2006). Inventory control, 2nd edition, New York: Spriner.
Deuermeyer, B. L., Schwarz, L.B. (1981). "A model for the analysis of system service level in warehouse-retailer· distribution systems: the identical retailer case". Presented in: Schwarz, L.B. (1981). Multilevel Production/Inventory Control systems: Theory and Practice, Elsevier science Ltd.
Graves, S. C. (1985). "A Multi-Echelon Inventory Model for a Repairable Item with one-for-one Replenishment". Management science, 31(10): 1247-1256.
Almeder, C., Preusser, M., & Hartl, R. F. (2009).Simulation and optimization of supply chains: alternative Or complementary approaches? OR Spectrum, 31, 95-119.
Amiri, M., Seif barghy, ·M,, Olfat, L., Razavi Hajiagha, S.H. (2012). "Determination of a desirable inventory policy in a three echelon multilayer supply chain with normal demand". International Journal of Industrial Engineering and Production Research, 23(1): 65-72.
Axsater, S. (1990). "Simple Solution Procedure for a Class of Two-Echelon Inventory Problem". Operatians Research, 38(1): 64-69.
Axsater, s. (2002). "Approximate optimization of a two-level distribution inventory system". International Journal of Production Economics, 81-82: 545-553.
Cachon, G.P. (2001). "Exact Evaluation of Batch-ordering Inventory Policies in Two-Echelon supply chains with Periodic Review". Operations Research: 49(1): 79-98.
Chu, Y., You, F., & Wassick, J. M. (2014). Hybrid method integrating agent-based modeling and heuristic tree search for scheduling of complex batch processes. Computers & Chemical Engineering, 60, 277-296.
Chu, Y., You, F., Wass1ck, J.M., & Agarwal, A. (2014).Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method. Computers & chemical Engineering, DOI: 10. 1016 /j . compchemeng. 2014.02.023. With general network structure via agent-based modeling. AIChE Journal, 59, 2884-2906.
Chu,Y., You, F., Wasslck, J.M., & Agarwal.A. (2014). Simulation – based optimization framework for multi – echelon inventory systems under uncertainty. computer & chemical Engineering , 73, 1-16.
Clark, A. J., Scarf, H. (1960). Optimal policies for a multi-echelon inventory problem". Management science, 6(4):475-490.
Gao,J. , Wang, w. D. (2008). "Simulation-based optimization and its application in multi-echelon network stochastic inventory system". 7th International conference on system simulation and scientific computing, 10-12 October, china, Beijing, 1302-1307.
Ghiami, Y., Williams, T., & Wu, Y. (2013). A two-echelon inventory model for a deteriorating item with stock-dependent demand, partial backlogging and capacity constraints. European Journal of Operational Research.
Ivanov, D., Dolgui, A., & Sokolov, B. (2012). Applicability of optimal control theory to adaptive supply chain planning and scheduling. Annual Reviews in control, 36, 73-84.
Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G., & Eversdyk, D. (2008). Integrated safety stock management for multi-stage supply chains under production capacity constraints. Computers & chemical Engineering,32,2570-2581.
Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G., V. & Eversdyk, D. (2004). A Simulation based optimization approach to supply chain management under demand uncertainty. Computers & chemical Engineering, 28, 2087-2106.
Kochel, P., Nielander, U. (2005). "Simulation-based optimization of multi-echelon inventory systems".International Journal of Production Economics. 93-94(1): 505-513.
Mele, F. D., Guillen, G., Espuna, A., & Puigjaner, L.(2006).A simulation-based optimization framework for parameter optimization of supply-chain networks. Industrial & Engineering chemistry Research, 45, 3133- 3178.
Melouk, S., Freeman, N., Miller, D., Dunning, M.,(2013). Simulation optimization based decision support tool for steel manufacturing. Int. J. Prod. Econ. 141 (1), 269–276.
Nikolopoul, A., & Ierapetritou, M. G. (2012). Hybrid simulation based optimization approach for supply chain management. Computers & chemical Engineering, 47,183-193.
O’Donnell, T., Maguire, L., McIvor, R., & Humphreys, P. (2006). Minimizing the bullwhip effect in a supply chain using genetic algorithms. International Journal of Production Research, 44, 1523–1543.
Pasandideh, S. H. R., Niaki, S. T. A., & Nia, A. R. (2011). A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Systems with Applications, 38, 2708 –2716.
Perea-Lopez, E., Ydstie,B.E., & Grossmann, I.E.(2003). A model predictive control strategy for supply chain optimization. Computers & chemical Engineering, 27, 1201- 1218.
Silva,C.A., Sousa, J.M.C. Runkler, T.A., &Dacosta, J.(2006). Distributed optimization of a logistic system.
Sherbrook, C.C. (1968). "Metric: A Multi- Echelon Technique for Recoverable Item Control". Operations Research, 16(1): 122- 141.14- Schwartz,j.D., Wang, W.L.& Rivera, D.E.(2006). Simulation- based optimization of process control policies for inventory management in supply chains. Automatica, 42, 1311- 1320.
