Post-objective determination of weights of the evaluation factors in public procurement tenders

In public procurement tenders the awarding criterion of the most economically advantageous bid employs weights to aggregate the numerical scores assigned to each proposal with respect to different evaluation factors. Typically these weights are fixed and subjectively set in advance. Methods, which objectively determine the weights after the opening of the sealed bids on the basis of the most or least favorable weights for each proposal, are developed. Post-objective methods of weight determination are shown to enhance the integrity of the evaluation process and to limit corruption in a public tender. The connection of Data Envelopment Analysis, which has been extensively applied to measure supplier efficiency, with the developed methods, is explored. Average least and most favorable weights are derived and optimal bidding strategies in this setting are presented.     

Hybrid Multi-attribute Decision Making Method of Electric Coal Procurement in Industry

Electric coal procurement is the basis of electric power production. In this paper, the problem of supplier selection is studied in multi-source procurement of electric coal. Concretely, the index system of supplier selection is presented, including the evaluation attributes of price, quantity, quality, delivery time and the reputation of supplier. Then, the problem of supplier selection is converted into a problem of hybrid multi-attribute decision making, and a projection method based on hybrid technique for order preference by similarity to ideal solution (TOPSIS) is presented to rank all suppliers and select winners. Its decision example is also given to implement the presented decision method and to demonstrate its effectiveness and practicality. This paper gives an effective way to the hybrid multi-attribute decision making for multi-source procurement of electric coal under fuzzy uncertain environment.     

Compact bidding languages and supplier selection for markets with economies of scale and scope

Combinatorial auctions have been used in procurement markets with economies of scope. Preference elicitation is already a problem in single-unit combinatorial auctions, but it becomes prohibitive even for small instances of multi-unit combinatorial auctions, as suppliers cannot be expected to enumerate a sufficient number of bids that would allow an auctioneer to find the efficient allocation. Auction design for markets with economies of scale and scope are much less well understood. They require more compact and yet expressive bidding languages, and the supplier selection typically is a hard computational problem. In this paper, we propose a compact bidding language to express the characteristics of a supplier’s cost function in markets with economies of scale and scope. Bidders in these auctions can specify various discounts and markups on overall spend on all items or selected item sets, and specify complex conditions for these pricing rules. We propose an optimization formulation to solve the resulting supplier selection problem and provide an extensive experimental evaluation. We also discuss the impact of different language features on the computational effort, on total spend, and the knowledge representation of the bids. Interestingly, while in most settings volume discount bids can lead to significant cost savings, some types of volume discount bids can be worse than split-award auctions in simple settings.     

高校图书馆书刊采访决策支持系统

在指出高校图书馆书刊采访科学决策重要性的基础上,分析影响书刊采访决策的基本因素,提出一种科学的书刊采访决策模式.建立了图书供应商选择、采购经费分配、书刊选购决策的数学模型,进而给出书刊采访决策支持系统的设计方案.该系统的应用可提高图书采访质量.     

Optimizing product assortment under customer-driven demand substitution

The problem of product assortment and inventory planning under customer-driven demand substitution is analyzed and a mathematical model for this problem is provided in this paper. Realistic issues in a retail context such as supplier selection, shelf space constraints, and poor quality procurement are also taken into account. The performance of three modified models, one that neglects customers’ substitution behavior, another that excludes supplier selection decision, and one that ignores shelf space limitations, are analyzed separately with computational experiments. The results of the analysis demonstrate that neglecting customer-driven substitution or excluding supplier selection or ignoring shelf space limitations may lead to significantly inefficient assortments. The effects of demand variability and substitution cost on optimal assortment and supplier selection decisions as well as on the optimal revenue are also investigated. The main contribution of this paper is the development of a practical and flexible model to aid retailers in finding optimal assortments to maximize the expected profit.     

Multi-criteria auctions without full comparability of bids

The emergence of auction mechanisms that support bids characterized by several attributes is one of the most recent evolutions within auction theory. These mechanisms, referred to as multi-attribute, multiple issue or multi-dimensional auctions, are at the intersection between multi-criteria decision and auction theories. The purpose of this paper is to introduce multi-criteria auctions the originality of which is not to require full comparability between bids. We claim that this distinctive feature is of great interest, especially in procurement situations. Furthermore, the existence of potential incomparability between multi-dimensional offers will permit us to manage different bidding niches coexisting within the same bidding space. A theoretical framework based on a general preference structure will be introduced and then referenced to existing approaches such as multi-attribute auctions or new ones such as dominance based multi-criteria auctions or butterfly auctions.     

Configurable offers and winner determination in multi-attribute auctions

The theory of procurement auctions traditionally assumes that the offered quantity and quality is fixed prior to source selection. Multi-attribute reverse auctions allow negotiation over price and qualitative attributes such as color, weight, or delivery time. They promise higher market efficiency through a more effective information exchange of buyer’s preferences and supplier’s offerings. This paper focuses on a number of winner determination problems in multi-attribute auctions. Previous work assumes that multi-attribute bids are described as attribute value pairs and that the entire demand is purchased from a single supplier. Our contribution is twofold: First, we will analyze the winner determination problem in case of multiple sourcing. Second, we will extend the concept of multi-attribute auctions to allow for configurable offers. Configurable offers enable suppliers to specify multiple values and price markups for each attribute. In addition, suppliers can define configuration and discount rules in form of propositional logic statements. These extensions provide suppliers with more flexibility in the specification of their bids and allow for an efficient information exchange among market participants. We will present MIP formulations for the resulting allocation problems and an implementation.     

Optimal purchasing policy in a two-component assembly system with different purchasing contracts for each component

This paper considers an assembly system where a firm produces a single product which is assembled using two types of components (component 1 and component 2). The components are provided by individual suppliers (supplier 1 and supplier 2). We assume that the firm makes different procurement contracts with supplier 1 and supplier 2. To supplier 1, the firm specifies the maximum inventory level of component 1 and makes a commitment to purchase the component as long as its inventory level is below this target level. To supplier 2, the firm has the option of purchasing or rejecting component 2 at each instant supplier 2 provides it. Formulating our model as a Markov decision problem, we identify a component 2 purchasing policy which maximizes the firm’s profits subject to the costs of rejecting component 1, holding component 2, and purchasing component 2. We also investigate how the changes in the sales price and cost parameters affect the optimal purchasing policy. Finally, we present numerical study for the optimal performance evaluation.This material is based upon work supported by the Korea Science and Engineering Foundation (KOSEF) through the Northeast Asia e-Logistics Research Center at University of Incheon.     

A practical expected-value-approach model to assess the relevant procurement costs

As an improvement to a series of expected value approach models, this study presents a supplier portfolio decision model, rather than a number of suppliers decision model. The model assesses risks as costs, considers types of heterogeneity in a procurement process and links the assessed terms in the programming model with accounting concepts. The model is then confirmed by using a procurement decision case with real data. The analysis has several implications for modelling, application and practice. In the light of the counterexamples shown, some thoughts pertaining to procurement decisions in supply management are reconsidered.     

Multiattribute electronic procurement using goal programming

One of the key challenges of current day electronic procurement systems is to enable procurement decisions transcend beyond a single attribute such as cost. Consequently, multiattribute procurement have emerged as an important research direction. In this paper, we develop a multiattribute e-procurement system for procuring large volume of a single item. Our system is motivated by an industrial procurement scenario for procuring raw material. The procurement scenario demands multiattribute bids, volume discount cost functions, inclusion of business constraints, and consideration of multiple criteria in bid evaluation. We develop a generic framework for an e-procurement system that meets the above requirements. The bid evaluation problem is formulated as a mixed linear integer multiple criteria optimization problem and goal programming is used as the solution technique. We present a case study for which we illustrate the proposed approach and a heuristic is proposed to handle the computational complexity arising out of the cost functions used in the bids.     

采购量分配决策模糊规划模型

供应商对形成企业的竞争力起着关键性的作用。采购决策中,采购商需要决策两方面的问题:应该选择哪些供应商以及在每一供应商应该采购多少。本文考虑了多目标性和目标的模糊性,同时为供应商选择和采购量分配提出了一个集成的决策模型。最后,通过一个算例阐释了该模型。     

Application of multi-dimensional procurement auction in single-period inventory models

Procurement is one of the major activities in the Manufacturing Resource Planning (MRP II), which is closely coupled with inventory management. Any improvement in this area will have a direct impact on the performance of the entire supply chain. Auction mechanism can be a successful procurement method when there are several potential suppliers available. In this paper, we consider a single-period inventory model when the selection of the supplier is done through a reverse auction with bids consisting of several attributes, namely price, shortage quantity, and lead time. The multi-dimensionality of the bid increases the complexity of the underlying problem creating a need for an approach to determine an approximate optimal target level. This is needed for the initiation of the auction and the procurement process. Monte-Carlo simulation method was used to investigate the performance of the approximation. Simulation results show that the analytical method provides an acceptable approximation for the optimal target inventory level.     

基于距离测度及支持度的混合评价信息供应商选择决策

新零售模式的推进迫使企业不断加强供应链节点上的供应商选择优化,是企业新时代发展面临的新课题。针对供应商选择优化提出一种基于距离测度及支持度的群决策方法,根据不同类型属性评价信息的距离测度定义,得到不同决策者之间关于单一属性指标的相互支持度,从而确定单个供应商各属性的群体综合评价值,并采用灰关联法对备选供应商进行排序择优。     

Coordination of stocking decisions in an assemble-to-order environment

This paper examines an assemble-to-order environment involving a short-life-cycle product that is sold in two different configurations, each requiring a unique component that must be stocked in advance. Both configurations of the product are assembled on the same equipment which has limited capacity. The focus of the analysis is on the determination of the appropriate stocking quantities for each of the configuration specific components. Because the same assembly capacity is often reused for different instances of the component stocking problem, we treat assembly capacity as exogenous. This represents the fact that capacity decisions are often made less frequently than are procurement/production quantity decisions. We first solve for the first-best stocking policy when the components are produced internally. We then consider the case when the components are procured from external suppliers, and investigate how different forms of contract between the assembler and the component suppliers affect coordination of the supply chain as well as each party’s profit. One particularly interesting finding is that it is possible to coordinate the supply chain with a single-price contract between each supplier and the assembler while awarding all parties positive profit.     

Restricting weights in supplier selection decisions in the presence of dual-role factors

One of the uses of data envelopment analysis (DEA) is supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. As well, in some situations there is a strong argument for permitting certain factors to simultaneously play the role of both inputs and outputs. The objective of this paper is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This paper depicts the supplier selection process through a DEA model, while allowing for the incorporation of decision maker’s preferences and considers multiple factors which simultaneously play both input and output roles. The proposed model does not demand exact weights from the decision maker. This paper presents a robust model to solve the multiple-criteria problem. A numerical example demonstrates the application of the proposed method.     

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.     

Supplier selection using axiomatic fuzzy set and TOPSIS methodology in supply chain management

The current paper presents a comprehensive methodology for supplier selection. In the first stage, the linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the criteria. The Axiomatic Fuzzy Set clustering (AFS) method, which handles ambiguity and fuzziness in the supplier selection problem effectively, is applied to cluster the suppliers and evaluate each potential supplier that aims at obtaining initial supplier ranking. In the second stage, the Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to determine the weight of various quantitative and qualitative criteria. To address multiple decision criteria in supplier ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to select the final suppliers. A numerical example composed of 30 suppliers and 6 criteria is studied, and the experimental results show that the proposed evaluation framework is suitable for supplier selection decisions even with the dependent criteria/attributes.     

An integrated multiple criteria decision making model applying axiomatic fuzzy set theory

This article presents a hybrid model for the multiple criteria decision making problems. The proposed decision model consists of three parts: (i) DEA (data envelopment analysis) is used to provide the best combination on the performance parameters of original data; (ii) By the application of AFS (axiomatic fuzzy set) theory and AHP (analytic hierarchy process) method, the weight of each attribute is calculated and (iii) TOPSIS (technique for order preference by similarity to ideal solution) is applied to provide the ranking order of that best combination based on the weights of attributes. In addition, we also provide the definitely semantic interpretations for the decision results by AFS theory. Specially, the model not only employs the performance parameters from raw data, but also considers the preferences from decision-makers that can make the decision results more reasonable. The proposed model is used for robot selection to verify the proposed model. Using the selection index, the evaluation of alternative robots and the selection of the most appropriate are eventually feasible. Moreover, a numerical example for supplier selection is included to illustrate the application of the model for the newly developed problems.     

基于粗糙集-层次分析法的制造业供应商评价选择

制造企业在确定供应商过程中,必须对供应商进行评价和选择,由于制造业的加工设备和加工工艺的复杂性,同时要确保供应商供货稳定和质量保证等多方面的因素,其评价指标体系必然有别于其它行业的评价指标.采用粗糙集属性约简的方法,对供应商评价选择指标进行约简分析,运用层次分析法对供应商进行评价选择.