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.     

基于多agent的供应链信息协调建模与仿真*

从核心制造企业的信息需求出发,采用基于多agent的供应链建模与仿真方法,研究在订单执行过程中,生产计划和采购计划等决策活动中的信息协调。利用面向agent的系统分析方法,提出了基于多agent的供应链信息协调框架模型,对其中的功能agent进行算法描述。结合Swarm平台构建了供应链仿真模型,对生产计划和采购计划等决策活动中的信息协调进行了模拟和分析,初步验证了模型的有效性。     

支持向量回归在不确定面向订单装配环境下交货期预测中的应用

针对面向订单装配(ATO)生产环境,如何根据订单信息、生产系统特性快速地估算出准确、可靠的交货期问题,在分析不确定性要素对交货期影响机制的基础上,构建了订单交货期预测模型。模型参数包括三个部分：订单上线时间、装配周期和异常拖期。订单上线时间基于零部件、生产能力的可用性,订单装配周期和异常拖期采用基于实际生产历史数据的支持向量回归(SVR)方法进行预测。案例研究表明该模型预测结果与实际交货期接近,可以用于指导订单交货期协商。     

基于移动agent技术的供应链订单流管理

面向订单的管理已成为供应链管理的重要内容,而先进的IT技术是使订单中的信息在供应链上各节点间流动与转化的有力支持。介绍了移动代理的优点及在供应链中的应用,并提出了基于移动代理技术的供应链订单流管理的模型和框架,对订单的处理过程和移动代理在订单流管理中的应用作了探讨。     

基于混合遗传算法的订单生产调度在机车车辆行业中的应用

机车车辆行业作为典型的面向订单的机械制造企业,优化的生产调度方法能提高订单的准时交货,缩短产品的生产周期,提高企业的市场竞争力。订单生产调度问题是典型的NP-hard问题。遗传算法(Genetic Algorithms)为求具有多个约束的复杂问题提供了有效的方法。但是遗传算法的局部搜索能力比较差,在解决订单生产调度问题中存在着明显的不足。本文引入了局部搜索能力很强的禁忌搜索算法,用遗传算法和禁忌搜索算法相结合的混合遗传算法来解决机车车辆行业中面向订单生产调度问题。     

面向装配的多agent排产模型研究

拖期罚金带来的成本问题使中小规模离散制造企业在竞争中面临巨大压力.为此,采用多agent技术设计面向装配的三层排产框架模型;利用多agent的自治和协同,结合经验规则,将复杂的大规模排产逐层分解,转化为可用算法优化的子问题,再自底向上归并更新,迭代寻优完成生产排产,并可依计划执行情况进行动态调度.以合作企业凹印机历史订单数据进行排产仿真,结果表明,通过对设备空闲时间分布的有效管理,此三层模型能够有效解决订单拖期问题,对中小企业的成本控制具有重要意义.     

ERP小辞典(18)

ＭＴＯ：这是面向订单生产（ＭＡＫＥ－ＴＯ－ＯＲＤＥＲ）的英文缩写,有时又可被称作按订单装配等,这是一种最终产品在收到客户订单后才确定的生产方式。为了促进供应链管理的功能,提高客户满意度,缩短向客户的交货期,对于那些具有较长提前期的产品必须在订单到达之前作出计划并生产。与此相对应的就是ＭＴＳ（面向库存生产）,其英文表述为：ＭＡＫＥ－ＴＯ－ＳＴＯＣＫ。这是一种客户订单到达之前就已经生产的制造模式,最终产品是从成品库中直接发运。（秋实）ERP小辞典(18)@秋实     

基于供应链的服装企业集成计划管理系统设计

本文对目前服装企业计划管理面临的问题进行了分析,提出了服装企业计划管理的新思路--基于供应链的集成计划管理,强调了面向订单的计划管理模式,并探讨了如何设计和实现集成计划管理系统.     

面向汽车零部件产业链云服务平台的采购订单整合技术

针对汽车零部件产业链供应商企业群的订单整合集成问题,提出了面向云服务平台订单整合的技术方案.在抽象出订单整合多级映射关系的基础上,建立了多层级订单映射树模型.采用弹性化的动态可配置接口,构建出面向汽车零部件产业链云服务平台的映射规则库,将复杂映射转换为直接映射.运用订单映射树的层级关系,采用映射整合算法,将来自不同整车厂的大批量异构订单整合为供应商企业群所需的零部件生产计划集.结果表明,运用多层级订单映射树模型能支撑面向汽车零部件产业链云服务平台订单整合的扩展应用.     

基于WLC的钢铁企业多工序、多路径订单投放方法

针对按订单生产(MTO)下的多工序、多路径的钢铁生产中订单投放问题建立数学模型,提出了度量订单交货提前/拖期程度、负荷均衡程度以及工序生产紧密衔接程度的3个指标.针对问题的模型及其特点,提出了基于负荷均衡的订单投放算法.首先基于负荷控制的思想为订单池中的订单指定生产路径;然后基于瓶颈工序优先均衡的原则调整工序负荷;最后对非瓶颈工序进行负荷均衡.基于企业实际生产背景的仿真实验结果验证了模型和算法的有效性.     

敏捷供应链插单调度优化的启发式算法

针对在敏捷供应链已有生产计划基础上进行的插单调度问题,以单工厂和多供应商组成的两级供应链系统为研究对象,采用时间槽表示可用调度时间,以最小化供应链总成本为目标,建立了调度优化问题的整数规划(IP)模型,设计了求解该模型的逐批选优启发式(OOSH)算法。通过算例将其与距离优先和作业周期优先决策算法比较,验证了启发式算法的有效性,显示了敏捷供应链协同调度可以有效提高供应链的市场竞争力。     

面向MES的钢铁烧结生产原料供应调度系统

针对某钢铁企业烧结厂烧结生产原料供应调度问题,提出了面向MES的烧结生产原料供应调度系统的整体功能、结构和设计原则,并采用了模型加启发式算法和人际交互相结合的烧结生产原料供应调度计划编制方法。设计并开发了烧结生产原料供应调度系统,通过现场数据离线仿真表明:该系统可快速准确地编制出符合现场情况、满足工艺要求、高效的烧结生产原料供应调度计划。     

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。     

Variable neighborhood search for the inventory routing and scheduling problem in a supply chain

The inventory, routing and scheduling decisions are three major driving factors for supply chain performance. Since they are related to one another in a supply chain, they should be determined simultaneously to improve the decision quality. In the past, the inventory policy, vehicle routing and vehicle scheduling are determined sequentially and separately. Hence, the total cost (inventory, routing and vehicle costs) would increase. In this paper, an integrated model for the inventory routing and scheduling problem (IRSP) is proposed. Since searching for the optimal solution for this model is a non-polynomial (NP) problem, a metaheuristic, variable neighborhood search (VNS), is proposed. The proposed method was compared with other existing methods. The experimental results indicate that the proposed method is better than other methods in terms of average cost per day.     

混合粒子群算法和多Agent系统的协同生产调度及其组合优化

为了从整体角度优化调度供应链网络的各个环节,研究了在供应链环境下核心制造商与制造商的协同生产调度方案,考虑在满足产品生产时间节点的最少加工时间。建立了多Agent的供应链环境下的协同生产调度模型,针对此模型设计了协同混合粒子群优化算法并进行求解。通过实例研究表明,供应链环境下制造商的协同优化对生产的计划与实行起到了关键的作用。     

Multi-plant production scheduling in SMEs

Traditionally, companies used to view themselves as separate entities and did not devote efforts to collaborate with other echelons of the extended enterprise. This even happened at multi-site companies owning different plants that belonged to the same supply chain. However, manufacturing facilities of the same supply chain should intensively share information and coordinate planning and scheduling tasks in order to get a globally optimized solution. This paper discusses some specific characteristics of the planning and scheduling problem in the extended enterprise including an analysis of a case study, and reviews the available state-of-the-art research studies in this field. Most studies suggest that integrated approaches can have a significant impact on the system performance, in terms of lower production costs, and less inventory levels. The paper provides some conclusions and suggestions of future works.     

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.     

柔性作业车间生产计划与调度集成优化求解策略

针对柔性作业车间(Flexible job-shop, FJS)生产计划(Production planning, PP)与调度紧密衔接的特点, 建立了生产计划与调度集成优化模型. 模型综合考虑了安全库存、需求损失及工件加工路线柔性等方面因素. 提出了一种基于拉格朗日松弛(Lagrangian relaxation, LR)的分解算法, 将原问题分解为计划子问题与调度子问题. 针对松弛的生产计划子问题, 提出一种新的费用结构, 以保证生产计划决策与实际情况相符, 并设计了一种变量固定—松弛策略与滚动时域组合算法进行求解. 对于调度子问题中的加工路线柔性问题, 提出了一种新的机器选择策略. 通过数值实验验证了模型与算法的有效性.     

半导体封装测试生产线排产研究

以某半导体封装测试（Semiconductor assembly and test manufacturing,ATM）企业为研究背景,对半导体封装测试的生产过程进行分析总结,提出一种新的“产能限定混线车间”（Capacity-limit flexible flow-shop,CLFFS）模型作为半导体封装测试生产线的排产模型.通过对半导体封装测试的特殊逻辑处理、排产方法以及排产规则等进行研究,提出采用逻辑约束和调度规则双层优化控制的启发式正序排产算法作为半导体封装测试的总体排产方法,同时针对批准备单处理生产阶段,提出一种新的预测开机控制优化调度方法.最后,结合CLFFS排产模型和所提出的策略方法,给出半导体封装测试排产的应用研究示例与比较,结果证明本文给定的总体排产方法在ATM中具有很好的可行性和业务逻辑嵌入的即便性,同时本文所提出的新的预测开机控制优化调度方法能够很好的缩短生产周期,提高生产效率.     

Dynamic supply chain scheduling

Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of dynamics, uncertainty, and adaptivity. Both supply chain schedule generation and execution control are represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. Hence, the problems and models of planning, scheduling, and adaptation can be consistently integrated on a unified mathematical axiomatic of modern control theory. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes. The application of optimal control for supply chain scheduling becomes possible by formulating the scheduling model as a linear non-stationary finite-dimensional controlled differential system with the convex area of admissible control and a reconfigurable structure. For this model class, theorems of optimal control existence can be used regarding supply chain scheduling. The essential structural property of this model are the linear right parts of differential equations. This allows applying methods of discrete optimization for optimal control calculation. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. The gained insights contribute to supply chain scheduling theory, providing advanced insights into dynamics of the whole supply chains (and not any dyadic relations in them) and transition from a partial “one-way” schedule optimization to the feedback loop-based dynamic and adaptive supply chain planning and scheduling.