دانلود مقاله ISI انگلیسی شماره 19119
عنوان فارسی مقاله

یک روش مدل سازی بهینه سازی معناشناختی دو مرحله ای در انتخاب تامین کننده در تدارکات الکترونیک

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
19119 2006 8 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
A two-phased semantic optimization modeling approach on supplier selection in eProcurement
منبع

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

Journal : Expert Systems with Applications, Volume 31, Issue 1, July 2006, Pages 137–144

کلمات کلیدی
برنامه ریزی الکترونیکی تدارکات - سیستم مبتنی بر دانش - تخصیص و انتخاب تامین کننده - مدل بهینه سازی - مدیریت مدل -
پیش نمایش مقاله
پیش نمایش مقاله یک روش مدل سازی بهینه سازی معناشناختی دو مرحله ای در انتخاب تامین کننده در تدارکات الکترونیک

چکیده انگلیسی

The eProcurement planning is crucial to reduce purchase cost while selecting the right suppliers and it contributes to improve corporate competitiveness. This eProcurement planning research describes a framework for the integration of a knowledge-based system capable of identifying a goal model from a Primitive Model. The Primitive Model is screened by the screening factors reflecting the purchase strategy. As a result, by using the framework for supplier selection and allocation (SSA), a purchaser is able to reduce the costs and time required to select the right suppliers and to alleviate anxiety for ‘out-of-favor’ suppliers. This approach is based on two-phased semantic optimization model modification that semantically builds a goal model through model identification and candidate supplier screening based on model identification rules and supplier screening rules. This approach contributes significantly to construction of an optimization model from the perspective of model management and it provides a useful environment for efficient eProcurement from the perspective of a purchaser.

مقدمه انگلیسی

The proliferation of B2B e-Commerce in recent years has resulted in an explosion of eProcurement on the Internet. Procurement from various suppliers is a capital-intensive decision that often accounts for a large portion of the total operating costs (Bonser & Wu, 2001). Hence, it is very important to reduce purchase cost while selecting the right suppliers and it contributes to improve corporate competitiveness. Research works related to supplier selection can be classified into two broad categories: a qualitative approach and a quantitative one. A majority of the research deals with qualitative supplier evaluation schemes. Given the economic importance and inherent complexity of the supplier selection process, only a few articles have addressed decision-making by quantitative methodologies. None of the supplier selection models, however, explicitly reflect the purchase policy or the supplier-related knowledge dynamically nor do most of them reflect the possibilities of purchasing several parts from a single supplier for price discount or bundling effect. Purchase strategies depend on the situation of an organization. In order to support the strategies, diverse models are necessary. Recently, model warehouse (Bolloju, Khalifa & Turban, 2002) is one of the methods to solve these problems. However, it is not easy for purchaser to meet with diverse purchase strategies by using only several ready-made models, and it is very inefficient to prepare all combinations of models in advance from model management point of view. Moreover, candidate suppliers vary depending on purchase conditions. It is a little complex to build a goal model to select right suppliers among all of the potential suppliers. Hence, a simpler approach is needed to solve this problem. In this research, we propose a two-phased semantic optimization modeling approach that formulates a goal model through model identification and candidate supplier screening for strategic supplier selection and allocation (SSA). In the procurement process, supply conditions of suppliers and purchase strategies of purchaser are considered together. Basically, a purchaser wants to minimize the purchase cost with supply conditions such as price discount and bundling while making purchase strategies. A purchase strategy affects a goal model. We build a goal model from SSA base models through the process of model modification that reflects purchase strategies. Fig. 1 depicts the modeling architecture for SSA. The SSA procedure is broken down into goal model identification, candidate supplier screening, goal model formulation, and model solving. The details of each component are as follows. • Goal Model Identification. A new specific goal model based on the SSA base model is identified by modeling factors, which compose a purchase strategy by purchase manager. Chang and Lee (2004) proposed three approaches to derive a goal model from a base model: the Primitive Model approach, the Full Model approach, and the Most Similar Model approach. The Primitive Model has only mandatory constraints and the Full Model has all possible requested constraints. The Most Similar Model is a model case that is the most similar one to a modeling request. Those models can be modified into a goal model by adding or deleting model components. Intuitively the Primitive Model Approach is effective when the goal model is similar to the Primitive Model ( Chang & Lee, 2004). Thus, in this research, we use the Primitive Model Approach, because it starts from a simple model. The identified goal model is composed of an SSA Primitive Model and additional model constraints. The identified goal model can be represented as: View the MathML sourceGMIdentified=(SSAPrimitiveModel;AddedConstraints). Turn MathJax on We described Model Identification Knowledge, which identifies a goal model from the Primitive Model, in Section 4.1. • Candidate Supplier Screening. Candidate suppliers are screened by the supplier screening factors, which compose a purchase strategy. The preliminary screened candidate suppliers must satisfy the purchaser's requirements for evaluation criteria such as quality, delivery, and price boundary. After screening, the goal model is described as: View the MathML sourceGMScreened=(GMIdentified;CandidateSuppliers). Turn MathJax on We described Candidate Supplier Screening Knowledge, which sifts candidate suppliers from potential suppliers, in Section 4.2. • Goal Model Formulation and Model Solving. The Modeling components corresponding to a base model and added constraints in GMScreened formulate an optimization model GMOpt using model coefficients of candidate suppliers and it can be solved by an IP solver such as iLOG, LINGO, or LINDO, etc. Full-size image (24 K) Fig. 1. The modeling architecture for supplier selection and allocation. Figure options To describe the above approach, we organized this article as follows. In Section 2, we reviewed previous studies related to SSA. In Section 3, we introduced an SSA Primitive Model with price discount and bundling effect. In Section 4, we described two-phased model formulation in details. Finally, we conclude our study.

نتیجه گیری انگلیسی

The emerging digital economy is creating abundant opportunities for operations research (OR) applications such as financial services, electronic markets, network infrastructure, packaged OR software tools, supply chain management (SCM), and travel-related services (Geoffrion & Krishnan, 2001). In this paper, we applied OR to strategic SSA in B2B eProcurement and proposed an approach on SSA using the two-phased optimization model formulation. This approach formulates a goal model after semantic model modification that is composed of model identification and candidate supplier screening. The model includes several realistic factors, such as price discount and bundling effect, which effectively bring the power of the optimization models to solve practical procurement problems. We elicited knowledge related to purchasing strategies and built rules for model identification and supplier screening. By using them, various modeling requests from the purchase manager can be managed. For further research, we should consider more practical factors, relations between factors, and relations between our eProcurement model and specific SCM related models such as demand forecasting and inventory models (Gao & Tang, 2003; Ghodsypour & O'Brien, 2001). Also, we should consider the variable time t, namely multiple time periods ( Degraeve & Roodhooft, 1998) for long-term eProcurement planning.

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