可持续闭环供应链网络设计的多目标模糊规划问题

为解决不确定条件下可持续闭环供应链网络设计的问题,以成本和环境影响最小、社会影响最大为目标,建立带有模糊参数的多目标闭环供应链网络规划模型.首先采用Me测度处理相关模糊目标和参数,并运用加权增广Epsilon-约束方法解决多目标问题,在此基础上设计一种基于[0, 1]随机数的双层编码遗传鲸鱼(GA–WOA)混合算法进行求解,然后构造多个不同规模算例,将混合算法求解结果与CPLEX、遗传算法的求解结果进行对比,结果证明该编码方式和混合算法具有合理性.最后针对模型的多个参数变化进行分析,以验证所建模型的可行性.     

电网中断下基于DWOA的生鲜产品闭环供应链网络多目标模糊优化设计

针对生鲜产品供应链网络设计问题,建立了一种电网中断下的闭环生鲜供应链网络多目标模糊优化设计模型,以此解决供应链网络设计中的不确定性问题.首先,针对电网中断下生鲜产品闭环供应链网络结构设计,建立目标为成本最小、碳排放最少、中断时间最短的优化函数,采用Me测度和三角模糊数对该模型进行处理,将多目标问题转换为单目标问题;其次,在原有鲸鱼算法的基础上,引入差分算法的交叉和变异理念,增强其搜索能力,改善其局限性,得到改进差分鲸鱼优化算法(DWOA),并采用此方法对处理后的模型求解;最后,通过数值实例和敏感性分析表明,提出的算法和模型在处理生鲜产品供应链网络优化设计方面具有较强的求解能力,且计算时间较短.     

供应链中二级分销网络优化设计的模糊机会约束规划模型

提出了供应链中二级分销网络优化设计的模糊机会约束规划模型. 模型中将各个需求地对产品的需求量以及各分厂的生产能力等难于确定的参数看成是模糊参数, 并进一步讨论了如何将模型中的机会约束清晰化. 文中还讨论了采用启发式算法同分枝定界法相结合以提高问题的求解速度.     

不确定经济环境下基于EVA的鲁棒供应链网络设计模型

针对由制造商、仓库、分销中心和客户组成的四级供应链网络设计问题,考虑以产品需求、短期利率、长期 利率、无风险利率、预期市场回报、证券承销费用和市场流动性等因素描述的经济环境不确定性,建立以供应链 经济增加值绩效为目标,网络设计、物流和财务运作为约束条件,设施选择、连接路径等为网络要素,生产、运输等物 流量和负债、保理、应收账款等财务项为决策变量的多产品、多周期供应链网络鲁棒设计模型.数值结果表明,基于 经济增加值的鲁棒供应链网络能够有效应对经济不确定性的影响.特别地,与传统随机优化方法相比,鲁棒优化能够确保供应链网络具有更好的鲁棒性和财务状况.     

煤炭运输路径选择与运输效率的关系模型仿真

在煤炭运输路径优化选择问题的研究中,煤炭运输网络在雨雪、泥石流、地震等突发事件下会发生路线的重新选择,选择路线过程带有波动性与随机性,受主观人为因素影响较大.传统的煤炭运输供应链路径选择与运输效率的关系模型建立方法无法对瞬时变化的路线选择进行自适应调整,无法建立包含路线突变情况的约束条件,建立的模型准确性较低.提出基于关联模糊理论的煤炭运输供应链路径选择与运输效率的关系模型建立方法,塑造可量化的煤炭运输供应链网络演化模型,依据供应链网络鲁棒性新测度,根据小范围供应链效率和连同图大小运算网络中单个节点的运输效率,将量化后的鲁棒性与效率的关系划分成两个独立的特征子空间以及非特征子空间,准确描述两者关系.仿真结果表明,在不同的干扰环境下新建模型能够较好地表述煤炭供应链路径选择与效率的关系.     

基于复杂网络的供应链网络鲁棒性分析

供应链网络经常面临各种突发事件的干扰,使网络用户受到损失。为更好地提高供应链网络的鲁棒性能,采用复杂网络理论构建了带有可调参数的供应链网络演化模型。在给出供应链网络鲁棒性新测度的基础上,分析了在受到随机干扰和选择性干扰的情形下,用不同拓扑结构的供应链网络模型进行仿真分析。仿真结果表明:供应链网络的拓扑结构对供应链网络的鲁棒性有重要的影响,通过调整模型中的参数,改变网络的演化机制,可以达到改善供应链网络鲁棒性的目的,并对于提高供应链网络的抗干扰能力具有重要的现实和理论意义。     

考虑偏好与合作的供应链网络设计与优化

提出了考虑核心企业决策偏好与成员企业合作意愿的双目标供应链网络设计与优化问题。应用模糊多目标规划方法,将决策者对各个目标的不精确期望水平考虑到供应链网络的设计与优化模型中。研究了集中决策模式下核心决策者的不同目标偏好与分散决策模式下各组织成员群体决策的合作意愿,对供应链的各个目标及网络优化配置的影响。结果表明,“极大极小”算子体现了核心决策者对各个目标的偏好,但大权重目标以牺牲小权重目标的值为代价的。新的“模糊与”补偿算子能使供应链的各个目标值获得理想的均衡效果,体现了供应网络中核心企业与其他成员企业群体协商决策的管理思想与行为特征。     

考虑运输中断的可靠三级供应链网络设计

现实供应链网络中每条运输路径都存在中断风险，只有合理地选择供应设施及其运输路径才能在一定程度上保证整个网络的运输可靠性。在设施能力约束下，为了保证总运营成本的最小化和整个网络的运输可靠性的最大化，提出了一个新的三级供应链网络设计的两目标混合整数规划模型。利用改进的非支配排序遗传算法（NSGA-II）求解提出的模型，并根据决策变量之间的关系设计新的染色体解码方法。通过路径可靠度对比实验和算例规模对比实验，不仅分析了模型的灵敏度和算法的有效性，还讨论了设施能力约束对算例结果的影响。构建的LRP模型及算法的研究能有效地帮助决策者设计出低成本、高可靠性的三级供应链网络。     

可持续闭环供应链网络的多目标优化研究

针对可持续的闭环5供应链网络优化问题,考虑柔性供应策略、设施改造对网络优化的影响,并以经济成本最小、碳排放量最低、社会效益最大为优化目标,构建了多目标的多情景-模糊优化模型。针对模型中的不确定参数,用三角模糊数与多情景优化的方法处理其模糊性和随机性,然后根据不同的分析角度,分别用线性加权法和NSGA-[Ⅱ]算法对模型进行求解,基于算例的求解结果,对目标权重的灵敏度、模型的稳健性以及目标间的冲突性进行了分析。证明了考虑柔性供应策略和设施改造问题的必要性;给出了闭环供应链网络设计的决策建议。     

多层供应链网络中欠载失效和过载级联失效的协同演化研究

供应链网络与我们的生活密切相关,而供应链网络的级联失效问题一直是研究的重点.文中提出了一种多层供应链网络混合失效模型,更好地模拟了真实供应链网络欠载失效和过载级联失效的过程,为预防供应链网络崩溃提供了参考.首先,建立了上层供应商网络过载级联失效模型和下层零售商网络欠载失效模型,它们共同构成了上下层网络混合的供应链网络模型;然后,通过采用不同的攻击方式攻击上下层网络,分析了供应链网络的失效迭代过程和脆弱性.在初始攻击比例一定的情况下,上层供应商网络相较于下层零售商网络有更强的鲁棒性,初始攻击节点为上层供应商网络节点时,网络崩溃的阈值更低,更容易使供应链网络发生全面崩溃.仿真结果验证了模型的有效性,为预防供应链网络崩溃提供了新的研究模型.     

基于库存切换的不确定动态供应链网络系统模糊鲁棒控制

研究基于库存切换的不确定动态供应链网络系统的鲁棒运作问题.首先,建立含有系统参数不确定和顾客需求不确定的动态供应链网络的离散T-S模糊模型;然后,提出一种新的模糊鲁棒控制策略,该控制策略不仅可以有效遏制子系统切换时产生的较大波动,而且可以抑制不确定因素对供应链网络的影响,进而保证供应链网络在不确定环境下鲁棒稳定;最后,通过仿真实例表明了所提出模糊鲁棒控制策略的有效性.     

Modeling of a Fuzzy Expert System for Choosing an Appropriate Supply Chain Collaboration Strategy

Nowadays, there has been a great interest for business enterprises to work together or collaborate in the supply chain. It is thus possible for them to gain a competitive advantage in the marketplace. However, determining the right collaboration strategy is not an easy task. Namely, there are several factors that need to be considered at the same time. In this regard, an expert system based on fuzzy rules is proposed to choose an appropriate collaboration strategy for a given supply chain. To this end, firstly, the factors that are significant for supply chain collaboration are extracted via an extensive review of literature. Then, a simulation model of a supply chain is constructed to reveal the performance of collaborative practices under various scenarios. Thereby, it is made possible to establish fuzzy rules for the expert system. Finally, feasibility and practicability of our proposed model is verified with an illustrative case.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

A fuzzy QFD approach to determine supply chain management strategies in the dairy industry

The aim of this study is to identify the crucial logistics requirements and supply chain management (SCM) strategies for the dairy industry. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of design requirements and supply chain management strategies are important issues during QFD processes for product or service design. For this reason, a fuzzy QFD methodology is proposed in this study to determine these aspects and to improve customer satisfaction. Qualitative information is converted firstly into quantitative parameters, and then this data is combined with other quantitative data to parameterize two multi-objective mathematical programming models. In the first model, the most important logistic requirements for the company are determined based on total technical importance, total cost, total feasibility and total value increment objectives, and in the second model, based on these objectives, appropriate supply chain management strategies are determined. Finally, a case study from the Turkish dairy industry is given to illustrate the proposed approach.     

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.     

T-S模糊神经网络应用于变参数系统的控制研究

针对基于模型的传统控制策略在线性时变系统中的应用受到系统的时变性和不确定性限制,通常难以获得理想的控制性能这一问题,提出了线性时变系统的一种变参数系统模型。该模型具有有界性和不确定性特点,利用模糊神经网络具有的自学习能力强、模型依赖性小以及鲁棒性强的优点,提出一种基于遗传算法的T-S模糊神经网络控制器对其进行控制研究,并通过仿真实验证明了该模糊神经网络控制器对变参数系统控制的可行性与有效性,为线性时变系统的控制问题提供了一种新思路。     

Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks

Nowadays, supply chains play an inevitable role in prompt handling of varying customers’ needs. Administration of a successful supply chain depends on how efficiently the network design is accomplished. Therefore, a supply chain network design problem is considered in this paper. The network addresses an uncertain environment threatened by different risk sources in order to captivate the real world conditions. A mixed-integer non-linear mathematical model is developed in which the uncertainties are represented by the fuzzy set theory. Benders decomposition is then applied to solve the proposed problem; consequently, the model is transformed into a mixed-integer one. Moreover, an interactive resolution method is applied to provide the decision maker with alternative decision plans in regard to different satisfaction degrees. Finally, the accuracy of the proposed model is checked by sensitivity analysis test and its performance is considered by different numerical examples.