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| تعداد مقالات | 9,691 |
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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 | ||
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