Allocation of external returns of different quality grades to multiple stages of a closed loop supply chain

A single period multistage closed loop supply chain (CLSC) is presented here considering simultaneous manufacturing of new products and remanufacturing of customer returned used products. The quantity of used products in reverse supply chain considered in the model is a fraction of the new products manufactured in the forward supply chain. Used products of known quantity are pulled from end customers as per quality grades. Products are assumed to be mechanical in nature and remanufacturer has to pay different acquisition prices for different quality grades of return. The groups of graded products based on the acquisition prices are thus sorted and sent to stages of CLSC earmarked for them as per demand for repairing/recycling of raw materials. After repairing/refurbishing and recycling at each stage of reverse supply chain the used parts/products become part of forward supply chain. In this paper, a nonlinear maximizing profitability function for CLSC has been formulated for the system with a price dependent demand for n-stages. The decision variables are selling price of product and percentage return of graded used products entering into different stages. A numerical example for a three stage model illustrates the method followed by managerial insight.     

A new DEA method for supplier selection in presence of both cardinal and ordinal data

The success of a supply chain is highly dependent on selection of best suppliers. These decisions are an important component of production and logistics management for many firms. Little attention is given in the literature to the simultaneous consideration of cardinal and ordinal data in supplier selection process. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to identify most efficient supplier in presence of both cardinal and ordinal data. Then, utilizing this model, an innovative method for prioritizing suppliers by considering multiple criteria is proposed. As an advantage, our method identifies best supplier by solving only one mixed integer linear programming (MILP). Applicability of proposed method is indicated by using data set includes specifications of 18 suppliers.     

Configuring a supply network in the presence of volume discounts

This paper considers a configuration problem for a manufacturer’s supply network in the presence of volume discounts. An integrated mixed-integer nonlinear programming (MINLP) model is developed to simultaneously determine the selection of suppliers and production levels under an uncertain demand environment. The suppliers provide discounts in terms of the total value of purchased items instead of the quantity of items. The objective of the model is to maximize the manufacturer’s expected profit, subject to both manufacturer and supplier capacities. An external function is developed to deal with integral parts of the model and is integrated with the general algebraic modeling system (GAMS) and its solvers to solve the complex problem. Two numerical examples are used to illustrate the effectiveness of the proposed model and solution approach. Management insights from the sensitivity analysis and comparisons of volume discount are also discussed.     

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.     

Integrating carrier selection with supplier selection decisions to improve supply chain security

Supply chain security is a major concern for logistics managers who have responsibility for inbound and outbound shipments to and from both domestic and international locations. We propose here that logistics decisions concerning security in the supply chain will be made more effectively when made in concert with decisions in related supply chain processes, especially supplier and carrier selection. Indeed, managers may minimize cost, transit time, and security risk by integrating decision processes internally, as well as with their carrier's and supplier's operations. Thus, we account for both intra‐firm collaboration between logistics and purchasing managers, as well as inter‐firm collaboration among buyers, suppliers, and carriers in a supply chain. In this paper, we propose a decision process that features a set of security rules and a multi‐objective optimization model to accomplish this aim. We then provide an illustration to demonstrate the potential usefulness of these concepts in practice.     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

A tri-level programming model based on Conditional Value-at-Risk for three-stage supply chain management

For the problem of supply chain management, the existing literature mainly focuses on the research of the single-stage supply chain or the two-stage supply chain that consists of a manufacturer and a retailer. To our best knowledge, little attention has been paid to the study of a more extensive supply chain that consists of a material supplier, a manufacturer and a retailer, which is a more practical and interesting case. Therefore, based on the Conditional Value-at-Risk (CVaR) measure of risk management, this paper proposes a tri-level programming model for the three-stage supply chain management. In this model, the material supplier and the manufacturer maximize their own profit while the retailer maximize his/her CVaR of expected profit. Further, we show that the proposed tri-level programming model can be transferred into a bilevel programming model, which can be solved by the existing methods. Numerical results show that the proposed model is efficient for improving the risk management of the three-stage supply chain.     

Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme

Carbon emission tax is an important measure for sustainable supply chain management. This paper studies an optimal supplier selection problem in the fashion apparel supply chain in the presence of carbon emission tax. We consider the scenario in which there are multiple suppliers in the market. In the basic model, each supplier offers a supply lead time and a wholesale pricing contract to the fashion retail buyer. For the fashion retail buyer, the supplier which offers a shorter lead time allows it to postpone the ordering decision with updated and better forecast, and also a smaller carbon tax. However, the wholesale price is usually larger. We propose a two-phase optimal supplier selection scheme in which phase one filters the inferior suppliers and phase two helps to select the best supplier among the set of non-inferior suppliers by multi-stage stochastic dynamic programming. The impacts brought by different formats of carbon emission tax are explored. Finally, we examine an extended model in which there is a local supplier who offers a buyback contract and accepts product returns. Insights from the analysis are discussed.     

集成化服务供应链的物流服务商选择研究

在分析集成化服务供应链的基础上,从客户满意度、服务质量、服务成本、企业资质、协同能力和绿色竞争力六个方面,构建了比较全面客观的物流服务供应商评价指标体系。通过熵权计算出物流服务供应商权值向量和专家打分计算出各个二级指标的权重,构建定性和定量相结合的供应商评价模型,通过综合评价计算出各个物流服务供应商的评价总权值。最后,结合案例说明,建立的服务供应商评价指标体系和综合评价模型能够比较全面地评价供应商,为研究物流供应商选择提供有益参考价值。     

基于QFD和双层规划的物流服务供应商选择研究

从物流服务成本和物流服务质量两个方面构建物流服务供应商选择的双层规划模型,上层规划以物流服务成本最小为目标,下层以选择的供应商的综合表现度最大为目标。通过改进QFD模型,将评价指标和客户需求相结合,对待选物流供应商进行综合评价,计算供应商的综合表现度。结合模型的特点设计分层迭代算法,算例验证了模型和算法的有效性。     

A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon

Allocating orders to suppliers is of a great importance in managing the supply chain. This process comes after supplier selection and evaluation. Having selected the qualified vendors, the manager will examine the performance of each one and evaluate the individual vendor. According to the results of periodic evaluations, the manager allocates orders to suppliers. Splitting orders among suppliers is so important that it may influence the efficiency of the whole chain. In this paper, we propose a fuzzy multi-criterion model for organizing the process of assigning orders to suppliers. We adopt a fuzzy approach in order to overcome the vagueness of the information due to the uncertainty of them. MCDM methods are used to allocate suitable shares of orders to the best possible suppliers. Different from previous works, we discuss this problem over a multi-period time horizon; namely, the model leads to solutions which optimize the order in both the whole year and the fractional periods of a year. A fuzzy linear programming model is developed to optimize the price, quality and risk objectives and satisfy constraints like logistics costs, suppliers′ capacity, supply chain demand, risk, etc. A numerical example comes to validate our proposed model.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

基于第三方回收再制造逆向物流网络设计

针对再制造逆向物流网络设计问题, 在考虑产品回收量和消费市场需求量不确定的条件下, 以第三方物流企业收益最大化和制造商收益最大化为目标建立了基于第三方回收多周期再制造逆向物流网络模型. 利用该模型可以确定每个周期制造商购买第三方物流企业产品的最优价格, 可以确定网络中回收中心和检测/拆卸中心的开设数量以及各设施间的物流量分配. 将不确定参数用三角模糊数表示, 借助模糊机会约束规划方法将该模型转化为确定性等价模型. 利用算例验证了模型的有效性, 并用目标规划法分析了两个目标之间的关系.     

物流服务供应商选择的双层规划模型及云遗传算法*

在物流服务供应链体系下,针对功能型物流服务供应商的选择问题,提出了使用双层规划模型分析方法。上层规划以集成物流服务供应商的外包业务成本最低为目标,下层规划以客户服务满意度最大为目标。其中,在下层规划中提出客户服务满意度指数模型,并借助熵权法对模型中的各外显指标进行赋权,进而得出客户服务满意度指数。最后,结合模型特点设计了云自适应遗传算法,并借助算例验证了模型和算法的有效性。     

供应商竞争关系下的第三方物流融资及物流服务决策

研究第三方物流企业（3PL）在处于竞争关系中的供应商面临资金约束时的融资策略选择问题。考虑由两个竞争关系的供应商和一个零售商构成的两级供应链,3PL企业为供应链提供代采购、物流及融资服务,分别考虑在代采购中向供应商提供融资能够获得价格折扣和代采购中考虑额外资金成本情况下,3PL融资意愿选择,向供应商提供（不提供）融资。建立3PL、供应商和零售商在不同情况下的收益模型,并给出供应商和零售商的最优定价,以及3PL的最优物流服务收费决策,通过比较融资前后各个企业的收益分析3PL参与供应链融资的积极作用。研究发现3PL向处于竞争环境下的资金短缺供应商提供融资能够增加3PL、零售商和供应链整体的收益,且3PL更愿意在考虑自身资金成本的情况下向供应商提供融资,并且供应商之间竞争越激烈,3PL和零售商的获益越大。     

考虑碳排放量与数量折扣的闭环供应链网络设计与多目标决策优化研究

由于闭环供应链网络在环境法规、客户压力等方面都受到关注,故供应商选择在供应链管理中更具挑战性,本文所提闭环供应链网络模型可解决上述问题.其中,供应商会提供数量折扣以激励买家购买更多的产品.模型的目标函数是将经济成本与碳排放量降至最低,最大限度提升客户满意度等参数,并确定出最佳的供应商、采购量、运输方式、技术类型、碳排放...     

Joint optimization of production planning and supplier selection incorporating customer flexibility: an improved genetic approach

Efficient and effective production planning and supplier selection are important decisions for manufacturing industries in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, material) for economic reasons. Yet these two decision making problems have traditionally been studied separately due to their inherent complexity. This paper attempts to solve optimally the challenging joint optimization problem of production planning and supplier selection, considering customer flexibility for a manufacturer producing multiple products to satisfy customers’ demands. This integrated problem has been formulated as a new mixed integer programming model. The objective is to maximize the manufacturer’s total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to locate near-optimal solutions. This approach differs from a canonical genetic algorithm in three aspects, i.e., a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee the feasibility of the solutions. The computational results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance.     

Extracting performance rules of suppliers in the manufacturing industry: an empirical study

Performance evaluation of suppliers is increasingly recognized as a critical indicator in supply chain cooperation. Traditional performance evaluation methods have the problems of a simple buy/sell relation and in one’s subjective views between manufacturers and suppliers, and they lack objective automatic evaluation processes in the supply chain considered. Statistical techniques used for evaluation rely on the restrictive assumptions of linear separability, multivariate normality, and independence of the predictive variables. Unfortunately, many of the common models of performance evaluation of suppliers violate these assumptions. The study proposes an integrated model by combining K-means clustering, feature selection, and the decision tree method into a single evaluation model to assess the performance of suppliers and simultaneously tackles the above-mentioned shortcomings. The integrated model is illustrated with an empirical case study of a manufacturer for an original design manufacturer (ODM) to demonstrate the model performance. The experimental results indicate that the proposed method outperforms listed methods in terms of accuracy, and three redundant attributes can be eliminated from the empirical case. Furthermore, the extracted rules by the decision tree C4.5 algorithm form an automatic knowledge system for supplier performance evaluation.     

Structured methodology for supplier selection and evaluation in a supply chain

Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture.In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology.This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.     

A fuzzy ANP model for supplier selection as applied to IC packaging

Supplier selection has been a popular topic since the selection of the most appropriate supplier for cooperation is being increasingly important for the success of an enterprise. The problem is multi-criteria in nature, and a variety of multi-criteria decision making methodologies have been proposed. However, most of them did not take into account the interrelationships among the critical success factors and the fuzziness of the data involved in deciding the preferences of the factors. The objective of this research is to propose a fuzzy analytic network process model to evaluate various aspects of suppliers. With the consultation with the experts, the proposed model can consider the feedback and interdependency of factors in a network, and the factors are pairwise compared under an uncertain environment. The weights of factors can be calculated, and the final priority of suppliers obtained. Semiconductor industry becomes increasingly globalize competitive nowadays, and a good supply chain relationship is essential for a company to survive and to acquire reasonable profit. Thus, a case study of IC packaging company selection in Taiwan is presented, and the proposed model is applied to facilitate the decision process. The priorities of the factors and the ranking of the suppliers can be a recommendation or reference for decision makers when making a supplier evaluation and selection decision.