نرم افزار زنجیره تامین از تئوری مجموعه های فازی به مدل کنترل موجودی - تجزیه و تحلیل سیستم DRP

As competition abounds, the efficient solution on inventory control of a DRP’s (Distribution Requirement Planning) supply chain management is a vital success factor for companies in today’s business world. A stochastic program of market distribution and its deterministic equivalent control program is approximated by a multi-echelon lot-sizing model based on “risk inflated effective demands”. The DRP-decomposition of this approximate model, which can be used with allocation application of Fuzzy Set Theory, is then introduced. The aim of this paper is to find methods to address traditional DRP’s weaknesses and to improve the performances of DRP systems. In this paper, the field of continuous review model will be focused in, and a new method on the model with triangular fuzzy numbers (input data) will be presented. By using the method, the maximum of order quantity under a minimum of total cost can be obtained. In many previous research, authors take a precise number approximately as the representative of a fuzzy number. But the precise number can not reflect the property of fuzzy inventory control number fully. Therefore, in a numerical example of this paper, in addition to providing a transformation for reducing a fuzzy number into a closed interval by introducing the interval mean value concept proposed by Dubios and Prude, this fuzzy system can be transformed into a more precise diagnosis system for channel members in the supply chain distribution organization.

1.1. Research motivation DRP (Distribution Requirement Planning) is one of the important subsystems which a modernization meat supply manufacturer adopts to respond to the chain store retail environment to achieve its supply chain management objective (Vollmann, Berry, Whybark, & Jacobs, 2004). In many sectors, the network through which a given item flows takes the form of a tree and, as illustrated in Fig. 1, this tree often has more than two echelons. Typically, the intermediate nodes in the tree are warehouses, the leaves are sales points or consumption points responding to an external demand, and the time required to ship items from node to node is not negligible.Almost research works on inventory control problem from this multi-echelon distribution network are solved by converting vagunese or imprecise input data to crisp one. But, many variables in inventory control process from this supply chain distribution network may truly be fuzzy. Some components of the setup, holding and shortage costs may be unknown with uncertainty problem (Gen, Tsujimura, & Zheng, 1996). In many previous research, authors take a precise number approximately as the representative of a fuzzy number. But the precise number can not reflect the property of fuzzy inventory control number fully. Therefore, in this paper, a transformation for reducing a fuzzy number into a closed interval by introducing the interval mean value concept proposed by Dubios and Prude will be presented. The fuzzy number can be transformed into a closed interval, and possibility theory is used here to obtain a more precise result for above interval. Overall in this paper, the field of continuous review model will be focused in, and a new method on the model with fuzzy input data will be presented. By using the method, the maximum of order quantity under a minimum of total cost can be obtained. For the reason that result should be a fuzzy number because of fuzzy input data, and the certain number about order quantity is preferred in real-world, it is necessary to transform the fuzzy result to crisp one. The application of strategic diagnosis system to supply chain management could be found in the papers of Supply Chain Management (Dahel, 2003, Huang et al., 2002, McAdam and Brown, 2001 and Sadler and Hines, 2002). Sadler and Hines (2002) proposed a conceptual model for strategic operations diagnosis without empirical results for meat business to retail business system (Sadler & Hines, 2002). However, Bhutta and Huq (2002) evaluates the problems of the strategic diagnosis system by evaluating the consistency between the result and the decisions of the strategic channel management. In other words, if the forecasting results for the manufacturing business to retail business strategic diagnosis system are highly consistent with the decisions made by the strategic experts experience, then the channel strategic diagnosis system method are considered helpful for channel strategy decision-makings. Except there must have a lot of transformation technique method talking about the fuzzy set method basis to reflect the property of fuzzy number fully, however, the research also providing a concrete bilateral channel diagnosis about whether a channel strategy is appropriate or not is in paucity. This research, based on the point of view, this fuzzy system can be transformed into a more precise diagnosis system for channel members in the supply chain distribution organization. We are trying to construct a channel strategic diagnosis system analysis by using more precious transformation technique of fuzzy set method. 1.2. Research purpose and procedure This study attempts to start filling the void of how channel strategic diagnosis system to construct a distribution requirement planning system analysis by using more precious inventory control transformation technique of fuzzy set method in the Asia-Pacific Orient supply chain distribution. Thus, the purpose of this study is to use the transformation technique method of fuzzy set method to reflect the inventory control property of fuzzy number fully, and to give the supply chain manager a concrete bilateral suggestion about whether a critical channel inventory control strategy is appropriate or not. This paper is constructed in the following way. The literature review of the DRP (Distribution Requirement Planning), the mean value of a fuzzy number for inventory control and strategic Continuous Review Inventory Model (CRIM) are analyzed in Sections 2 and 3. More specifically, Sections 4 and 5 gives the model construction process and its empirical Numerical example for the meat business to retail business data in Taiwan supply chain distribution. Finally, the Sections 6 and 7, their conclusions and future directions are discussed.

Fuzzy Continuous Review Inventory Model (CRIM) deals with matching optimum inventory level control for supply and forecasted possibility of fuzzy set theory for demand, varying channel multi-echelon retail type orders over the medium term of lead time etc. The aim of Fuzzy CRIM decision-making is to set overall Inventory levels for each channel multi-echelon retail type orders category to meet fluctuating or uncertain demands in the near future, such that Fuzzy CRIM also determines the appropriate reorder point and order quantities to be used. This study develops a Fuzzy CRIM programming model in a fuzzy environment. The proposed model aims to minimize total inventory costs with reference to optimum reorder point, order quantity level, channel multi-echelon retail type capacity and warehouse space. The purpose of channel strategic diagnosis is to find the most efficiency method of inventory control for supply chain management, which will contribute to the channel organization in the future. Therefore, an effective model to select the efficiency and efficacy method is important in channel supply chain management practical sense. In this paper, a new method which use interval mean value concept for inventory control problem was presented here to manage material flows in multi-echelon supply-distribution networks. The method combined the interval mean value concept with possibility fuzzy set theory to obtain a crisp solution for solving the practical demand. Using a closed interval as a solution for order quantity and total cost in inventory control, the DM can make a decision more flexibly and adaptably in the real-world. Therefore, This research can provide a concrete bilateral channel diagnosis about whether a channel strategy is appropriate or not is in paucity. After all, the most of decisions can be made based on a synthetical consideration rather than according to calculation result only. Furthermore, we made a few exploration on the point which input data is fuzzy number rather than one only coefficients is fuzzy like the work done before. We could try to construct a channel strategic diagnosis system analysis by using more precious transformation technique of Fuzzy Set Theory. Fuzzy logic is used to formulate the relationship between the input and output variables. The learning ability of Fuzzy logic network is used to refine the knowledge base. The distributions of this paper are as follows. First it gives an empirical testing for the interval mean value concept of critical strategic diagnosis. Second, it gives an alternative explanation for the interval mean value. Third, it provides the channel practitioners a more concrete bilateral and scientific model to diagnosis the critical interval mean value. Fourth, it is an integration between artificial intelligence and Distribution Requirement Planning (DRP), providing a successful application example of Fuzzy logic. Fi