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.     

基于系统动力学的大型客机供应链质量管控契约优化

研究再制造视角下我国大型客机供应链质量控制问题.根据大型客机制造三级供应链中供应商、主制造商及销售商的运作特点,刻画三者内在的动力学参数特征;构建基于质量奖惩和成本分摊的供应链控制契约,建立主制造商质量控制契约协调优化规则;设计大型客机供应链质量控制仿真的系统动力学模型;以液压系统为例仿真分析管控策略,并通过对比分析得出管控策略的有效性.     

基于双层规划的多供应商物料配送期量优化

为有效解决多供应商单制造商构成的二级供应链中供应商配送批量不经济、配送时间无序导致物流成本高和卸货拥堵等问题,建立多供应商配送期量的双层规划模型.上层模型从库存与配送联合优化视角,考虑供应商配送批量经济性,以供应链物流总成本最低为目标,确定最优配送批量;下层模型基于上层优化结果,考虑制造商生产时序性,以供应商时间惩罚成本最小化为目标,确定供应商配送时间序列.采用遗传算法和动态规划相结合的方法求解上下层模型,得到供应商最优配送期量标准.通过实例运用,验证了模型与算法的有效性及可行性.     

基于双层规划的多供应商物料配送期量优化

为有效解决多供应商单制造商构成的二级供应链中供应商配送批量不经济、配送时间无序导致物流成本高和卸货拥堵等问题,建立多供应商配送期量的双层规划模型.上层模型从库存与配送联合优化视角,考虑供应商配送批量经济性,以供应链物流总成本最低为目标,确定最优配送批量;下层模型基于上层优化结果,考虑制造商生产时序性,以供应商时间惩罚成本最小化为目标,确定供应商配送时间序列.采用遗传算法和动态规划相结合的方法求解上下层模型,得到供应商最优配送期量标准.通过实例运用,验证了模型与算法的有效性及可行性.     

面向网购的低碳供应链设计模型及其应用分析

针对网购中的低碳供应链网络设计问题,利用凹函数对碳排放成本、物流成本和配送中心固定成本之和进行最小化建模.由于该模型不能被直接求解,首先,应用拉格朗日松弛法将其分解成单一供应商选址和凹背包两个问题,在此基础上提出了基于子问题求解的拉格朗日启发式算法;然后,从供应链网络运营成本和供应链网络设计两个方面进行应用分析.结果表明,考虑碳排放成本的供应链设计能够优化网购环境下低碳供应链资源的配置.     

复杂产品供应链质量控制方案递阶决策模型

提出一类供应链质量控制方案递阶决策模型,辅助复杂产品主制造商更好地推广和落实质量控制方案,以实现供应链整体质量的最优改善.首先搭建了一类新型的供应链质量屋分析平台,并将其表征为多目标规划模型,确定最优方案组合;然后,基于复杂产品供应链多层级结构设计质量屋网络,以控制资源为纽带构建多级多目标递阶决策模型,实现了质量控制方案的继承和衔接;最后,针对某型号商用飞机供应链开展案例研究,验证了所提出模型和方法的可行性和有效性.     

装备制造企业供应商综合评价系统

针对国内装备制造业特点,分析了全球供应链条件下的供应商评价体系.分析了国内装备制造产品的主要特征,如产品装配过程复杂、供应链管理复杂、产品构型管理复杂等.提出了一种新的供应商评价方法.设计了一种基于三角模糊数的评价指标体系构建方法.应用层次分析得到评价指标权重,采用灰色关联度对企业供应商综合能力进行评估;以某装备制造企业供应商管理为研究背景,建立了供应商评价模型,通过应用实例分析说明了本方法的实用性     

考虑保质期的两级供应链MTS-MTO提前期优化研究

在具有保质期特性的快消品行业中,供应商在面向订单(MTO)生产策略下较长的订货提前期会给零售商带来高库存以及高运作成本,削弱供应链的快速响应能力.对比原来的MTO生产策略,提出一种面向库存-面向订单(MTS-MTO)混合生产策略.首先,基于产品保质期的特点建立MTO生产策略下的供应链成本模型,并给出供应链成本最优的经济订货批量;其次,根据MTO求出的最优经济订货批量,建立MTS-MTO生产策略下的供应链成本模型,并确定供应商的应持有库存M,求出供应商在MTS-MTO生产时的充要条件,为供应链决策提供依据;再次,给出一种基于满意度的Nash协商模型以确定零售商应给予供应商的补偿H;最后,通过数值分析验证并讨论符合MTS-MTO生产的产品特征.     

多周期多原料的供应链网络成本优化及其混合遗传算法

建立并求解一个基于成本最小的供应链网络模型.与以往研究不同,在该模型中生产一种产品需要至少两种原料,每种原料都可以由备选供应商提供.根据模型的特点,用0、1代表对原材料供应商、工厂和分销中心的选择情况,以MATLAB 7.6为平台,运用Sheffield大学的遗传算法工具箱,将遗传算法与线性规划算法相结合,实现了模型的求解.算例结果表明,给出的染色体编码方案正确,混合遗传算法有效,能解决多周期、多原料的供应链网络成本优化问题.还探讨了需求和距离变化,以及需求随机时对最优成本和最优个体的影响.研究表明,需求变化的影响大于距离变化的影响,需求随机对最优成本和最优个体的影响不大.     

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.     

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.     

Simulation-based heuristic method for container supply chain network optimization

With the emerging of free trade zones (FTZs) in the world, the service level of container supply chain plays an important role in the efficiency, quality and cost of the world trade. The performance of container supply chain network directly impacts its service level. Therefore, it is imperative to seek an appropriate method to optimize the container supply chain network architecture. This paper deals with the modeling and optimization problem of multi-echelon container supply chain network (MCSCN). The problem is formulated as a mixed integer programming model (MIP), where the objective is subject to the minimization of the total supply chain service cost. Since the problem is well known to be NP-hard, a novel simulation-based heuristic method is proposed to solving it, where the heuristic is used for searching near-optimal solutions, and the simulation is used for evaluating solutions and repairing unfeasible solutions. The heuristic algorithm integrates genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method and give some managerial implications.     

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.     

基于改进HBA算法的生鲜闭环供应链网络鲁棒优化设计

针对生鲜闭环供应链网络设计问题,建立了一种基于生鲜闭环供应链网络的鲁棒优化模型,以解决供应链网络中的不确定性问题。首先,针对涵盖五个节点的生鲜供应链网络结构建立了多周期、多产品,以最小化成本、最小环境影响为目标的混合整数规划模型,采用模糊折中规划与区间数据鲁棒优化方法进行处理;其次,在原有蜜獾算法的基础上引入差分进化原则,增强算法的全局搜索能力与收敛速度;最后,通过MATLAB数值分析与仿真实例表明,所提鲁棒优化模型与蜜獾算法在求解生鲜闭环供应链网络设计问题中具有明显优势。     

A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration

Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.     

Efficient multi-objective optimization of supply chain with returned products

Closed loop supply chain aims at integrating return products in the traditional supply chain processes. The return flows generate new uncertain elements (returns and leadtimes) and optimization of inventory control in this context is a complex issue. Inventory policies have to generate good performances (service level, cost) and be easy to implement in practice. A supply chain model based on simulation and multi-objective optimization is proposed to optimize control policies for multi-echelon supply chain with returned products. The method is tested on three inventory policies which correspond to different ways of making decision.     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

低碳环境下闭环供应链网络设计多目标鲁棒模糊优化问题

针对低碳环境下多级闭环供应链网络设计的战略定位和配置问题,综合考虑网络参数的模糊性以及多产品流,以供应链网络的总成本和总碳排放最小为目标,建立多目标鲁棒模糊优化模型.该模型将机会约束的最低置信水平作为决策变量,实现目标函数期望值、最优鲁棒性和可行鲁棒性之间的有效平衡.最后,设计一种基于必要性测度的交互式$\varepsilon$约束模糊求解方法,并通过算例验证所提模型和算法的有效性和可行性.