Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing

The coordinated supplier selection and customer order scheduling in the presence of supply chain disruption risks is studied for single and multiple sourcing strategies. Given a set of customer orders for products, the decision maker needs to select a single supplier or a subset of suppliers for purchasing parts required to complete the customer orders, and schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The suppliers are located in different geographic regions and the supplies are subject to different types of disruptions: to random local disruptions of each supplier individually, to random regional disruptions of all suppliers in the same region simultaneously and to random global disruptions of all suppliers simultaneously. For any combination of suppliers hit by different types of disruptions, a formula for calculating the corresponding disruption probability is developed. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional value-at-risk as a risk measure. The problem objective is either to minimize expected worst-case cost or to maximize expected worst-case customer service level, i.e., the expected worst-case fraction of customer orders filled on or before their due dates. The risk-averse solutions that optimize worst-case performance of a supply chain under disruptions risks are compared for the two sourcing strategies and the two objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

A mixed-integer non-linear program to model dynamic supplier selection problem

In a highly competitive scenario, suppliers play a vital role in making a business organization successful. Business of any organization is continuous process and therefore the supplier selection is also dynamic in nature. This is quite natural as the organization’s demand; supplier’s capacity, quality level, lead time, unit part cost and fixed transportation cost of supplier varies with time. Therefore, supplier identified for one period may not necessarily be same for the next period to supply the same set of parts. Hence, the supplier selection problem is highly dynamic in real practice. In this paper, a mixed-integer non-linear program (MINLP) is developed to address the dynamic supplier selection problem (DSSP). To validate the proposed MINLP data are generated randomly. A numerical illustration is also provided to demonstrate the proposed MINLP using LINGO.     

Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem

Supplier selection is nowadays one of the critical topics in supply chain management. This paper presents a new decision making approach for group multi-criteria supplier selection problem, which clubs supplier selection process with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner similar to the decision makers who have gathered considerable knowledge and expertise in procurement domain. Nevertheless, under many conditions, exact data are inadequate to model real-life situation and fuzzy logic can be incorporated to handle the vagueness of the decision makers. As per this concept, fuzzy-AHP method is used first for supplier selection through four classes (CLASS I: Performance strategy, CLASS II: Quality of service, CLASS III: Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using simulation based fuzzy TOPSIS technique, the criteria application is quantitatively evaluated for order allocation among the selected suppliers. As a result, the approach generates decision-making knowledge, and thereafter, the developed combination of rules order allocation can easily be interpreted, adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the results analyzed.     

Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain

Environmental sustainability of a supply chain depends on the purchasing strategy of the supply chain members. Most of the earlier models have focused on cost, quality, lead time, etc. issues but not given enough importance to carbon emission for supplier evaluation. Recently, there is a growing pressure on supply chain members for reducing the carbon emission of their supply chain. This study presents an integrated approach for selecting the appropriate supplier in the supply chain, addressing the carbon emission issue, using fuzzy-AHP and fuzzy multi-objective linear programming. Fuzzy AHP (FAHP) is applied first for analyzing the weights of the multiple factors. The considered factors are cost, quality rejection percentage, late delivery percentage, green house gas emission and demand. These weights of the multiple factors are used in fuzzy multi-objective linear programming for supplier selection and quota allocation. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model. The proposed approach can handle realistic situation when there is information vagueness related to inputs.     

A supplier selection and order allocation problem with stochastic demands

We consider a system comprising a retailer and a set of candidate suppliers that operates within a finite planning horizon of multiple periods. The retailer replenishes its inventory from the suppliers and satisfies stochastic customer demands. At the beginning of each period, the retailer makes decisions on the replenishment quantity, supplier selection and order allocation among the selected suppliers. An optimisation problem is formulated to minimise the total expected system cost, which includes an outer level stochastic dynamic program for the optimal replenishment quantity and an inner level integer program for supplier selection and order allocation with a given replenishment quantity. For the inner level subproblem, we develop a polynomial algorithm to obtain optimal decisions. For the outer level subproblem, we propose an efficient heuristic for the system with integer-valued inventory, based on the structural properties of the system with real-valued inventory. We investigate the efficiency of the proposed solution approach, as well as the impact of parameters on the optimal replenishment decision with numerical experiments.     

考虑风险规避与质量和服务水平的VMI供应链期权协调策略

针对一个考虑风险规避供应商与质量和服务水平的二级VMI供应链,应用条件风险价值(CVaR)准则刻画供应商的风险规避行为,提出由期权和成本分担构成的组合契约,构建以零售商为主导的Stackelberg博弈模型,探讨供应链协调策略以及风险规避对供应链协调和利润分配的影响.研究表明,供应商的最优生产量随着其风险规避程度的增加而减小,但最优质量和服务水平与风险规避程度无关;当且仅当供应商风险规避程度较低时供应链才能实现协调,且供应商风险规避程度是影响供应链契约设计和利润分配的关键因素.     

基于蚁群算法的MC供应链调度优化研究

为解决大规模定制模式下客户订单分离点后的动态供应链调度问题，提出了包括供应商选择及企业合作时序安排的优化调度模型，设计了基于蚁群算法的求解过程。通过多组数据实验及结果比较分析，对模型算法的有效性、稳定性进行了验证。     

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.     

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、碳排放量、库存量及工厂间运输流量.本研究基于MATLAB R2010a软件包对测试问题进行模型验证及敏感性分析,结果表明:考虑数量折扣后,可显著降低供应链的总成本.随着碳排放成本的上升,供应链的总成本也会随之上升,随之碳减排率也会不断提升.可知该模型具有有效性与实用性,可为供应链网络设计者提供决策依据,为政府制定碳补贴政策实现减排提供理论依据.     

A decision support system assessing the trust level in supply chains based on information sharing dimensions

Assessing customer trust in suppliers with regards to its influencing factors is an important open issue in supply chain management literature. In this paper, a customer trust index is designed as the trust level arising from the information sharing degree and quality, related to the information shared by a supplier with his customer. The customer trust level is evaluated using a fuzzy decision support system integrating information sharing dimensions. The core is a rule-based system designed using the results of questionnaires and interviews with supply chain experts. Several tests were generated in order to analyze the impact of the different information sharing attributes on the customer trust index. The developed approach is then applied to a real supply chain from the textile industry. Results show large differences of weight and impact between the different information-related factors that build the customer trust index. It is also shown that the proposed system has an important role in ensuring the objectivity of the trust assessment process and in helping decision makers evaluate their business partners.     

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。     

Supply chain management: a modular Fuzzy Inference System approach in supplier selection for new product development

The critical objectives of purchasing departments include obtaining the product requested, at the right cost, in the right quantity, with the best quality, at the right time, from the right supplier. These goals require effective decisions concerning supplier selection at the early stage of product development. This work provides an application of fuzzy set theory in supply chain management, specifically in supplier selection for new product development. Here, a Fuzzy Inference System is proposed as an alternative approach to handle effectively the impreciseness and uncertainty that are normally found in supplier selection processes. This paper also shows that the proposed decision-making model is applicable to any supply chain system.     

A fuzzy solution approach for multi objective supplier selection

In today’s severe competitive environment the selection of appropriate suppliers is a significantly important decision for effective supply chain management. Appropriate suppliers reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. In this study a multiple sourcing supplier selection problem is considered as a multi objective linear programming problem. Three objective functions are minimization of costs, maximization of quality and maximization of on-time delivery respectively. In order to solve the problem, a fuzzy mathematical model and a novel solution approach are proposed to satisfy the decision maker’s aspirations for fuzzy goals. The proposed approach can be efficiently used to obtain non-dominated solutions. A numerical example is given to illustrate how the approach is utilized.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

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.     

An interactive possibilistic programming approach for a multi-objective closed-loop supply chain network under uncertainty

In this article, we first propose a closed-loop supply chain network design that integrates network design decisions in both forward and reverse supply chain networks into a unified structure as well as incorporates the tactical decisions with strategic ones (e.g., facility location and supplier selection) at each period. To do so, various conflicting objectives and constraints are simultaneously taken into account in the presence of some uncertain parameters, such as cost coefficients and customer demands. Then, we propose a novel interactive possibilistic approach based on the well-known STEP method to solve the multi-objective mixed-integer linear programming model. To validate the presented model and solution method, a numerical test is accomplished through the application of the proposed possibilistic-STEM algorithm. The computational results demonstrate suitability of the presented model and solution method.     

Capacity-constrained supplier selection model with lost sales under stochastic demand behaviour

In today’s market conditions, volume of demand is quite uncertain and thus it is hard to estimate. In many cases, buyer is prone to use supply chain flexibility rather than inventory holding strategy to withstand demand uncertainty. We assume that the buyer releases a replenishment order to the supplier for each cycle (or period) under the contract which is mainly composed of four parameters: (1) supply cost per unit, (2) minimum order quantity, (3) order quantity reduction penalty and (4) maximum capacity of the supplier. Based on these parameters, there are two flexibility options that buyer should evaluate in the order of cycle (1) issue an order smaller than the minimum order quantity and pay the related penalty and (2) place no order and lose the sales. Hence, Q _lost emerges as a critical buyer decision, the order quantity, below which no order is placed. Total expected supply cost plus lost sales, as a function of Q _lost is presented. We derive the optimal Q _lost that minimises the total cost function. Since capacity of each supplier is finite, we then develop a supplier selection model with total cost minimisation over the suppliers subject to capacity constraint that has a stochastic nature stemming from demand behaviour. Linearisation on the model is performed using chance-constrained programming approach. From a given set of supply bids from the potential supply chain partners, the buyer is able to make a quantifiable choice.     

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.     

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.     

Vertical pricing competition in supply chains: the effect of production experience and coordination

We consider a two‐echelon supply chain consisting of a single supplier (producer) and a retailer. The supplier determines the wholesale price with a production cost decreasing with experience. The retailer orders products from the supplier to meet demands. Negative effects of a vertical competition in static supply chain models are typically attributed to a double marginalization. Using an intertemporal supply chain problem, defined by a differential game, we show that in addition to the “cost” of double marginalization, the margin gained from reducing production costs affects the supply chain performance as well. In our analysis, performance is shown to deteriorate even more than the deterioration observed in static problems with no learning (experience). To improve the performance, we provide a time‐variant version to the well‐known, pure, two‐part tariff strategy, which in its dynamic framework may coordinate the supply chain only partially. Efficient coordination in a supply chain is shown to be possible if a mixed two‐part tariff strategy is employed, however.