模糊环境下考虑供应中断的再制造闭环供应链研究

为研究需求和回收品质量模糊不确定环境下供应中断对再制造闭环供应链的影响,建立了系统总成本模型。运用模糊机会约束规划方法解决模型中不确定性问题,并采用遗传算法和粒子群算法来寻找算例的最优解。结果表明：在不同需求量和回收品质量的水平下企业成本有不同的变化;随着供应中断概率的增加,平均总成本也随之增加。因此,再制造企业在面临需求量和回收品质量模糊不确定的环境时,应尽量避免供应中断,制定符合企业发展的运作计划。     

多产品供应商选择的模糊多目标整数规划模型

以成本、质量、交货为目标,考虑供应商供应能力、采购数量、供应数量的柔性、评价等级等约束,建立了多产品供应商选择的模糊多目标规划模型.采用降半梯形分布的隶属度函数将模糊多目标规划模型转化为单目标线性规划并求解,应用算例证明了模型的有效性和可行性.     

基于可信性理论的闭环供应链生产计划

针对生产、库存和拆卸能力有限情况下,考虑可回收旧产品数量、分销商需求量、生产成本、外包成本、单位旧产品拆卸时间、拆卸成本和单位产品生产时间为模糊不确定数,以供应链总成本最小化为目标,采用期望值规划和机会约束规划相结合的形式,建立不确定环境下供应链生产计划模型。基于可信性规划方法,将不确定模型转化为清晰确定形式。最后,设计算例,通过求解给出在供应链总成本最小下,企业各个计划期内的最优生产量、零部件采购量以及制造商和回收中心对物料的运输计划;并分析了可信性水平、再制造率和回收率变动对供应链总成本的影响,为企业制定生产计划提供参考依据。     

考虑回收率的逆向物流网络设计问题

针对报废产品的逆向物流网络设计问题,同时考虑优化物流网络运营成本和报废产品的回收率,构建一个多目标整数规划模型,来确定回收中心的数量、规模、地址和负责回收的区域以及是否对报废点给予回收价格补贴.通过算例分析,发现了随着回收率提高,各项成本的变化规律,并提出了回收率处于不同水平时使之进一步提高的策略.     

基于机会约束规划的弹性供应链多目标优化

研究供应商失效风险、零部件价格与制造商产能波动并存环境下,采用多源采购策略的弹性供应链成本与服务水平多目标优化问题,根据不确定理论构建了多目标机会约束规划模型,并采用标准化正规化约束法(Normalized normal constraint method)求解模型的Pareto前沿。通过数值算例求解验证了所提出模型及其求解算法的有效性,并对模型中的关键参数进行了敏感性分析,能够为企业管理人员开展供应商选择、零部件订单分配及顾客订单排序决策提供借鉴与参考。     

基于模糊规划的电子废弃物逆向物流网络设计

为了解决回收量不确定情况下的回收网络设计问题,构建了一个基于模糊规划的逆向物流网络模型.采用三角模糊数来描述回收量,利用模糊统计方法计算模糊参数的隶属度,把模糊机会约束清晰化,从而将模糊规划模型转化为确定性规划模型,采用传统求解算法和LING○9.0软件求解.以广西逆向物流网络设计为例,选择电视机、电冰箱、洗衣机、空调...     

考虑时间可靠度约束的4PL路径优化问题研究

运输时间的不确定性是影响第四方物流路径选择的重要因素。本文 将可靠性理论应用到路径优化问题中,把客户对时间可靠度的要求作为约束条件,以运输总费用最小为目标,基于多重图建立考虑时间可靠度约束的第四方物流路径优化问题的机会约束规划模型,设计双列变长编码机制的遗传算法求解模型。通过仿真实例验证模型和算法的有效性,结果表明针对客户对时间可靠度要求的不同,该算法可以快速并有效地帮助决策者做出最优的路径选择。     

基于低碳理念的多目标闭环供应链网络设计

针对低碳环境下的供应链网络设计,研究具有供应商、制造商、消费者、配送中心、回收中心及处理中心的多层级闭环供应链网络。考虑网络设施约束、碳排放、顾客服务等因素的影响,以网络运营成本、碳排放成本和顾客需求响应时间成本为目标建立多目标规划模型,解决网络内设施建设、选址、设施间流量分配等网络资源优化配置问题。利用Lingo11.0软件对模型求解,通过算例分析设施能力限制、碳税税率以及时间延迟成本对设施建设方式和网络成本的影响,为企业提供决策支持并在生产实践中发挥指导作用。     

交货量不确定下装备制造企业绿色供应商选择

基于绿色供应链理念,针对交货数量不确定条件下装备制造企业的绿色供应商选择与订货量分配问题,以采购成本、环境效益及产品拖期程度为准则,构建了考虑生产物料齐套要求和最小加工批量约束的随机整数规划模型;借助引入的二进制变量,将模糊的随机整数规划模型转化为清晰的多目标混合整数规划模型;进而结合缩小求解空间的思想,运用两阶段启发式算法对所建模型进行求解。算例验证结果表明,将随机整数规划模型应用于装备制造业绿色供应商选择具有较强的可行性和有效性。     

基于CVaR的两阶段第四方逆向物流网络设计模型

研究了具有风险规避特性的产品回收第四方逆向物流网络设计问题。首先,以条件风险值(CVaR)作为成本的风险度量指标,确定物流网络中的条件风险值成本;然后,以回收产品的数量和客户对再制造产品的需求量为随机变量,以总利润的期望值最大为目标建立两阶段随机规划模型;最后,采用引入自适应变异算子的遗传算法对该模型进行求解。通过具体算例说明了该模型能够有效地解决在风险因素影响下的第四方逆向物流的网络设计问题。     

测试系统成本控制与相对不确定度分配方法研究

根据动态规划原理，提出以控制成本为前提、利用计算机软件辅助计算对复杂测试系统的相对不确定度进行合理分配的方法。这种方法既能满足成本控制又能保证系统的相对不确定度指标的要求。     

供应链管理中生产和运输集成的排序问题

分析了供应链管理中多制造商、多客户的生产和运输集成起来的排序(调度)问题.建立了集成排序模型,在研究解的最优性条件基础上,用工件的总流程时间作为排序目标,解决使总费用达到最小的问題,给出相应的动态规划算法,并分析算法的复杂性.     

Partner selection model for design chain collaboration

Due to increasing global competition and shorter new product life cycles, brand owners collaborate more often with design chain partners to bring a new product to market. This paper proposes a design partner selection model to minimise the overall design chain cost, taking into account communication cost, time-to-market, and quality. The model can be formulated as a stagecoach problem. Next, a dynamic programming algorithm is developed for this model to select the optimal partners through the design chain. Finally, a numerical example and a sensitivity analysis are presented. The results also reveal that design quality and manufacturing quality positively influence the total cost, and when brand owners need to speed up time-to-market to keep up with a competitor, the design quality will become less important.     

Practical solution approaches to solve a hierarchical stochastic production planning problem in a flexible automated workshop in China

This paper deals with a Hierarchical Stochastic Production Planning (HSPP) problem of Flexible Automated Workshops (FAWs), each with a number of Flexible Manufacturing Systems (FMSs). The problem includes not only the standard (demand, capacity and material supply) uncertainties but also uncertainties in processing times, necessity for rework and scrap. In contrast to most work that only considers either single period or infinite horizons, we also considers multiple time periods and multiple products. One objective of this paper is to determine a cost minimizing production plan for each FMS taking into consideration work-inprocess inventory, work centers overload and underload, cumulative over- and under-production of finished products over a finite time horizon. The HSPP problem is formulated as a stochastic nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. To facilitate the solution procedure, the model is first transformed into a deterministic nonlinear programming model and then into a linear programming model. For medium- or small-scale problems, Karmarkar's algorithm is applied to obtain the solution. For large-scale problem, an interaction/prediction algorithm is used. The effectiveness of these approaches is benchmarked against the linear programming method in Matlab 5.0 in various HSPP settings.     

Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.     

Optimal selection of application loading on cloud services

There is a need for identifying computing power hours and storage utilisation along with total cost optimisation. The present paper focuses on optimal selection of application loading process on the cloud services considering relevant factors. Using this model, small companies that plan to develop applications and use cloud services may determine cost and optimal selection of service by taking into account its own as well as provider’s perspectives into consideration. The paper consists of four stages. First stage deals with the estimation of required computing power hours in the planned duration. Second stage relates to the calculation of storage capacity. Third stage corresponds to the formation of multi objective goal programme to prioritise computing power hours and storage utilisation requirements of applications and optimise total cost of usage. Finally, fourth stage deals with the mixed integer non-linear programming to minimise total cost considering other variable factors. For small application developers who cannot afford in-house IT infrastructure, we find an optimal framework for allocating number of applications on cloud services such as Infrastructure as a Service and Platform as a Service. For ease in planning, the user company can quickly decide corresponding number of applications at appropriate services, and at the same time can reduce overall usage cost. With the help of proposed method, the service provider may keep a suitable inventory of cores to provide backup computing power and storage capacity. This adds value to developers also, as company can plan for their operations corresponding to the business growth.     

Repeat inspection planning using dynamic programming

In this paper we propose a dynamic programming approach to the problem of determination of the inspection sequence of multi-characteristic critical components, and the number of repeat inspection for each characteristic. The model presented here considers the case of several classification of a product by an inspector. An inspector could classify a product as non-defective, to be reworked, or to be scrapped, with respect to a certain characteristic. The model accounts as well for possible misclassification by the inspector. The dynamic programming algorithm searches for a solution that minimizes the total cost of inspection per accepted component. The total cost includes the cost of false rejection of good items, the cost due to false acceptance of an item which is either reworkable or to be scrapped, the cost of inspection, and the cost of rework.     

基于模拟试验法的离散公差稳健设计

 在分析计算机辅助公差设计技术的研究现状的基础上，提出了基于模拟试验法的离散公差稳健设计方法．在该方法中将加工成本与质量损失作为两个独立的目标函数，从而建立离散公差优化设计模型，采用试验设计法和CP方法相结合的技术实现公差的稳健设计，最后用实例证明该方法的可行性．     

多核集群任务分配问题的0-1整数规划求解模型

研究了典型多核集群任务分配中的节点内通讯特性。基于0-1整数非线性规划模型和线性松弛技术,给出了一种0-1整数线性规划任务分配问题求解优化模型。由于节点内的通讯量与通讯延迟较大,以最小化计算代价和节点间通讯代价为研究目标的传统求解模型具有严重的局限性,而该求解模型考虑了节点内通讯代价,并采用了线性规划松弛技术,其目标是最小化计算代价、节点间通讯代价和节点内通讯代价。计算结果验证了提出的模型的有效性。     

Optimal tolerance re-allocation for the generative process sequence

In generative process planning, the sequence of machining processes is decided according to the specifications of parts, such as tolerance values. However, in order to obtain the minimal manufacturing cost, the machining process sequence needs to be considered before tolerances are assigned. It is therefore difficult to assign optimal tolerances so that a minimum manufacturing cost is achieved. This paper presents an iterative approach for reallocation of tolerance within the given functional constraints to minimize the manufacturing cost. With the given values of tolerance and corresponding process sequences, which are derived from a handbook or a designer's experience as initial inputs, each iteration of tolerance re-allocation tries to improve the total cost by shifting tolerances along the different processes in the current sequence. The re-allocation problem is formulated as a mixed integer nonlinear programming problem. The Lagrange Multiplier method has been used to solve nonlinear programming, and an exhaustive search method has been adopted to guarantee the global optimum in solving the zero-one algorithm. A prototype system has been implemented in an object-oriented programming environment and a case study is presented to demonstrate the capability of the system.