一种供应商选择与评价模型

供应商的选择是决定企业成本及产品质量的重要因素,本文提出一种利用信息不完全的多准则层次分类方法来解决供应商选择评价问题,该方法利用训练集的分类和准则权系数的不完全信息,通过数学模型计算各准则的偏好值以及一致性指标,进而确定候选供应商是否属于所需分类,最后通过一个具体实例验证了该方法的有效性.     

基于SPN模型的物流采购决策

在电子商务物流管理系统(ELMS)中,针对采购决策存在的供应商选择和订单选择问题,根据ELMS采购流程和多属性决策方法得到供应商选择调度算法,利用基于随机Petri网的多服务器多队列模型实现订单选择策略。性能分析结果表明,该方法能有效减少ELMS系统的响应时间,并提高其吞吐率。     

突发灾害下医疗资源供应商选择模型及算法

对医疗资源供应商选择问题进行建模,提出一种权重系数信息不完全的多准则区间直觉模糊集的供应商排序算法。该算法通过逻辑集成得到各供应商的区间直觉模糊集,计算各供应商的区间直觉模糊数的Hamming距离,并利用粒子群优化算法求解建立的非线性优化模型,得出最优准则的权重系数。通过比较各供应商与优等及次等供应商的距离,进行最优排序。以大规模突发灾害事件下的供应商选择为案例,说明该模型及算法的有效性和可行性。     

供应商选择的双层规划模型及求解分析

为了促使供应商在降低产品成本的同时,提高服务质量,提出了利用双层规划建立供应商选择模型。上层规划以制造企业的采购费用最小为目标,下层以选择的供应商的服务质量最大为目标。其中,下层规划中供应商的服务质量用综合评价得分体现先计算出各影响因素的权重,乘以标准判断矩阵得出各供应商的综合得分。最后,结合模型特点设计了双层迭代的算法,并结合实例验证了模型和算法的有效性。     

整合供应商选择的多层级选址-库存模型

针对多级供应链网络设计中选址和库存一体化决策问题,基于梯级库存策略,建立了整合供应商选择的多层级选址．库存模型。模型以网络中供应商的选择成本、工厂和配送中心的打开成本、层级之间的运输成本、库存成本、采购成本和生产成本之和最小为目标,同时对供应商的选择、工厂和配送中心的选址、配送中心对顾客的分配、层级之间的运输量、工厂和配送中心的订货批量进行决策。为了求解所建立的模型,设计了基于部分编码的粒子群优化算法。20个不同规模的算例测试表明：所建立的模型是有效的,能用于多层级供应链网络的设计;所设计的算法无论是在求解精度,还是在运算速度上都明显优于数学优化软件Lingo9．0,尤其是当供应链网络中总节点数较大时。     

评价波动对石化项目采购决策的影响

石化项目物资采购决策的本质是供应商选择,主要依据企业按照物资供应商一定评价标准所得到的评价考核结果.由于物资供应商评价标准中的主观因素和时间、环境因素可能导致评价考核结果变化,使得物资供应商的评价会产生波动,导致不同的物资供应商评价结果,从而影响采购决策.为了研究物资供应商评价波动对采购决策的影响程度,以典型石化项目—乙烯裂解炉项目物资采购为例,提出了供应商量化考核因子的变量,建立以采购为主的供应链数学规划模型.通过定量分析,对比不同物资供应商量化考核因子的波动对供应商选择的影响大小,识别出对供应商量化考核因子变化更为敏感的物资类别,为做好石化项目物资供应商选择和采购决策提供有益参考.     

钢铁集团内部协同采购策略优化

以钢铁企业集团内部采购物流的协同优化为研究对象,对企业协同化采购的策略进行分析,分析子策略的采购物流成本并求解其最优成本。建立以需求契合度最大化和采购物流成本最小化为目标的采购策略模型,并采用平衡将其因子转化为单目标模型。依据对单独采购的评价选取每个子公司的供应商,采用逐步构解的邻域方式设计求解问题的模拟退火算法。对典型算例的求解结果分析表明,协同采购策略能够节约企业采购成本,取得较好收益。     

零售企业可替代消费品采购数量分配优化模型

研究连锁零售企业采购可替代消费品时在不同供应商之间分配采购数量的问题。考虑企业的经营性质和供应商选择的实际,给出了评价指标及其度量,并以采购数量满足各个指标的评价值最优建立了多目标整数规划模型,然后介绍了模型的求解方法并给出了一个具体的数值算例。     

新品速递

电子商务解决方案供应商 Commerce One 公司发布“Commerce One 协作采购”解决方案,该方案为企业构建了一个 B2B 的电子商务供货门户,以实现从采购到支付的全部交易过程都在线进行以及交易伙伴之间的协作交流。采用该方案,采购者还可与全球最大的 B2B 交易网(Global Trading Web)上的15多万个供货商进行     

随机时变需求下有阶段和总量约束的多源采购优化

针对有阶段采购量区间约束及最小总量要求的多供应商多阶段订单分配问题,以采购方采购、库存、缺货成本总和与期末库存残值之差的期望值最小为目标,建立随机非线性规划模型.考虑采购量约束及多供应商情形,扩展报童模型得出单阶段最优采购策略.以此为基础建立多阶段启发式算法,通过仿真分析各参数对最优采购策略及其总成本的影响.将最小总量承诺契约扩展到时变需求和多供应商情形,可为采购方降低成本.     

An intelligent supplier evaluation,selection and development system

Supplier evaluation and selection process has a critical role and significant impact on purchasing management in supply chain. It is also a complex multiple criteria decision making problem which is affected by several conflicting factors. Due to multiple criteria effects the evaluation and selection process, deciding which criteria have the most critical roles in decision making is a very important step for supplier selection, evaluation and particularly development. With this study, a hybridization of fuzzy c-means (FCM) and rough set theory (RST) techniques is proposed as a new solution for supplier selection, evaluation and development problem. First the vendors are clustered with FCM algorithm then the formed clusters are represented by their prototypes that are used for labeling the clusters. RST is used at the next step of modeling where we discover the primary features in other words the core evaluation criteria of the suppliers and extract the decision rules for characterizing the clusters. The obtained results show that the proposed method not only selects the best supplier(s), also clusters all of the vendors with respect to fuzzy similarity degrees, decides the most critical criteria for supplier evaluation and extracts the decision rules about data.     

Supplier selection model using Taguchi loss function,analytical hierarchy process and multi-choice goal programming

In recent years, determining the best supplier has become a key strategic consideration in the competitive market. Since the decision commonly involves evaluating different criteria or attributes, supplier selection process is a multiple criteria decision-making (MCDM) problem. This study integrates the Taguchi loss function, analytical hierarchy process (AHP) and multi-choice goal programming (MCGP) model for solving the supplier selection problem. The advantage of this proposed method is that it allows decision makers to set multiple aspiration levels for the decision criteria. A numerical example of application is also presented.     

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.     

An integrated fuzzy-lp approach for a supplier selection problem in supply chain management

In supply chain management process, the firm select best supplier takes the competitive advantage to other companies. Thus, supplier selection is an important issue and with the multiple criteria decision-making approach, the supplier selection problem includes both tangible and intangible factors. This paper is aimed to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient. Finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. The integrated model is illustrated by an example in a textile firm.     

Fuzzy DEMATEL method for developing supplier selection criteria

Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that a large amount of direct and indirect profits can be obtained from effective and efficient SCM practices. Supplier selection has great impact on integration of the supply chain relationship. Effective and accurate supplier selection decisions are significant components for productions and logistics management in many firms to enhance their organizational performance. This study pioneers in using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to find influential factors in selecting SCM suppliers. The DEMATEL method evaluates supplier performance to find key factor criteria to improve performance and provides a novel approach of decision-making information in SCM supplier selection. This research designs a fuzzy DEMATEL questionnaire sent to seventeen professional purchasing personnel in the electronic industry. Our research results find that stable delivery of goods is the most influence and the strongest connection to other criteria.     

Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection

A company must purchase a lot of diverse components and raw material from different upstream suppliers to manufacture or assemble its products. Under this situation the supplier selection has become a critical issue for the purchasing department.The selection of suppliers depends on number of criteria and the challenge is to optimize selection process based on critical criteria and select the best supplier(s). During supplier selection process initial screening of potential suppliers from a large set is vital and the determination of prospective supplier is largely dependent on the criteria chosen of such pre-qualification. In the literature, many judgments based methods are proposed and derived criteria selection from the opinion of either the customers or the experts. All these techniques use the knowledge and experience of the decision makers. These methods inherit certain degree of uncertainty due to complex supply chain structure. The extraction of hidden knowledge is one of the most important tools to address such uncertainty and data mining is one such concept to account for such uncertainty and it has been found applicable in many scenarios. The proposed research aims to introduce a data mining approach, to discover the hidden relationships among the supplier’s pre-qualification data with the overall supplier rating that have been derived after observation of previously executed work for a period of time. It provides an overview that how supplier’s initial strength influence its final work performance.     

A model for exploring the impact of purchasing strategies on user requirements determination of e-SRM

Recently, business managers have touted supplier relationship management (SRM) as the next core competency and a source of competitive advantage. This suggests the need of an e-SRM installation. SRM domain involves relatively wide and complex business processes and without the support of IT, it would not be accomplished easily. As the Internet became popular and many firms were quick to launch e-SRM, however, the effect went farther than was expected. In particular, the fundamental is in the determination of user requirements, commonly considered to be the key to the success of system installation and often inappropriately planned. This study involves a strategy-based planning process to guide user requirements determination. Moreover, the implementation step in this process, identification of supplier selection criteria, has rarely been considered and the literature has only discussed it isolatedly for the choice of a supplier. Basically, the process includes three steps: define purchasing strategies, identify supplier selection criteria, and determine system requirements. Empirical results have indicated that purchasing strategies significantly influence the performance of user requirements determination.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

An integrated fuzzy MCDM approach for supplier evaluation and selection

A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.