تخصیص سفارش ـ انتخاب تامین کننده: روش برنامه ریزی پویا با معیارهای چندگانه و دو مرحله ای
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|19286||2011||6 صفحه PDF||سفارش دهید||3690 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 132, Issue 1, July 2011, Pages 52–57
This paper proposes a two-stage multiple criteria dynamic programming approach for two of the most critical tasks in supply chain management, namely, supplier selection and order allocation. In the first stage, to address multiple decision criteria in supplier ranking, the analytic hierarchy process (AHP) is employed. In the second stage, supplier ranks are fed into an order allocation model that aims at maximizing a utility function for the firm as well as minimizing the total supply chain costs, subject to constraints on demand, capacity, and inventory levels. A dynamic programming approach is crafted to solve the proposed bi-objective model.
Supply chain management provides an integrated decision-making framework for the planning and control of firm assets, operations, transactions, and information channels in a fashion that maximizes business profitability and customer satisfaction. Purchasing constitutes one of the key strategic functions in supply chain management. For a typical manufacturer, purchased items (such as raw materials) may represent 60% of her total sales, and the purchasing share in the total turnover in industrial companies normally ranges between 50–90% (Boer et al., 2001). It is therefore essential to carefully manage the supplier selection process and the order allocation strategy of the firm in order to establish a competitive and effective purchasing function. Selecting the right supplier(s) gives a company a competitive edge and is instrumental in reducing costs and improving the quality of end products. As a consequence, there exists a continued interest in the development of suitable frameworks to evaluate and select suppliers. As far as the order allocation strategy is concerned, supply chain management practitioners seek to identify the optimal ordering quantities to be purchased from each supplier over a specified planning horizon. To this end, a wide spectrum of mathematical programming models has been investigated to gain insights into newsvendor-type settings or lot-sizing problems under various assumptions. This paper develops a multi-criteria decision-making framework that incorporates supplier selection and order allocation decisions under time-varying prices/costs, capacity, and demand volumes and profiles. The novelty of the proposed approach stems from the fact that, to the best of our knowledge, all proposed supplier selection-order allocation frameworks in the literature are based on either static-multiple objective or dynamic-single objective models. The remainder of the paper is organized as follows. In Section 2, we present a review of the relevant literature. Section 3 provides a formal problem statement, and elaborates on the application of the analytic hierarchy process (AHP) to rank potential suppliers and the proposed bi-objective order allocation model which is solved in Section 4 using an adequate dynamic programming approach. Section 5 discusses a small-sized illustrative example to gain insights into the proposed decision-making framework and the dynamic programming solution. Section 6 concludes the paper with a summary of our findings and directions for future research.
نتیجه گیری انگلیسی
Purchasing function, and in particular supplier selection decisions, are key to the success of a company in today's competitive market. To ascertain the optimal supplier selection-order allocation decisions, the Analytic Hierarchy Process (AHP) has been used in concert with multiple objective dynamic programming to rank potential suppliers and accordingly identify the optimal quantities to be ordered from them. By using AHP, we addressed the multiple criteria nature of such a selection process, which involves both qualitative and quantitative attributes. Furthermore, using a multiple objective dynamic programming approach provided the opportunity of determining the optimal order quantities for several time-periods with respect to both a (non-monetary) utility objective (to be maximized) and a cost function (to be minimized). An illustrative example demonstrated the usefulness of the proposed bi-objective model in identifying attractive trade-offs between utility and cost objectives. We recommend the following directions for future research. First, the dynamism of the model could be extended to account for changes in the set of potential suppliers. In this way, the number of suppliers a manufacturer is dealing with could vary over time. Also, it would be interesting to consider settings where the manufacturer is facing stochastic demands due to market uncertainties.