加工与装配车间集成调度的多目标优化模型

为解决一类具有多品种混流加工作业车间和流水装配车间的两阶段集成调度优化问题,以加工线最大完工时间和产品总生产完工时间最小为目标,并考虑通过对零部件加工提前完工和装配线等待施加惩罚系数,以保证缓冲区在制品库存和装配过程均匀连续生产,建立加工与装配车间集成调度的多目标优化模型,充分利用加工和装配工序之间存在的并行性,合理确定零部件加工顺序和装配排序,以缩短产品生产周期,降低生产成本,提高生产设备利用率;同时针对所建立的模型,设计遗传算法进行求解,采用零件加工和产品装配的两段实数编码,以稳态复制对群体进行选择,对交叉和变异算子进行设计,以保证新个体满足工序先后约束的可行性,避免了非可行解的混入影响优化结果;最后通过实例验证所建调度模型的可行性和算法的有效性。     

汽车装配线生产计划与调度的集成优化方法

为提高汽车装配线的生产效率,优化资源配置,研究了汽车装配线生产计划和调度的集成优化问题,给出了该问题的混合整数规划模型.利用分枝定界算法和单纯型法求得问题的粗生产计划.通过将模拟退火算法和快速调度仿真相结合,探讨了一种新的启发式算法.然后基于已求得的粗生产计划,针对三种不同寻优组合论述了该算法的实现.将该算法应用于实际算例,仿真结果表明该算法对求解此类问题有着很好的效果.     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型。基于免疫算法以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的。在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于．NET平台和B／S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标。     

汽车装配车间生产计划与调度的同时优化方法

文中提出三种新方法来解决汽车装配车间生产计划与调度的同时优化问题.首先将汽车装配线简化为一个Flow shop问题,并建立其混合整数规划模型,以求得使各装配工位的准备成本和空闲时间尽可能少并尽可能满足产品需求的粗生产计划.然后在粗生产计划的基础上考虑装配线的细节,用Tabu搜索法与快速调度仿真相结合的三种不同启发式算法使生产计划与调度同时得到优化,并给出了三种算法的复杂性.大量算例的比较研究表明了这些算法的有效性和适用性.     

汽车同步装配线生产计划与调度集成优化汽车同步装配线生产计划与调度集成优化

为克服传统的"自顶向下"方式下生产计划与调度不协调的缺陷,针对汽车同步装配线,构造了生产计划与调度集成优化混合整数规划模型,并采用拉格朗日松弛法将其分解为批量计划及调度等子问题.将调度子问题转化为与时间相关的旅行商问题,并采用dynasearch算法求解.对于拉格朗日对偶问题,采用均衡方向策略法求解.仿真实验结果验证了模型及算法的有效性.     

汽车装配车间生产计划逆向递推与调度

本根据实际汽车装配企业的需要,提出一种依据各车间的上下游关系从总车间成车下线计划开始向递推出所有上游车间生产计划的方法和基于下游需求拉动与上游关键装配件供应约束的车间生产调度方法,并结合数据库技术给出了他们的实现方法。目前,该方法已经应用于实际,并取得了良好的效果。     

基于HGA的冲压车间生产计划与调度的集成优化

根据车身厂冲压车间和生产库房的实际情况，研究了冲压件成批生产的Job—shop车间生产计划和凋度的集成优化问题，给出该问题的非线性混合整数规划模型，并采用混合遗传算法进行求解。遗传算法中，给出一种新的启发式规则来改善初始解集，通过与递阶分解方法进行比较，得出该算法对求解该类问题有着很好的效果。     

基于HGA的冲压车间生产计划与调度的集成优化

根据车身厂冲压车间和生产库房的实际情况,研究了冲压件成批生产的Job-shop车间生产计划和调度的集成优化问题,给出该问题的非线性混合整数规划模型,并采用混合遗传算法进行求解。遗传算法中,给出一种新的启发式规则来改善初始解集,通过与递阶分解方法进行比较,得出该算法对求解该类问题有着很好的效果。     

一类Job- shop 车间生产计划和调度的集成优化

讨论一类Job—shop车间的生产计划和调度的集成优化问题，给出了该问题的非线性混合整数规划模型，并采用混合遗传算法进行求解。该模型利用调度约束来细化生产计划，以保证得到可行的调度解。在混合算法中，利用启发式规则来改善初始解集，并采用分段编码策略将计划和调度解映射为染色体。算例研究表明，该算法对求解该类问题具有很好的效果。     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型.基于免疫算法.以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的.在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于.NET平台和B/S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标.     

Robust planning in optimization for production system subject to random machine breakdown and failure in rework

This study is concerned with robust planning in optimization, specifically in determining the optimal run time for production system that is subject to random breakdowns under abort/resume (AR) control policy and failure in rework. In most real-life production processes, generation of defective items and breakdowns of manufacturing equipment are inevitable. In this study, random defective rate is assumed and all manufactured items are screened. The perfect quality, reworkable and scrap items are identified and separated; failure-in-rework is assumed. The system is also subject to random machine breakdown; and when it occurs, the AR policy is adopted. Under such policy, the production of the interrupted lot will be immediately resumed when the machine is restored. Mathematical modeling and derivation of the production-inventory cost functions for both systems with/without breakdowns are presented. The renewal reward theorem is used to cope with the variable cycle length when integrating cost functions. The long-run average cost per unit time is obtained. Theorems on convexity and on bounds of production run time are proposed and proved. A recursive searching algorithm is developed for locating the optimal run time that minimizes the expected production-inventory costs. A numerical example with sensitivity analysis is provided to give insight into the optimal operational control of such an unreliable system.     

Integrated process planning and scheduling by an agent-based ant colony optimization

This paper presents an ant colony optimization (ACO) algorithm in an agent-based system to integrate process planning and shopfloor scheduling (IPPS). The search-based algorithm which aims to obtain optimal solutions by an autocatalytic process is incorporated into an established multi-agent system (MAS) platform, with advantages of flexible system architectures and responsive fault tolerance. Artificial ants are implemented as software agents. A graph-based solution method is proposed with the objective of minimizing makespan. Simulation studies have been established to evaluate the performance of the ant approach. The experimental results indicate that the ACO algorithm can effectively solve the IPPS problems and the agent-based implementation can provide a distributive computation of the algorithm.     

RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts

A walking-worker fixed-position flexible assembly line is a shop-floor where products are placed at work centres, the workers move from one work centre to another, and tools and components are brought to the work centre for assembly according to the process and production plan. Such assembly shop-floor configuration is not only suitable for producing large, bulky, heavy or fragile products, but also offers necessary flexibility and competitive operational efficiency for products of modest variety and production volumes. However, the shop-floor with a fixed-position assembly islands typically suffers from limited spaces at work centres and high dynamics of material and manpower flows in addition to common shop-floor problems. This paper presents an affordable solution to these problems by using wireless manufacturing (WM)–an emerging advanced manufacturing technology (AMT). WM relies substantially on wireless devices such as radio frequency identification(RFID) auto ID sensors and wireless information networks for the collection and synchronization of the real-time field data from manufacturing workshops. A simplified example is used to illustrate how to deploy WM technology for implementing the concept of Just-In-Time (JIT) manufacturing to reduce the shop-floor work-in-progress (WIP) inventories and smoothening their flows through real-time information visibility and traceability.     

基于柔性job shop的集成化工艺规划与生产调度

为有效利用车间资源管理系统,在研究基于柔性jobshop的工艺规划与生产调度集成问题的基础上,提出基于工艺规划的多agent生产调度系统（Flexible process planning based Multi-Agent production Scheduling System,FMASS）．该系统综合考虑零件的工艺规划柔性和车间生产柔性,采用混合建模的方法建立4类agent及其行动规则,通过各类agent相互之间的协商与竞争得到零件的工艺规划和工序,从而实现工艺规划与车间调度系统的集成．对工艺规划与车间调度的集成算法进行性能测试,结果表明该系统具有一定的预见性和全局优化能力,且柔性和对动态变化的适应性较好．     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.     

一种推拉结合的生产计划与调度模型*

通过分析混合流程企业生产计划管理的实际需求特点,提出生产工艺流程图和一种推拉结合的生产计划与调度模型.模型以订单、库存和预投为综合需求起点,以拉式补货的方式制订有限产能的主生产计划,依据工艺流程图分解生成可行的流程作业计划和离散作业计划,以及车间级物料需求计划等.在生产执行中则结合工序流转卡等方式进行推式生产,以保证生...     

Modeling and Pareto optimization of multi-objective order scheduling problems in production planning

This paper addresses a multi-objective order scheduling problem in production planning under a complicated production environment with the consideration of multiple plants, multiple production departments and multiple production processes. A Pareto optimization model, combining a NSGA-II-based optimization process with an effective production process simulator, is developed to handle this problem. In the NSGA-II-based optimization process, a novel chromosome representation and modified genetic operators are presented while a heuristic pruning and final selection decision-making process is developed to select the final order scheduling solution from a set of Pareto optimal solutions. The production process simulator is developed to simulate the production process in the complicated production environment. Experiments based on industrial data are conducted to validate the proposed optimization model. Results show that the proposed model can effectively solve the order scheduling problem by generating Pareto optimal solutions which are superior to industrial solutions.