انتخاب هماهنگ شده مناقصه های تأمین تجهیزات در محیط های با ظرفیت محدود
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|16993||2009||11 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 9847 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||14 روز بعد از پرداخت||886,230 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||7 روز بعد از پرداخت||1,772,460 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electronic Commerce Research and Applications, Volume 8, Issue 6, November–December 2009, Pages 291–301
Pressure to increase agility and reduce costs is pushing enterprises to dynamically select among offers from a broader range of suppliers. This process is facilitated by the adoption of web services standards. An important requirement in this context is the ability to move away from unidimensional price-based e-procurement models and develop richer solutions that are capable of capturing other important attributes in the selection of supplier bids. Research on the evaluation and selection of supplier bids (“winner determination”) has traditionally ignored the temporal and finite capacity constraints under which manufacturers and service providers often operate. We consider the problem faced by a firm that procures multiple key components or services from a number of possible suppliers. Bids submitted by suppliers include both a price and a delivery date. The firm has to select a combination of supplier bids that will maximize its overall profit. Profit is determined by the revenue generated by the products (or services) sold by the firm, the costs of the components (or services) it acquires as well as late delivery penalties it incurs if it fails to deliver its products/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We proceed to show that our model can be characterized as a pseudo-early/tardy scheduling problem and use this observation to build an efficient heuristic search procedure. Computational results show that our heuristic procedure typically yields solutions that are within a few percent from the optimum. They further indicate that taking into account the manufacturer/service provider’s capacity can significantly improve its bottom line.
Today’s global economy is characterized by fast changing market demands, short product lifecycles and increasing pressure to offer high degrees of customization, while keeping costs and lead times to a minimum. In this context, the competitiveness of both manufacturing and service companies will increasingly be tied to their ability to dynamically select among multiple possible supply chain partners in response to changing market conditions. In this paper, we consider an environment where a firm needs to meet customer delivery commitments while procuring a combination of key components or services from multiple possible suppliers. At any point in time, components or services offered by different suppliers may vary both in terms of prices and delivery dates. Such a situation arises in a number of different contexts. This includes manufacturers with long-term relationships with more than one supplier (possibly independently managed plants owned by the same firm) as well as manufacturers or service providers dynamically selecting prospective suppliers in response to changing market demands. These latter scenarios arise in the context of capacity subcontracting in manufacturing and logistics , as well as in a wide range of other sectors (e.g., call center capacity, dynamic procurement of programming services (Programmingbids.com ), translation services (Language123.com ), and a growing number of other services ). These dynamic practices are increasingly facilitated by the emergence of web services standards, such as ebXML , W3C SOAP , OASIS UDDI and W3C WSDL . Prior research on bid selection (“winner determination”) has generally ignored temporal and capacity constraints under which companies operate (e.g., due dates by which different orders need to be delivered to customers as well as the limited capacity available to assemble components/services obtained from suppliers). The work presented herein shows that taking such constraints into account can help companies make more judicious decisions when it comes to selecting among multiple supply alternatives. Specifically, we present techniques aimed at exploiting temporal and capacity constraints to help a firm select among supply alternatives that differ in price and delivery date. We refer to this problem as the Finite Capacity Multi-Component Procurement (FCMCP) problem. This article provides a formal definition of the FCMCP problem, discusses its complexity and introduces several rules that can be used to prune its search space. It presents an efficient pseudo-early/tardy heuristic search procedure that takes advantage of these pruning rules. Computational results show that accounting for the firm’s finite capacity can significantly improve its bottom line, confirming the important role played by finite capacity considerations in procurement problems. Results are also presented that compare the performance of our heuristic search procedures both in terms of solution quality and computational requirements under different supply profiles (or “bid profiles”). These results suggest that our pseudo-early/tardy procedure is generally capable of generating solutions that are within just a few percent from the optimum and that it scales nicely as problem size increases. The balance of this paper is organized as follows. Section 2 provides a brief review of the literature. In Section 3, we introduce a formal model of the FCMCP problem. Section 4 identifies three rules that can help a firm (manufacturer or service provider) eliminate non-competitive procurement bids or bid combinations. Section 5 introduces a heuristic search procedure that exploits a property of pruned FCMCP problems introduced in Section 4 to solve the resulting problem as a pseudo-early/tardy scheduling problem. Section 6 presents a post-processing procedure that can further improve the quality of a solution. An extensive set of computational results are presented and discussed in Section 7. Section 8 provides some concluding remarks and discusses future extensions of this research.
نتیجه گیری انگلیسی
The Web is facilitating the emergence of more flexible and expressive supply chain trading practices. In this article, we focused on a situation where a firm uses the Web to obtain and evaluate alternative procurement options for several of the components or services it needs to fulfill multiple customer delivery commitments. This problem is representative of the one faced by manufacturers with long-term relationships with more than one supplier as well as by manufacturers or service providers dynamically selecting prospective suppliers in response to changing market demands. Examples of this latter situation include capacity subcontracting in manufacturing as well as a wide range of Web-enabled scenarios found in the service industry (e.g., call center capacity, dynamic procurement of programming services, translation services). In contrast to prior work in this area, which has generally ignored capacity and delivery date considerations, this article has introduced a deterministic model for finite capacity multi-component procurement where a firm has to select among supplier bids that differ in terms of price and delivery date. We have identified several dominance criteria that enable the manufacturer (or service provider) to quickly eliminate dominated bids and bid combinations. We have shown that the resulting problem can be modeled as a pseudo-early/tardy problem with step-wise earliness costs. A randomized pseudo-early/tardy (PET) search heuristic has been introduced to help the manufacturer select a combination of bids that maximizes its overall profit, taking into account its finite capacity as well as the prices and delivery dates associated with different supplier bids. An important contribution of the work reported in this article is in quantifying the benefits of finer winner determination models such as the FCMCP model we have introduced. By explicitly accounting for capacity constraints and synchronization requirements among the components required by each order, this model empowers the manufacturer to selectively balance procurement costs and tardiness costs at a much finer level than models that ignore capacity constraints. Using our PET search procedure, we have shown that the FCMCP model can yield significant savings over simpler infinite capacity bid selection models. Comparison with optimum solutions obtained using branch-and-bound suggests that a hybrid heuristic that combines our PET and SA procedures generally yields solutions that are within a few percent of the optimum. It can be shown that the techniques presented in this article can be extended to situations where one needs to relax the lot-for-lot assumption (e.g., to take advantage of price discounts, to reduce fixed ordering costs, or to handle situations where customer order quantities and quantities in supply bids do not match) or deal with customer orders for products with overlapping BOMs . It should also be noted that the model and techniques presented in this paper can easily be generalized to accommodate situations where the manufacturer can process multiple orders at the same time (non-unary capacity) or where the manufacturer incurs setup times for switching production between different product families. This is true for the pruning rules we introduced as well as for our PET search procedure. At the same time, we have not attempted to evaluate our techniques on these problems and hence do not know, for instance, how far our heuristic search procedures would be from the optimum. It is also worth noting that our pruning rules also apply to situations where the manufacturer is modeled as a more complex job shop environment, where each order has to flow through a (possibly different) succession of machining (or service) centers.