مدل کنترل موجودی با استفاده از منطق فازی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5329||2001||10 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||7 روز بعد از پرداخت||320,400 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||4 روز بعد از پرداخت||640,800 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 73, Issue 3, 13 October 2001, Pages 217–226
A model based on fuzzy logic is proposed for inventory control. The periodic review model of inventory control with variable order quantity is considered. The model takes into account the dynamics of production–inventory system in a control theoretic approach. The control module combines fuzzy logic with proportional-integral-derivative (PID) control algorithm. It simulates the decision support system to maintain the inventory of the finished product at the desired level in spite of variations in demand. The effectiveness of the proposed control model is illustrated using the actual data of a typical packaging organization operating in the Sultanate of Oman.
Control of inventory in production and operations systems is very important for better management and utilization of resources. Techniques based on the principles of linear control theory had previously been proposed in the field of inventory control , ,  and . In a recent work , inventory ordering models had been proposed in terms of control system theory taking into account the non-linear characteristics of the ordering rules. In those models, the current level of inventory was compared with the pre-assigned set value, either periodically at specified time intervals or continuously. The order was triggered as a sequence of impulses whenever the inventory reached the set value (trigger level). The dynamics of the system had been modeled in the form of only finite time-delay. In another recent work , the concept of fuzzy set theory had been applied to inventory control models considering the fuzziness of inputs only. The dynamics of the system was not taken into consideration. In the present work, a fuzzy logic-based model is presented for inventory control in a medium-scale production system. The main objective is to maintain the inventory at a desired level in spite of fluctuations in the demand taking into account the dynamics of the production system. The production process is modeled in the form of a first-order dynamic system with a representative production time constant. The production quota is decided on the basis of the difference (error) between the present and the desired inventory level. The decision process is simulated using fuzzy logic controller (FLC) coupled with a conventional proportional-integral-derivative (PID) controller. In this, the concept of fuzzy precompensation proposed by Kim et al.  has been combined with the improvized PI-type FLC proposed by Lee . The type of FLC, proposed in the present work, has the advantage of fast response with lower overshoot in tracking a varying target inventory level. The method is used to simulate the typical inventory control scenario for finished product. The procedure is illustrated using the data for a typical packaging organization operating in the Sultanate of Oman. The selected company manufactures corrugated paper boxes for packaging.
نتیجه گیری انگلیسی
A procedure of inventory control using fuzzy logic is illustrated using the data for a typical packaging organization operating in the Sultanate of Oman. The model has been used for simulating the inventory control process of finished product. The inventory is maintained reasonably well within the desired level in spite of variations in demand. The present model will be useful as a decision support system in monitoring the inventory situation taking into account the supply and demand scenario. The model is quite general so that it can be used for other similar organizations, with approximate model parameters. However, the data used in the first set of results are kept simple, in the form of annual averages, to illustrate the procedure and to examine the relative performance of different controller settings. The gains and scaling factors have been selected using a number of trials based on simulations. The extension of the procedure for handling more general input data and longer periods of simulations with the automated gain adjustment is also demonstrated. The procedure has a potential for possible extension to an “automated” inventory control system through on-line processing of information on current inventory status and its desired value, in a suitable time scale.