پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 709 |
| تعداد مقالات | 10,213 |
| تعداد مشاهده مقاله | 71,680,432 |
| تعداد دریافت فایل اصل مقاله | 63,496,573 |
انبارش کالاها در انبار با رویکرد دادهکاوی (مطالعه موردی: شرکت فولاد مبارکه اصفهان) | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره 82، مهر 1404، صفحه 23-35 اصل مقاله (687.51 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.33295.2624 | ||
| نویسندگان | ||
| حسین رئیسی دزکی* ؛ غلامعلی رئیسی اردلی | ||
| دانشکده مهندسی صنایع، دانشگاه صنعتی اصفهان، اصفهان، ایران | ||
| تاریخ دریافت: 26 بهمن 1402، تاریخ بازنگری: 15 شهریور 1403، تاریخ پذیرش: 28 مهر 1403 | ||
| چکیده | ||
| یکی از عوامل اثرگذاز در صنایع تولیدی برای حفظ رقابتپذیری و برآورده کردن انتظارات مشتریان مدیریت بهینه انبار شامل تخصیص محصولات به مکانهای ذخیرهسازی درون انبار می-باشد. چیدمان مناسب میتواند، مدت زمان جمعآوری سفارشات را کاهش داده و منجر به افزایش زمان پاسخگویی به سفارشات در انبار گردد. تمرکز این پژوهش بر موضوع انبارش کالا با استفاده از ابزارهای داده محور میباشد. در این پژوهش، یکی از انبارهای فولاد مبارکه اصفهان به عنوان مطالعه موردی در نظر گرفته شد و فرایند تخصیص کالاها به قفسهها در دو مرحله صورت -پذیرفت. در مرحله اول کالاها با استفاده از الگوریتم k-means به چهار خوشه تقسیم شد و برای افزایش کیفیت خوشهبندی، از دو اقدام: "یک پیش الگوریتم که نقاط اولیه k-means را با پراکندگی بیشتری انتخاب میکند" و "یک الگوریتم ژنتیک که جوابهای خوشهبندی را از بهینگی محلی خارج میکند" استفاده گردید. در مرحله دوم با استفاده از تجربه خبرگان، مکان هر خوشه در انبار مشخص شده است. درادامه بر اساس یک معیار ارجحیت، کالاهای هر خوشه به قفسههای مختلف اختصاص یافته است. چند سفارش به صورت تصادفی انتخاب شده و مجموع فاصلههای کالاهای سفارشات محاسبه شد. نتایج نشان میدهد که مدل پیشنهادی میتواند مجموع فاصلههای کالاها در سفارشات را در انبار به طور متوسط 15 درصد کاهش دهد. | ||
| کلیدواژهها | ||
| دادهکاوی؛ انبار؛ انبارش کالا در انبار؛ خوشه بندی K-means؛ الگوریتم ژنتیک | ||
| عنوان مقاله [English] | ||
| Storage Location Assignment Problem using Data Mining Approach (Case Study: Mobarakeh Steel Company) | ||
| نویسندگان [English] | ||
| Hossein Raesi Dezaki؛ Gholam Ali Reisi Ardali | ||
| Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran | ||
| چکیده [English] | ||
| One of the influential factors in manufacturing industries to maintain competitiveness and meet customer expectations is optimizing warehouse management, including the allocation of products to storage locations within the warehouse. Proper arrangement can reduce order picking time and increase responsiveness to orders in the warehouse. This research focuses on the topic of inventory management using data-driven tools. In this study, one of the warehouses of Mobarakeh Steel in Isfahan was considered as a case study, and the process of allocating products to shelves was carried out in two stages. In the first stage, products were divided into four clusters using the k-means algorithm. To improve the quality of clustering, two steps were taken: "a pre-algorithm that selects initial k-means points with greater dispersion" and "a genetic algorithm that eliminates local optima in cluster solutions." In the second stage, the location of each cluster in the warehouse was determined based on experts experience. Subsequently, based on a preference criterion, products in each cluster were assigned to different shelves. Several orders were randomly selected, and the total distances of the ordered products were calculated. The results show that the proposed model can reduce the total distances of products in orders in the warehouse by an average of 15 percent. | ||
| کلیدواژهها [English] | ||
| Inventory management, Data mining, Clustering, GA | ||
| مراجع | ||
|
[1] Fukuyama, Hirofumi, Hui-hui Liu, Yao-yao Song, and Guo-liang Yang. "Measuring the capacity utilization of the 48 largest iron and steel enterprises in China." European Journal of Operational Research 288, no. 2 (2021): 648-665. [2] Diabat, Ali, Ehsan Dehghani, and Armin Jabbarzadeh. "Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times." Journal of Manufacturing Systems 43 (2017): 139-149. [3] Diwani, Salim A, and Zaipuna O Yonah. "A novel holistic disease prediction tool using best fit data mining techniques." International Journal of Computing and Digital Systems 6, no. 02 (2017): 63-72. [4] Hand, David J. "Principles of data mining." Drug safety 30 (2007): 621-622. [5] Reyes, J, E Solano-Charris, and J Montoya-Torres. "The storage location assignment problem: A literature review." International Journal of Industrial Engineering Computations 10, no. 2 (2019): 199-224. [6] Chuang, Yi-Fei, Hsu-Tung Lee, and Yi-Chuan Lai. "Item-associated cluster assignment model on storage allocation problems." Computers & Industrial Engineering 63, no. 4 (2012): 1171-1177. [7] Fontana, Marcele Elisa, and Cristiano Alexandre Virgínio Cavalcante. "Using the efficient frontier to obtain the best solution for the storage location assignment problem." Mathematical Problems in Engineering 2014. [8] da Silva, Denilson Dimas, Natália Veloso Caldas de Vasconcelos, and Cristiano Alexandre Virginio Cavalcante. "Multicriteria decision model to support the assignment of storage location of products in a warehouse." Mathematical Problems in Engineering 2015. [9] Li, Jiaxi, Mohsen Moghaddam, and Shimon Y Nof. "Dynamic storage assignment with product affinity and ABC classification—a case study." The International Journal of Advanced Manufacturing Technology 84, no. 9 (2016): 2179-2194. [10] Pang, King-Wah, and Hau-Ling Chan. "Data mining-based algorithm for storage location assignment in a randomised warehouse." International Journal of Production Research 55, no. 14 (2017): 4035-4052. [11] Zhang, Ren-Qian, Meng Wang, and Xing Pan. "New model of the storage location assignment problem considering demand correlation pattern." Computers & Industrial Engineering 129 (2019/03/01/ 2019): 210-219. [12] Micale, Rosa, Concetta Manuela La Fata, and Giada La Scalia. "A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem." Computers & Industrial Engineering 135 (2019): 199-210. [13] Lorenc, Augustyn, and Tone Lerher. "PickupSimulo–Prototype of Intelligent Software to Support Warehouse Managers Decisions for Product Allocation Problem." Applied Sciences 10, no. 23 (2020): 8683. [14] Yerlikaya, Mehmet Akif. "Storage Location Assignment with Fuzzy PROMETHEE Method in Warehouse Systems with Uncertain Demand." Journal of the Institute of Electronics and Computer 2, no. 1 (2020): 142-150. [15] Lorenc, Augustyn, Małgorzata Kuźnar, and Tone Lerher. "Solving product allocation problem (PAP) by using ANN and clustering." FME Transactions 49, no. 1 (2021): 206-213. [16] Zhou, Li, Lili Sun, Zhaochan Li, Weipeng Li, Ning Cao, and Russell Higgs. "Study on a storage location strategy based on clustering and association algorithms." Soft Computing 24, no. 8 (2020): 5486-5499. [17] Küçükdeniz, Tarık, and Özlen Erkal Sönmez. "Integrated Warehouse Layout Planning with Fuzzy C-Means Clustering." Paper Presented at the International Conference on Intelligent and Fuzzy Systems, 2022. [18] da Silva, Denilson Dimas, Natália Veloso Caldas de Vasconcelos, and Cristiano Alexandre Virginio Cavalcante. "Multicriteria Decision Model to Support the Assignment of Storage Location of Products in a Warehouse." Mathematical Problems in Engineering 2015. [19] Fontana, Marcele Elisa, and Vilmar Santos Nepomuceno. "Multi-criteria approach for products classification and their storage location assignment." The International Journal of Advanced Manufacturing Technology 88, no. 9 (2017): 3205-3216. [20] Ye, Yanbin, and Chia-Chu Chiang. "A parallel apriori algorithm for frequent itemsets mining." Paper presented at the Fourth International Conference on Software Engineering Research, Management and Applications (SERA'06), 2006. [21] Steinley, Douglas, and Michael J Brusco. "Choosing the number of clusters in Κ-means clustering." Psychological Methods 16, no. 3 (2011): 285-295. | ||
|
آمار تعداد مشاهده مقاله: 554 تعداد دریافت فایل اصل مقاله: 316 |
||