دانلود مقاله ISI انگلیسی شماره 19155
عنوان فارسی مقاله

"تعیین اندازه دسته تولید" موجودی همراه با انتخاب تامین کننده با استفاده از الگوریتم هوشمند هیبریدی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
19155 2008 7 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Inventory lot-sizing with supplier selection using hybrid intelligent algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Applied Soft Computing, Volume 8, Issue 4, September 2008, Pages 1523–1529

کلمات کلیدی
الگوریتم ترکیبی هوشمند - "تعیین اندازه دسته تولید" موجودی - شبکه های عصبی فازی () - الگوریتم ژنتیک () - تحلیل مؤلفه های اصلی () () - () - () -
پیش نمایش مقاله
پیش نمایش مقاله "تعیین اندازه دسته تولید" موجودی همراه با انتخاب تامین کننده با استفاده از الگوریتم هوشمند هیبریدی

چکیده انگلیسی

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.

مقدمه انگلیسی

The multi-period inventory lot-sizing scenario with a single product was introduced by Wagner and Whitin in [1], where a dynamic programming solution algorithm was proposed to obtain feasible solutions to the problem. Soon afterwards, Basnet and Leung [2] developed the multi-period inventory lot-sizing scenario which involves multiple products and suppliers. The model used in these former research works is formed by a single level indicating the type, amount, suppliers and purchasing time of the products. This model, however, is not able to consider the planning of the supply sector of the firm. In addition, the models used in previous works assumed that the rate of production demand is constant. While in practice a mechanism to forecast the rate of such demand is required. In this paper, we introduce a new model for the multi-period inventory lot-sizing problem with supplier selection. We also propose a hybrid intelligent algorithm which is able to plan and control the inventory at different levels depending on the accurate forecasting of different demand rates. Our algorithm is based on a fuzzy neural network (FNN) and a genetic algorithm (GA): the forecasting of the periodical demand rates is done by means of the FNN, and the result of the FNN which are then passed to a GA in order to optimize the planning and inventory controlling models based on which the proper suppliers are selected. The rest of the paper is organized as follows: Section 2 provides a literature review on the current inventory lot-sizing. Our hybrid intelligent algorithm is proposed in Section 3. Section 4 presents the experimental results using real data obtained in one of the biggest supply chain of sewing machines manufacturing in Iran. Finally, Section 5 closes the paper giving some concluding remarks.

نتیجه گیری انگلیسی

The current paper aims at providing a model capable of planning and controlling the inventory in supply chain and unifying the selection of supplier for multi-products, period, suppliers and levels. Additionally, accurate demand forecasting is considered as an important factor especially in production system using push. The given results assert that, compared with ARIMA, FNN can optimize the forecasting which prepares a reliable function to plan and control the inventory. Moreover, the unified model of inventory planning and controlling the supplier selection is able to balance the costs at different levels and select the proper supplier. Experimental results approve that such a model can reduce the costs of the case study by 4%. According to the problems beyond using non-liner classical models, i.e. long-term solution, the paper intends to depend on GA and pattern search, which minimize the amount of objective function by 11.6%.

خرید مقاله
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