نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی‌ارشد مهندسی‌صنایع،دانشکده‌صنایع،واحد‌ ‌قزوین،دانشگاه آزاد اسلامی،قزوین‌،ایران

2 دانشیار‌گروه مهندسی‌صنایع،دانشکده‌صنایع،واحد‌ ‌قزوین،دانشگاه آزاد اسلامی،قزوین‌،ایران

چکیده

ذخیره‌سازی و چیدمان صحیح محصولات در انبار، باعث افزایش کارایی در پاسخگویی به درخواست ها، تسریع در شناسایی محصولات، افزایش قابلیت دسترسی به اقلام موجود در انبار، استفاده بیشتر از فضای موجود در انبار، تعیین موقعیت محصولات در انبار و آسیب ندیدن آن‌ها، فراهم آمدن حداکثر انعطلاف پذیری و شرایط مطلوب انبارداری می شود. با بررسی مطالعاتی که در حوزه انبارداری و چیدمان محصولات در انبار صورت گرفته و قبل از چیدمان محصولات در انبار، به صورت جامع و فراگیر با توجه به ویژگی کالاها،دسته بندی روی آنان صورت نگرفته است. لذا در این مقاله با استفاده از الگوریتم های یادگیری ماشین و داده کاوی با توجه به ویژگی‌هایی که برای کالاها در انبار کارخانه تولیدی - صنعتی فراسان در نظر گرفته شده است به دسته بندی محصولات پرداخته و سپس به چیدمان محصولات در انبار با استفاده از مدل برنامه ریزی ریاضی پرداختیم. هدف از مسئله مورد بررسی در حوزه انبارداری و چیدمان محصولات،علاوه بر دسته بندی محصولات بر اساس ویژگی‌های آن‌ها ،کمینه کردن تابع هزینه به دست آمده نسبت به مدل برنامه‌ریزی‌ریاضی می‌باشد.از این رو برای دسته بندی کالاها از الگوریتم مبتنیبر‌چگالی (DB ) ، شبکه عصبی نگاشت خودسازمان ده (SOM ) و روش خوشه بندی سلسله مراتبی (AGNES) استفاده شده است. نتایج به دست آمده نشان می‌دهد که شبکه SOM،عملکرد بهتری نسبت به الگوریتم مبتنی بر چگالی دارد و همچنین الگوریتم مبتنی بر چگالی عملکرد بهتری نسبت به روش خوشه بندی سلسله مراتبی دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting an improved heuristic algorithm for the storage space allocation problem under a dedicated storage policy

نویسندگان [English]

  • Mohsen Kochaki 1
  • Behnam Vahdani 2

1 Master student,Department of Industrial engineering,Qazvin Branch,Islamic Azad University,Qazvin,Iran.

2 Associate Prof,Department of Industrial Engineering,Qazvin Branch, Islamic Azad University, Qazvin, Iran

چکیده [English]

The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.
Introduction
Warehouses play a crucial role in every supply chain that involves activities such as receiving, storing, picking, and transporting goods. The way goods are stored directly affects the costs associated with warehousing, so it is important to have efficient management systems in place in order to stay competitive in the global market. Having an organized warehouse layout, utilizing technology for inventory management, and implementing streamlined processes can all contribute to reducing costs and increasing efficiency in warehousing operations. By continuously optimizing operations and staying up-to-date with industry trends, businesses can ensure they are meeting customer demands and staying ahead of the competition (Jinxiang Gu et al., 2007). Storage is the primary and essential function in all warehouses. The methods used for storing items can vary depending on the type of warehouse and its specific goals and objectives (Berman, 1996). The main goal of storage and warehouses is to meet the needs of consumers or enhance service in a manner that takes into account limitations in resources. Efficient management of storage also helps to enhance the speed and reliability of deliveries, which has been identified as a crucial factor for performance in the last twenty years (Ann E. Gary et al., 1992). When looking at logistics costs from an economic perspective, the costs associated with storage and warehousing services make up around 15% of the total logistics costs in developed countries like Germany (Handfield et al., 2013). In this context, properly allocating storage can reduce costs. After deciding how to store the goods, we determine their arrangement. The purpose of this article is to determine the optimal arrangement of goods in the dedicated storage system. Arranging the goods logically in the warehouse increases efficiency in responding to requests, accelerates goods identification, increases accessibility, makes better use of space, determines the location of goods and protects them. It also provides more flexibility and more suitable conditions for storage. It should be noted that due to the functional nature of warehouses, which requires rapid response to determine optimal goods placement, innovative solutions are imperative. All algorithms proposed to solve organizing goods in warehouses must completely consider inclusiveness according to attributes like grouping, similarity, flammability, degradability, inbound/outbound amounts, and stockroom area. Therefore, according to the huge volume and diversity of data in these systems, utilizing data extraction strategies can maximize efficiency of mathematical planning models whose inputs include inbound/outbound amounts for each good and stockroom area assigned. This confirms arrangements account for qualities like item classes, quantities, traits, and warehouse restrictions. Usually, algorithms presented by these methods typically have some limitations. For example, you could reference the inventory of products stocked in your warehouse. A useful way to enhance or address existing issues is through the use of data-driven and machine learning techniques. In this work, we aim to improve an innovative algorithm described in prior studies using data-focused and collaborative learning approaches. Next, we will provide a brief overview of the framework. Then, the problem definition and mathematical model are described. Following, the methods and analyses employed and findings obtained are examined. After, the effect of the algorithm on performance metrics is assessed. Later, applications of machine learning methods for inventory are explained. Finally, results and recommendations are presented.
Method
According to the items found in the storage facility, nine characteristics for goods were identified, such as group one, group two, similarity, combustion, combustible, corruption, violation, the quantity of goods entering and leaving the warehouse, and storage space extracted. Subsequently, 17 warehouse performance indicators were used to calculate the cost function through a mathematical programming model, analyzing 55 different scenarios. The commodities were then classified using machine learning algorithms SOM, DBSCAN, and AGNES, based on the identified characteristics and inventory performance indicators, with the cost function calculated for each algorithm. Finally, a comparison was conducted between inventory performance indicators and the cost function using the mathematical planning model and the suggested algorithm, with performance evaluated through statistical tests like the Levene test, Kruskal Wallis test, and the Brown for Syte test.
Results
Based on the inventory of 2800 different types of products in the warehouse of Farasan Industrial and Manufacturing Plant, characteristics were extracted for each product. Additionally, warehouse performance indicators and cost functions were analyzed using mathematical programming models and machine learning algorithms. The performance of three algorithms was compared with a mathematical algorithm through statistical tests such as Levene's test, Kruskal-Wallis test, and Brown-Forsythe test. The results showed that the SOM neural network was more efficient than the other two algorithms. Thus, by combining mathematical programming models and machine learning algorithms, one can improve warehouse performance and reduce costs, providing optimal solutions for factory inventory management.
Conclusion
In previous research, it was found that products were stored in warehouses without any prior processing. This created a gap in the field, highlighting the importance of categorizing similar goods before storing them in warehouses to reduce storage costs for factories and manufacturing companies. To address this issue, a sophisticated algorithm was developed to enhance product quality in warehouses across all industries. Reducing storage costs is a common objective for companies and factories, influenced by various factors in their environments. This research focused on developing a model for keeping products in warehouses by considering factors such as product diversity. This study used DBSCAN, AGNES, and SOM algorithms to classify products based on 9 features extracted from the products, which resulted in 55 different classification modes with each of the machine learning algorithms. The development of this algorithm aimed to provide factory and warehouse managers with a solution for making more effective decisions in arranging warehouse products.

کلیدواژه‌ها [English]

  • Warehouse
  • Layout
  • Machine Learning
  • Storage space allocation
  1. زنجیرابی فراهانی، رضا و عسگر، نسرین (1384) «انبارداری و ذخیره‌سازی»، تهران، دانشگاه صنعتی امیرکبیر.
  2. علیمی، حسینعلی (1380), «مدیریت انبار و عملیات مرتبط با سیستم‌های انبارداری»، تهران، سازمان مدیریت صنعتی.
  3. ALONSO-AYUSO, A., TIRADO, G. & UDÍAS, Á. (2013). On a selection and scheduling problem in automatic storage and retrieval warehouses. International Journal of Production Research, 51, 5337-5353 https://doi.org/10.1080/00207543.2013.813984
  4. International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1-7). IEEE
  5. ARABANI, A. B. & FARAHANI, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62, 408-420.https://doi.org/10.1016/j.cie.2011.09.018
  6. BALLESTÍN, F., PÉREZ, Á. & QUINTANILLA, S. (2020). A multistage heuristic for storage and retrieval problems in a warehouse with random storage. International Transactions in Operational Research, 27, 1699-1728 // https://doi.org/10.1111/itor.12454
  7. BERMAN, B. (1996). Marketing channels, John Wiley & Sons In CORPORATION, T. C. 1999. Introduction to data mining and knowledge discovery, Two Crows
  8. BHARATI, M. & RAMAGERI, M. (2010). Data mining techniques and applications MUHAMEDYEV, R. 2015. Machine learning methods: An overview. Computer modelling & new technologies, 19, 14-29.
  9. ÇELIK, M., ARCHETTI, C. & SÜRAL, H. (2021). Inventory routing in a warehouse: The storage replenishment routing problem. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.11.056
  10. LI, L. & CHEN, Z. Hungarian-based Heuristics for Single-machine Flow-Rack AS/RS with Determined Storage and Retrieval Locations. Proceedings of the 3rd International Conference on Computer Science and Application Engineering, (2019). 1-7. https://doi.org/10.1145/3331453.3361669
  11. DUPONT, L. Warehouse location problem with concave costs: heuristics and exact method. The Proceedings of the Multiconference on" Computational Engineering in Systems Applications", (2006). IEEE, 1341-1346. https://doi.org10.1109/CESA.2006.4281845
  12. GASTWIRTH, J. L., GEL, Y. R. & MIAO, W. (2009). The impact of Levene’s test of equality of variances on statistical theory and practice. Statistical Science, 24, 343-360.
  13. GOETSCHALCKX, M. & RATLIFF, H. D. (1990). Shared storage policies based on the duration stay of unit loads. Management Science, 36, 1120-1132. https://doi.org/10.1287/mnsc.36.9.1120
  14. KALFAKAKOU, R., KATSAVOUNIS, S. & TSOUROS, K. (2003). Minimum number of warehouses for storing simultaneously compatible products. International Journal of Production Economics, 81, 559-564. https://doi.org/10.1016/S0925-5273(02)00368-7
  15. LI, L. & CHEN, Z. Hungarian-based Heuristics for Single-machine Flow-Rack AS/RS with Determined Storage and Retrieval Locations. Proceedings of the 3rd International Conference on Computer Science and Application Engineering, (2019). 1-7.https://doi.org/10.1109/ACCESS.2023.3246518
  16. LU, W., MCFARLANE, D., GIANNIKAS, V. & ZHANG, Q. (2016). An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research, 248, 107-122. https://doi.org/10.1016/j.ejor.2015.06.074
  17. LU, Y., SUN, Y., XU, G. & LIU, G. A grid-based clustering algorithm for high-dimensional data streams. International Conference on Advanced Data Mining and Applications, (2005). Springer, 824-831. https://doi.org/10.1007/11527503_97
  18. HAN, J. & KAMBER, M. (2001). Data mining: concepts and techniques. 1st edn San Diego. CA: Academic Press.
  19. HAND, D. J., MANNILA, H. & SMYTH, P. (2001). Principles of data mining (adaptive computation and machine learning), MIT Press..
  20. MATZLIACH, B. & TZUR, M. (2000). Storage management of items in two levels of availability. European Journal of Operational Research, 121, 363-379. 10.1016/S0377-2217(99)00037-5
  21. MCKIGHT, P. E. & NAJAB, J. (2010). Kruskal‐wallis test. The corsini encyclopedia of psychology, 1-1. https://doi.org/10.1002/9780470479216.corpsy0491
  22. MEZGHANI, S. & FRIKHA, A. (2012). A heuristic approach to the warehouse management problem: a real case study. International Journal of Logistics Systems and Management, 13, 342-357. https://doi.org/10.1504/IJLSM.2012.049702.
  23. MITCHELL, T. M. (2006). The discipline of machine learning, Carnegie Mellon University, School of Computer Science, Machine Learning
  24. MIRZA, S., MITTAL, S. & ZAMAN, M. (2016). A review of data mining literature. International Journal of Computer Science and Information Security (IJCSIS), 14, 437-442.
  25. NISHI, T. & KONISHI, M. (2010). An optimisation model and its effective beam search heuristics for floor-storage warehousing systems. International Journal of Production Research, 48, 1947-1966. https://doi.org/10.1080/00207540802603767
  26. PALMER, A., JIMÉNEZ, R. & GERVILLA, E. (2011). Data mining: Machine learning and statistical techniques. Knowledge-Oriented Applications in Data Mining, Prof. Kimito Funatsu (Ed.), 373-396. https://doi.org/10.5772/13621
  27. QIU, R., SUN, Y. & SUN, M. (2022). A robust optimization approach for multi-product inventory management in a dual-channel warehouse under demand uncertainties. Omega, 102591. https://doi.org/10.1016/j.omega.2021.102591
  28. QUINTANILLA, S., PÉREZ, Á., BALLESTÍN, F. & LINO, P. (2015). Heuristic algorithms for a storage location assignment problem in a chaotic warehouse. Engineering Optimization, 47, 1405-1422. https://doi.org/10.1080/0305215X.2014.969727.
  29. REVILLOT-NARVÁEZ, D., PÉREZ-GALARCE, F. & ÁLVAREZ-MIRANDA, E. (2019). Optimising the storage assignment and order-picking for the compact drive-in storage system. International Journal of Production Research, 1-21http://doi.org/10.1080/00207543.2019.1687951
  30. ROTH, A. J. (1983). Robust trend tests derived and simulated: Analogs of the Welch and Brown-Forsythe tests. Journal of the American Statistical Association, 78, 972-980. http://doi.org /10.1080/01621459.1983.10477048
  31. SEYEDI, I., HAMEDI, M. & TAVAKKOLI-MOGHADDAM, R. (2019). Truck Schedulin Cross-Docking Terminal by Using Novel Robust Heuristics. International Journal of Engineering, 32, 296-305.http://doi.org/10.5829/ije.2019.32.02b.15.
  32. SHAKERI, M., LOW, M. Y. H., TURNER, S. J. & LEE, E. W. (2012). A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock. Computers & Operations Research, 39, 2564-2577. https://doi.org/10.1016/j.cor.2012.01.002.
  33. SHAPIRO, S. S. & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591-611. https://doi.org/10.2307/2333709
  34. Heuristic solutions for transshipment problems in a multiple door cross docking warehouse. Computers & Industrial Engineering, 61, 402-408. https://doi.org/10.(1016)/j.cie.2010.09.010
  35. SUKHOV, P., BATSYN, M. & TERENTEV, P. A Dynamic Programming Heuristic for Optimizing Slot Sizes in a Warehouse. ITQM, (2014). 773-777. https://doi.org/10.1016/j.procs.2014.05.327
  36. TOOTKALEH, S. R., GHOMI, S. F. & SAJADIEH, M. Tootkaleh, S. R., Ghomi, S. F., & Sajadieh, M. S. (2016). Cross dock scheduling with fixed outbound trucks departure times under substitution condition. Computers & industrial engineering, 92, 50-56. https://doi.org/10.1016/j.cie.2015.12.005‏
  37. TAN, P.-N., STEINBACH, M. & KUMAR, V. (2013). Data mining cluster analysis: basic concepts and algorithms. Introduction to data mining, 487, 533
  38. VAN DEN BERG, J. P. & GADEMANN, A. (2000). Simulation study of an automated storage/retrieval system. International Journal of Production Research, 38, 1339-1356. https://doi.org/10.1080/002075400188889
  39. VELICKOV, S. & SOLOMATINE, D. Predictive data mining: practical examples. 2nd Joint Workshop on Applied AI in Civil Engineering, (2000).
  40. WAUTERS, T., VILLA, F., CHRISTIAENS, J., ALVAREZ-VALDES, R. & BERGHE, G. V. 2016. A decomposition approach to dual shuttle automated storage and retrieval systems. Computers & Industrial Engineering, 101, 325-33. https://doi.org/10.1016/j.cie.2016.09.013
  41. WITT, A. & VOß, S. (2007). Simple heuristics for scheduling with limited intermediate storage. Computers & Operations Research, 34, 2293-2309. https://doi.org/10.1016/j.cor.2005.09.004
  42. XIAO, J. & ZHENG, L. (2010). A correlated storage location assignment problem in a single-block-multi-aisles warehouse considering BOM information. International Journal of Production Research, 48, 1321-1338. https://doi.org/10.1080/00207540802555736
  43. YANG, D., WU, Y. & MA, W. (2021). Optimization of storage location assignment in automated warehouse. Microprocessors and Microsystems, 80, 103356. https://doi.org/10.1016/j.micpro.2020.103356
  44. ZAERPOUR, N., YU, Y. & DE KOSTER, R. B. 2015. Storing fresh produce for fast retrieval in an automated compact cross‐dock system. Production and Operations Management, 24, 1266-1284. https://doi.org/10.1111/poms.1232
  45. ZHANG, G., SHANG, X., ALAWNEH, F., YANG, Y. & NISHI, T. (2021). Integrated production planning and warehouse storage assignment problem: An IoT assisted case. International Journal of Production Economics, 234, 108058. https://doi.org/10.1016/j.ijpe.2021.108058
  46. Faveto, A., Traini, E., Bruno, G., & Chiabert, P. (2024). based method for evaluating key performance indicators: an application on warehouse system. The International Journal of Advanced Manufacturing Technology, 130(1), 297-310.http://doi.org/ 10.1007/s00170-023-12684-4‏
  47. Do, E., Kim, M., Ko, D. Y., Lee, M., Lee, C., & Ku, K. M. (2024). Machine learning for storage duration based on volatile organic compounds emitted from'Jukhyang'and'Merry Queen'strawberries during post-harvest storage. Postharvest Biology and Technology, 211, 112808. https://doi.org/10.1016/j.postharvbio.2024.112808‏
  48. Kaynov, I. (2021). Deep Reinforcement Learning for Asymmetric One-Warehouse Multi-Retailer Inventory Management. https://doi.org/10.1016/j.ijpe.2023.109088
  49. Tokat, S., Karagul, K., Sahin, Y., & Aydemir, E. (2022). Fuzzy c-means clustering-based key performance indicator design for warehouse loading operations. Journal of King Saud University-Computer and Information Sciences, 34(8), 6377-6384. https://doi.org/10.1016/j.jksuci.2021.08.003
  50. Li, Y., Wang, H., Bai, K., & Chen, S. (2021). Dynamic intelligent risk assessment of hazardous chemical warehouse fire based on electrostatic discharge method and improved support vector machine. Process Safety and Environmental Protection, 145, 425-434.http://doi.org/ 10.1016/j.psep.2020.11.012
  51. Karder, J., Beham, A., Werth, B., Wagner, S., & Affenzeller, M. (2022). Integrated Machine Learning in Open-Ended Crane Scheduling: Learning Movement Speeds and Service Times. Procedia Computer Science, 200, 1031-1040. https://doi.org/10.1016/j.procs.2022.01.302‏
  52. Giner, J., Katic, D., Kovacs, K., Glawar, R., & Sihn, W. (2023). A computer vision based approach to reduce system downtimes in an automated high-rack logistics warehouse. Procedia CIRP, 118, 1078-1083. https://doi.org/10.1016/j.procir.2023.06.185
  53. Voća, N., Pezo, L., Jukić, Ž., Lončar, B., Šuput, D., & Krička, T. (2022). Estimationof the storage properties of rapeseeds using an artificial neural network. Industrial Crops and Products, 187, 115358. https://doi.org/10.1016/j.indcrop.2022.115358
  54. adier, A. L., & Alpan, G. (2013, October). Scheduling truck arrivals and departures in a crossdock: Earliness, tardiness and storage policies. In Proceedings of 2013