集成多准则供应商评价的产品配置方法研究

针对配置设计的产品在成本、质量、准时交货率等多方面受供应商影响的问题,提出一种集成多准则供应商评价的产品配置设计方法,包括配置模型的构建以及求解。首先采用TOPSIS方法对供应商进行多准则评价,将评价结果与模块实例相关联,构建集成化的配置模型。在配置模型的基础上,构建以供应商综合能力最优、成本最小为目标,客户的期望价格与配置规则为约束的集成供应商选择的产品多目标配置优化模型,并采用NSGA-Ⅱ算法求解。以高速列车转向架部分模块为例,证明了所提方法的可行性与有效性。     

敏捷制造(AM)环境下多供应商选择模型研究

针对多品种物资供应环境下多供应商选择的多目标问题,在多供应商选择特征分析的基础上,建立了以供应综合成本最低为目标函数、满足多个约束条件的细合优化模型.为解决求解的困难,采用变换分解算法,将原模型转换为整数规划问题.并且通过算例验证了模型及求解方法的正确性及有效性.新方法克服了常规供应商的选择评价方法只针对单一物资供应过程和面向单一供应商选择过程的局限性.     

基于供应链的供应商选择模型与算法

以石化企业为背景,研究了供应链中多供应商/多炼油厂的原油供应商选择问题,建立了以成本、质量、交货及供应链循环时间为目标的多目标采购优化数学模型,以实现供应商选择和最优采购计划的集成.然后,利用层次分析法(Analytic Hierarchy Process, AHP)与多目标规划相结合的方法求解模型得到供应商选择及优化采购方案.数值仿真结果表明所提出方法的有效性和实用性.     

基于供应链的供应商选择模型与算法

以石化企业为背景,研究了供应链中多供应商/多炼油厂的原油供应商选择问题,建立了以成本、质量、交货及供应链循环时间为目标的多目标采购优化数学模型,以实现供应商选择和最优采购计划的集成.然后,利用层次分析法(Analytic Hierarchy Process,AHP)与多目标规划相结合的方法求解模型得到供应商选择及优化采购方案.数值仿真结果表明所提出方法的有效性和实用性.     

改进蚁群算法在采购供应链组建中的应用

以电梯企业为背景,研究了采购供应链中多供应商选择问题,建立了以成本、质量、交货及售后服务等9项评价指标为目标的多目标采购优化数学模型,并采用层次分析法和改进的蚁群算法相结合的多目标规划方法对该模型进行求解,得到一组最优的供应商组合。最后,通过一个算例验证了该方法的有效性。     

改进蚁群算法在采购供应链组建中的应用

以电梯企业为背景,研究了采购供应链中多供应商选择问题,建立了以成本、质量、交货及售后服务等9项评价指标为目标的多目标采购优化数学模型,并采用层次分析法和改进的蚁群算法相结合的多目标规划方法对该模型进行求解,得到一组最优的供应商组合。最后,通过一个算例验证了该方法的有效性。     

基于改进粒子群算法的供应商参与可靠性设计优化

研究供应商参与下的汽车产品子系统可靠性设计的优化问题,考虑供应商参与产品设计的可信度因素,建立以最大化系统的可靠度和供应商的可信度为优化目标的多目标数学规划模型。通过加权的方法把多目标优化模型转化为单目标非线性整数规划模型。采用粒子群(Particle swarm optimization,PSO)算法进行求解,提出适用于“零部件—供应商”关系的离散粒子编码方法。设计带有自适应动态惩罚项的适应度函数,把优化问题转化为无约束优化问题,并将粒子的搜索范围扩展到近可行解空间,进而较好地改进了算法的搜索速度和收敛性能。以某中级轿车传动系统零部件可靠性设计的优化问题为实例,进行仿真研究,应用质量功能展开和模糊评判的方法生成了零部件的权重和供应商可信度初始数据值,仿真结果验证了所提出PSO算法的实用性和有效性。     

协同供应下的库存控制及供应商选择

针对制造企业的多源采购问题,以协同技术为基础,研究单一产品多源供应情况下的库存优化控制和供应商选择.基于供应商的能力约束,以平均库存成本最低为优化目标,建立了非线性整数规划模型,并提供了一种混合求解策略,为企业的多源库存控制提供理论依据.     

基于改进蚁群算法的多供应商选择问题求解

为克服传统供应商选择过程中只针对单一物资供应过程和面向单一供应商选择过程的局限性,以质量、成本、交货期和交货提前期为评估指标,以最小化评估指标综合值为目标,建立了针对多品种供应条件下多供应商选择的0-1整数规划模型.基于蚁群算法,构造了适合该模型特征的改进蚁群求解算法,并阐述了其求解过程.通过模拟算例及对比分析表明,该方法是有效、可行的,它可为企业进行多品种供应的多供应商选择问题提供了可参考的模型和求解算法.     

基于制造知识保护的供应商选择模型与实证研究

以影响企业长期竞争力的核心制造知识的获取与保护为目标，研究了外包中供应商选择问题；定义了制造知识，包括制造规范等文件化知识和以专家知识、技术Know-how为主体的非文件化知识。外包中，制造商与供应商互动过程中，会导致上述两类知识流动，外包决策必须考虑这种知识流动对企业长期竞争力造成的影响；依据供应商制造规范原创能力及其运作规范性将供应商分为4种类型；提出了基于上述目标的供应商类型与产品部件类型匹配模型，并通过2个企业360种零件与160家供应商的外包状况实证了所提模型的有效性。     

A multi-objective decision-making process of supplier selection and order allocation for multi-period scheduling in an electronic market

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems when suppliers have capacity or other different constraints, two questions persist: which suppliers are the best and how much should be purchased from a selected supplier? Here, we propose an integrated approach of analytic hierarchy process (AHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and multi-objective nonlinear programming to consider both tangible and intangible factors in choosing the best suppliers and define the optimum quantities among selected suppliers to maximize the total value of purchasing and minimize the budget, total penalized earliness and tardiness, and defect rate. The priorities are calculated for each supplier by use of AHP. TOPSIS is applied to rank the suppliers. Finally, using the obtained weights, the optimal quantities of order to the suppliers are clarified in multi-period horizon. An application study presents the validity and efficiency of the proposed model. Moreover, a performance analysis has been worked out on the numerical example to investigate the capability and effectiveness of the results.     

A fuzzy multi-objective programming model for supplier selection with volume discount and risk criteria

In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria. In this paper, a fuzzy multi-objective programming model is outlined to propose supplier selection taking quantitative, qualitative, and risk factors into consideration. Also quantity discount has been considered to determine the best suppliers and to place the optimal order quantities among them. The mixed integer derivative nonlinear programming is obtained from fuzzy multi-objective programming model by chance-constrained method. To solve this problem, an innovative method is proposed. In addition, several “what if” scenarios are facilitated. Finally, a real-life sample is used to validate the proposed model.     

信息不对称的折扣区间研究

为研究不完全合作情况下供需双方的折扣问题,运用运筹学理论,建立基于供应商的非线性规划模型,提出了供需双方都认可的折扣区间并进行了讨论,结果表明:供应商允许折扣上限不受信息对称与否的影响;如果供应商对采购商成本估计偏大,则折扣空间偏小,对供应商制定折扣策略不利;反之,如果供应商对采购商成本估计偏小,则放大了折扣区间范围,容易使谈判进入僵局.结论可以作为信息不对称情况下供应商确定折扣策略的依据.     

Possibilistic inventory and supplier selection model for an assembly system

In this paper, the problem of inventory lot-sizing and supplier selection for an assembly system is considered, where the supplier available capacities are assumed as ambiguous dynamic parameters. In this scenario, which is a frequent case in large assembly-based factories such as automobile manufacturers, the final product is assembled from multiple components with different conversion factors, which can be sourced from multi-capacitated suppliers through the multi-period horizon of imprecise demand. Due to high shut-down costs of assembly lines, it is assumed that production never stops even though some components may not be available. Therefore, the unfinished products are transferred to a buffer zone and preserved there until the lacking components become available. In this study, a possibilistic mixed integer mathematical model, with fuzzy objective function and soft constraints, is developed to determine which component in what quantities, from which suppliers, and in which periods should be ordered. The model, inspired by the real case of the Iran Khodro Car Company, aims to maximize the profit while keeping a high customer service level by avoiding shortages. This model also considers the ambiguity of dynamic parameters such as demand, suppliers’ available capacities, prices, and holding and shortage costs. To solve the problem, the possibilistic model is first converted into an auxiliary crisp multi-objective model. Through an interactive fuzzy approach, the suggested multi-objective problem is then transformed into an equivalent single-objective model. Finally, a particle swarm optimization is proposed to achieve the overall satisfactory compromise solution. A numerical sample is used to validate the proposed model.     

Multi-objective supply chain sourcing strategy design under risk using PSO and simulation

Sourcing strategy design in a supply chain is vital to gain competitive advantage. In recent years, supply chain risks are growing significantly and supplier failure is identified as one of the top supply chain risks. Researchers attempt to mitigate the negative impacts of supplier failure by applying strategies such as local versus global sourcing, single versus dual/multiple-sourcing, performance-based supply contracts, and optimizing the order allocation among suppliers. Global sourcing is a widely recognized strategy among firms, and it involves a trade-off between reliable, high-cost local suppliers and unreliable, low-cost offshore suppliers. The global sourcing is associated with the risks of exchange rate volatility, trade restrictions, longer lead time, and problems with supplier reliability. Sourcing strategy design considering price, exchange rate risks, and supplier delivery reliability is an important research topic and needs attention. In this work, a hybrid optimization and simulation approach is proposed to design the supply chain sourcing strategy. In the optimization approach, a multi-objective binary particle swarm algorithm is developed for minimizing the total cost and maximizing the supplier delivery reliability. Selected scenarios from the optimization results are modeled using Witness simulation software to evaluate the robustness of sourcing strategies under price, exchange rate and demand risks. The proposed approach is exemplified using a real-life case study of a plastic product manufacture in India.     

Multiple sourcing decisions using integrated AHP and knapsack model: a case on carton sourcing

Purchase allocation is a multi criteria decision making (MCDM) problem. Multitude of qualitative and quantitative factors is involved in the multiple sourcing decisions. Analytic hierarchy process (AHP) has widely been used to find out the relative rankings of suppliers. AHP can be combined with regular supplier quantitative audit process. In classical AHP, decision maker (DM) has to pair wise compare suppliers for each factor, whereas the proposed audit based simplified AHP will remove the complexity of comparison. Quarterly audit-based AHP rankings and supplier performance probability products can be used in place of revenues in the backward recursive resource allocation knapsack model. This combined model will decompose purchase allocation problem into different stages and combine one supplier at each stage and provide the optimum and feasible solution in the end. Solution at each stage is also a feasible option. This model is only applicable when the total order quantity and the capacity of all suppliers are integer multiples of economic or minimum order quantity. This integrated model thus provides number of orders/supplier.     

Linear approach for solving a piecewise linear vendor selection problem of quantity discounts using lexicographic method

The multi-objective problem pertaining to vendor selection becomes complicated with the inclusion of a discount pricing schedule due to its nature of piecewise linearity. To overcome the difficulty of solving such a piecewise linear multi-objective problem, a linear approach is described in this paper. The use of the lexicographic method enables the decision-maker to establish the limit for defective components and late deliveries as constraints in the model. Demand can be exactly met considering the defective components present in the supply. Applying this approach to a manufacturing firm has proved its practicality. Also, the proposed methodology can be effectively used in JIT/Lean supply environments by fixing the number of vendors .     

Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria

Simulation optimization is providing solutions to practical stochastic problems. Supplier selection is one of the most important decisions that determine the survival of an organization. In this paper, a novel multi-objective simulation optimization method to make decisions on selecting the suppliers and determining the order quantities is proposed. Regarding the fact that a real supply chain is multi-objective with uncertain parameters and includes both quantitative and qualitative variables, the proposed method considers these points and is applicable to real-world problems. This method also considers supplier selection and order quantity allocation to each supplier, which are totally related, as an integrated model. The proposed method consists of four basic modules: Cuckoo Optimization Algorithm (COA), Discrete Event Simulation (DES), Supply Chain Model (SCM), and Generalized Data Envelopment Analysis (GDEA). Unlike many multi-objective methods, the proposed method is not limited to the number of objective functions and this is one of its main benefits. It also pays attention to the efficiency of the organization and, at the same time, finding inputs which result in best output amounts. This method, in addition to the convergence criterion, pays special attention to the dispersion of the Pareto frontier as the second criterion for choosing the good solutions. For implementation of the proposed method, the numerical results for the problem of supplier selection in multi-product, multi-customer modes, and uncertain and qualitative variables are discussed and the Pareto frontiers are presented. The proposed method in this paper is compared with a similar method, and the results show the efficiency of the proposed method.     

Synchronization in multi-echelon supply chain applying timing discount

Due to the increasing emphasis on the effective management of the supply chain, synchronization and cooperation issues between suppliers and retailers in decentralized multi-echelon inventory/distribution systems have gained much attention in the recent years. In this paper, we consider coordination issues of a distribution system composed of a manufacturer, a supplier (distributor), and several retailers. The supplier outsourcing a third party offers a timing discount to multiple retailers in order to synchronize the timing of their orders with the order cycle. It is also assumed that retailers are allowed to face stock outs. In this paper, a mathematical model is developed to analyze the problem. Results show that while synchronizing the supplier and the outsourcer enhances the supply chain efficiency, offering any type of timing discount by the supplier decreases the supply chain efficiency. It is also noted that having coordination between the manufacturer and the supplier has no impact on the supplier’s profit but may decrease the retailer’s costs. It is believed that our findings provide potential and significant managerial implications in the area of supply chain coordination when these systems are decentralized.     

多产品采购条件下的供应商选择与订购量分配问题研究

为了解决随机性需求和价格折扣并存条件下的多产品采购供应商选择问题,建立了相应的多目标混合整数随机规划模型.该模型的特点是：①模型的约束条件中兼具确定性和随机性;②通过约束条件方程式准确地表现随机性需求和价格折扣两大假设条件.根据该模型的特殊结构,提出了一种适用的求解策略：首先,通过把机会约束转化为确定性等价类,而将多目标混合整数随机规划模型转化为多目标混合整数规划模型;然后,采用最大满意度法,将体现决策者偏好和目标模糊性的加权模糊多目标混合整数规划模型转化为求解等价的多个单目标混合整数规划问题;最后,在确定每个模糊目标的隶属度函数表达式的基础上求得问题的最优解.另外,通过应用算例说明了模型的有效性和可行性.