不确定环境下跨国供应链生产计划研究

介绍了跨国供应链生产计划研究与不确定规划论在供应链计划研究中的应用;提出了包含模糊需求与随机生产能力的跨国供应链计划模型,模型中将运输成本分配因子与转让价格作为决策变量,根据随机机会约束规划等价类理论,将模型转化为模糊机会约束规划模型,并利用遗传算法与模糊模拟技术相结合的混合算法设计了模型的求解方案;最后,通过数据仿真说明了模型的有效性与适用性。     

供应链中基于不同运输主体的批量模型比较研究

在单位产品运输成本对批量敏感时，为了使订货商的最佳订货批量与供应商的最佳生产批量趋同，建立了供应链中不同主体在产品运输时，供、需双方在分散决策和集中决策情形下的最佳批量模型。研究表明，首先，在供应商负责产品运输时，运输能力柔性越强，其最佳生产批量越小，从而与订货商要求的短周期、小批量的订货更趋近；其次，订货商负责产品运输时，其最佳订货批量与供应商的最佳生产批量更趋一致，从而供、需方可以在双赢的前提下使整个供应链的利益更优。     

基于供应商管理库存的批量订货契约模型及应用

研究基于供应商管理库存(VMI)的批量订货契约模型及其实例.通过供应商制定价格折扣的契约策略激励订货商批量订货,以便协调两级供应链中供需双方的利益关系.其中以供应商为策略的制定方,供应商给予订货商适当的价格折扣,订货商以最佳的订货批量做出响应,使得双方的成本结构都达到最优化.最后给出实例分析.     

供应商产出随机下基于风险共享的供应链协同模型

为降低供应链上游的供应风险,研究了供应商随机产出下两供应商-单制造商系统在溢出库存惩罚下的风险共享协同模型。分析了集中决策下供应链系统最优批量决策,以及溢出库存惩罚下供应商和制造商各自的最优批量决策;证明了在存在溢出库存惩罚下,供应商和供应商、供应商和制造商之间存在纳什均衡,且纳什均衡下供应链利润不大于供应链系统的最优利润值。为达到供应链系统的最优利润,提出了溢出库存惩罚下基于风险共享的供应链协同模型。通过案例分析说明,制造商通过调整满足一定关系的订货量、制造商风险共享系数和供应商风险共享系数,能使得独立决策下供应链达到集中决策下的供应链利润。     

可变提前期供应链库存建模优化及其算法研究

研究了由单个分销商和单个供应商组成的二级供应链库存系统,与以往文献中经常忽视或假设不可变提前期所不同的是:该研究将提前期视为决策变量,且是与订货批量相关的线性函数.建立了2种不同情形下可变提前期的库存模型:(1)主从对策的Stackelberg库存模型;(2)集中决策的供应链联合库存优化模型.在数值说明时采用遗传算法对模型进行优化求解,结果表明,集中决策模式下的优化效果优于Stackelberg主从对策模式.     

可变提前期供应链库存建模优化及其算法研究

研究了由单个分销商和单个供应商组成的二级供应链库存系统,与以往文献中经常忽视或假设不可变提前期所不同的是:该研究将提前期视为决策变量,且是与订货批量相关的线性函数.建立了2种不同情形下可变提前期的库存模型:(1)主从对策的Stackelberg库存模型;(2)集中决策的供应链联合库存优化模型.在数值说明时采用遗传算法对模型进行优化求解,结果表明,集中决策模式下的优化效果优于Stackelberg主从对策模式.     

三种由零售商主导的双渠道供应链结构在生产成本扰动下的鲁棒性比较

鉴于目前仍然缺乏在生产成本危机下不同供应链结构间最优订货与定价决策变动情况的比较研究,提出三种带有不同合作模式的双渠道供应链模型,基于零售商主导的Stackelberg博弈,引入价格折让协议机制,得出三种双渠道供应链在生产成本扰动出现前后两阶段的最优订货与定价决策。通过比较三种供应链结构在危机下的最优订货与定价决策的变动情况,给出了它们在危机发生下的鲁棒性排序。结果表明,供应链或部分供应链的垄断特征,导致了生产危机下原计划最优订货和定价决策的脆弱性。     

基于斯坦克尔伯格博弈的订货提前期决策研究

研究了供应链中供应商和零售商两级间的订货提前期决策问题.考虑现实中常见而研究较少的供应商决定交货时间和零售商决定订货量的订货过程,在提前期管理成本由上下游分担的条件下,建立了基于斯坦克尔伯格博弈的订货提前期模型,对比分析了供应商先动和零售商先动情形下的提前期和订货批量决策,说明了先行公布提前期对降低供应商成本和缩短供应链中的提前期是有利的.最后,通过算例对模型和结论进行了验证.     

天平计量仪器多渠道的价格策略

构建了基于价格竞争的多渠道价格策略研究模型,该模型可适用于不同供应链上的制造商进行简单的纵向与横向的价格博弈,以制造商为主分析不同决策下的销售价格策略.在整个市场范围内,采用Stackelberg价格博弈竞争策略分析供应链纵向价格策略,以均衡的原则完成供应链横向博弈的价格策略.通过实例分析表明:在面对市场上新的寡头制造商竞争时,拥有双渠道营销方式的制造商采取集中利润决策有利于提高其所在供应链的利润,采用分散利润决策能够在一定程度上削弱竞争对手的利润,激励零售商维持原有分销渠道,促进渠道内部利润的重新分配.     

制造成本扰动下链与链竞争的定价和生产决策

为解决突发事件引起的制造成本扰动对供应链企业产生的负面影响,研究了在制造成本扰动下两条竞争供应链的Stackelberg博弈模型,比较了有无制造成本扰动两种情形下两条竞争供应链的价格和订货策略,讨论了成本扰动对两条供应链的最优定价、订货策略和利润的影响,分析了不同成本扰动范围下两条供应链的最优调整策略。研究发现,只有当扰动成本超过一定阈值时,调整订货和定价策略才是占优策略,并得出了成本扰动的鲁棒范围。通过比较成本扰动发生后调整和不调整情况下供应链的利润,研究了进行策略调整的价值。通过数值分析验证了相关结论。     

具有模糊缺陷率和订货费用的库存管理研究

为解决目前库存研究中忽略产品缺陷率和订货费用不确定的问题，研究了同时具有模糊缺陷率和模糊订货费用的库存管理问题。首先利用模糊理论描述了库存管理中的不确定性，建立了缺陷率和订货费用模糊下的经济订货批量模型，以确定合理的订货批量，使得整个利润最大。然后利用符号距离法对模型目标进行了解模糊运算，给出并证明了参数为三角形模糊数时的最优订货策略。最后利用仿真实验验证了该策略，并研究分析了缺陷率和订货费用的模糊性对最优订货批量和最优利润的影响。     

基于Lagrange松弛分解的多产品生产--分销系统的联合决策

考虑全球制造环境下多产品生产分销网络系统中的联合物流决策问题,包括供应商指定的生产任务、生产批量、供应商和用户之间的年运输量和订货批量,提出了基于Lagrange松弛的两层分解启发式算法(LRD)来求解联合决策模型(JDM-M),其中第一层是供应商指定的生产任务、生产批量和运输流量的联合决策(APLS-TF),第二层是运输和订货批量的联合决策(TOQ-M)。仿真分析表明LRD对于大规模的集成决策问题是行之有效的方法。     

Dynamic sequencing of jobs on conveyor systems for minimizing changeovers

As many enterprises deal with orders of various products everyday and attend to maximize total profit or minimize total cost, a quick automatic dispatching tool is required for higher efficiency. In practical, the dispatching is not an easy task even for experienced experts; it is often determined depending on empirical knowledge, but usually would not be the optimal solution. It is due to the complex nature of the so-called “multi-plant order allocation problem”, which can be modeled as a typical mixed-integer programming problem. Considering companies with multiple plants, the large quantity of orders need to be distributed within a short time and the due dates of various orders shall be met such that the total costs can be minimized. In this research, an order allocation scheme for multi-plant to produce a variety of products is developed. The objective is to minimize total cost, including operation cost, setup cost, transportation cost, and the penalty cost of order delay under the constraint of capacity load. A mixed-integer programming model, named model of quasi-transportation problem, is proposed and solved. Several examples are presented and solved and compared by a general software LINDO and a genetic algorithm. Proposed genetic algorithm is demonstrated to be more efficient as the dimension of problem increases.     

Optimal selling price, marketing expenditure and lot size under general demand function

Joint pricing and lot sizing models enable manufacturers in making decision regarding production and pricing. These types of models have been proven to be very popular in the supply chain literature and are collectively known as the joint pricing and lot sizing models (JPLM). These models have been limited to a specialized demand function. In this paper, our proposed model relies on the fact that demand is a general function. We use a new approach to include a marketing effort factor in JPLM to increase profit for the manufacturer. In this approach, marketing effort is described as a separate factor that influences the demand. We obtain optimal solutions for selling price, marketing expenditure, and lot size such that the manufacture’s profit is maximized. The production, setup, and holding costs are also represented in the model. Numerical examples presented in this paper, which include sensitivity analysis of the key parameters, will be used to illustrate our model.     

Genetic algorithm and Hopfield neural network for a dynamic lot sizing problem

The dynamic lot sizing plays an important role when the demand of an item varies with time. This paper addresses a dynamic lot sizing problem (DLSP) with capacity constraint and discount price structure. Although the well-known dynamic programming (DP) of Wagner-Within is capable of providing an optimal solution for single stage lot sizing problems, it suffers from its high computational complexity. This limits the use of DP in practical problems that are generally larger in size. Two meta-heuristics, genetic algorithm (GA) and Hopfield neural network (HNN) are designed for DLSP to get best trade-off between solution quality and computational time. The DP algorithm is modeled to derive the optimal solution to the experimental problem. The optimality of GA and HNN are tested by comparing the percentage deviation of GA and HNN results against the optimal solution derived using DP.     

不完全信息下逆向供应链中制造商的最优合同

为解决不完全信息下制造商和回收商废旧产品回收的定价问题,基于单一制造商和单一回收商构成的逆向供应链系统,建立了委托-代理模型.该模型中,制造商在最大化自身利润的基础上建立了一种激励机制,该机制可使回收商真实地报出自己的运营成本的同时,其利润达到最优.通过对模型的分析、求解得到了不完全信息下制造商的最优合同,并通过案例进行了分析.     

A modified particle swarm optimization for disaster relief logistics under uncertain environment

Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.     

产品回收多级逆向物流网络优化设计模型

为降低逆向物流成本,改善客户产品回收便利性,优化逆向物流网络中产品回收的渠道选择决策,在原有正向物流网络基础上,提出了包含初始收集点、集中回收中心和回收处理工厂三种物流设施的多级逆向物流网络.以总逆向物流成本最小为目标,兼顾各客户群的产品回收便利性,建立了0-1混合整数规划模型,实现逆向物流网络的选址/分配问题.应用遗传算法求解该模型.模拟算例表明,该算法有效且可行,为企业进行逆向物流网络设计提供可参考的模型和求解算法.