柔性自动化车间的最优随机生产计划

研究了由多个柔性制造系统组成的柔性自动化车间的最优随机生产计划问题,首先根据实际需要建立车间生产计划的随机非线性规划模型,为求解方便,将其近似转化成确定非线性规划模型,并通过引进约束进一步转化成线性规划模型。由于这种模型规模较大,很难在微机上用单纯形法在可接受的时间内获得其最优解。为此,分别用卡马卡算法和基于卡马卡算法的关联预测法,求解柔性自动化车间最优生产计划问题,并编制了相应软件。最后通过算例研究,比较了卡马卡算法、基于卡马卡算法的关联预测法和Matlab中的线性规划法,结果表明,所提方法非常适合将不确定性环境中的随机产品需求计划,最优分解成由柔性自动化车间中各柔性制造系统执行的短期随机计划。     

离散随机需求下共用装配线生产计划优化

依据工程机械产品的离散随机需求和生产线能力受限的特点,充分考虑生产不同产品的切换费用、生产启动费用、产品持有费用、需求拖欠费用等,研究单条装配线上的多产品生产计划优化问题。通过构建生产计划的优化模型导出求解模型的递推公式,设计了求解模型的动态规划算法以及启发式算法,最后进行了算例分析。该优化方法可用于工程机械装备等生产企业制定单条装配线上的多产品生产计划。     

钢铁联合企业中期生产计划建模及算法

针对钢铁联合企业品种结构多样,工艺流程复杂,多阶段、多工厂联合生产的特点,建立了以客户需求满足度、企业效益等为目标的中长期生产计划优化模型。该模型为大规模、非线性、网络优化模型,主要考虑了生产能力、库存限制、工艺制程等约束条件,可以保证企业生产物流平衡和能力与市场平衡。提出了约束遗传算法嵌套线性规划方法的分层混合求解算法,外层以约束遗传算法中的染色体作为控制因子转换模型分解问题规模,内层采用线性规划快速求解,通过内外层嵌套循环,实现了非线性模型与线性模型的转换求解。通过算例证明了所提算法的有效性。     

集成化车间作业计划动态关键路径编制算法

针对单件小批生产加工装配式制造的特点,结合车间作业计划与物料需求计划的集成化管控需求,提出了基于生产过程网络图的动态关键路径作业计划编制算法.算法按照零部件加工装配过程中的工艺顺序和装配关系,将计划期内订单的形成过程抽象化为生产过程网络图,并将物料需求计划映射为网络图中的节点,进行生产能力控制和加工装配约束.以生产过程网络图为作业计划编制的对象模型,以订单的交货期为依据,采用倒排的方式,通过动态寻找网络图中的关键路径来确定车间作业计划顺序和资源分配.通过某密封产品生产企业机加车间生产计划编制的实例,证明了该算法能快速有效地在有限生产能力条件下编制高效可行的车间作业计划,保证作业计划与物料需求计划的一致性.     

不确定可重入定点装配车间集成生产计划与调度

为满足工厂—车间一体化管理需求,研究了不确定可重入定点装配车间生产计划与调度集成优化问题。在分析车间装配特点的基础上,利用期望值描述不确定可重入情况,建立了双层生产计划与调度集成优化随机期望值模型,上层为能力约束的生产计划模型,下层为不确定可重入定点装配车间调度模型。提出了一种具有双层结构的交替迭代式改进遗传算法,上层用精英遗传算法求解生产计划,代入下层后采用基于随机模拟技术的遗传模拟退火算法求解生产调度,然后将调度结果返回上层重新求解新计划,如此不断交替迭代以实现计划与调度的同时优化。通过算例仿真验证了模型及算法的有效性。为制定不确定可重入定点装配车间生产计划与调度提供了一种合理可行的方法。     

一种多周期随机需求生产/库存模型

为使存贮、生产和缺货等费用的总和最小，建立了一种多周期随机需求生产／库存模型，该模型采用（S，Q）策略对生产和库存进行控制，即当成品库存降至8时准备生产，生产量为Q。通过对该模型费用函数特性的分析，设计了一种迭代学习算法，根据该算法可以得出系统的最优生产准备点及最优生产量。将所提出的迭代学习算法与遗传算法进行了比较，结果显示，两者所得到的控制量是吻合的，且迭代学习算法的求解速度更快，从而证实所建立的模型和提出的迭代学习算法是正确有效的。     

多阶段产品需求的串并联生产系统最优生产计划

研究了多条生产线组成的串并联生产系统面临多阶段产品供不应求时生产效益最大的最优生产计划问题。首先对制造企业实际情况进行现场调研,构造了串并联系统的上下游成品关联结构,建立了多阶段产品需求下无优先级的上下游生产线最大生产效益的非线性规划模型,并应用遗传算法进行求解,最后通过数值实验证明了模型的有效性。相比于其他设定优先级别生产的方法,本方法普遍适用于各种供不应求情况,能为企业带来最大生产效益,应用范围广泛。     

JIT柔性混合流水车间生产调度问题研究

针对混合柔性流水车间多种工艺路线的生产调度问题,分析了生产工艺计划与车问调度系统的集成原理,建立目标模型,通过将简单遗传算法加以改进,在建立集成模型的基础上,对算法进行研究,把进化后的遗传算法（SGA）和改进的模拟退火算法（SA）有机结合,使算法优化机制融合和优化结构互补,形成较为高效的混合优化算法,并对问题进行求解。最后给出了一个具体算例,验证算法的有效性和先进性。     

面向节能的混合流水车间调度问题建模与优化

针对面向节能的不相关并行机混合流水车间调度问题的特点,分析了混合流水车间的能耗组成,基于Wagner建模思想提出一种以最小化能耗为目标的混合整数线性规划模型,并提出一种改进的候鸟优化算法求解该问题。在候鸟优化算法中,采用随机策略生成初始种群,提出4种解码方法;基于关键路径的思想设计了两种移动策略,目的分别是调整空闲时间段使其可以实行关机重启策略,以及尽量延迟机床开机;该算法中领飞鸟和跟飞鸟通过最优交换操作和最优插入操作进行进化。通过41组实例对加入移动和关机重启策略的候鸟优化算法,以及没有加入移动和关机重启策略的候鸟优化算法进行测试,证明了所提移动和关机重启策略能够大幅度减少车间总能耗。同时,通过41组实例测试验证了所提模型和算法的有效性和可行性。     

GA-COA求解柔性作业车间多资源调度问题

针对传统柔性作业车间调度在仓储、运输方面考虑的不足，将工件的存储位置以及工件在仓库、机床之间的运输考虑到传统柔性作业车间调度问题（FJSP）中。提出一种考虑仓储、运输及加工的柔性作业车间多资源调度问题（MRFJSP），以最小完工时间为目标函数进行数学建模。考虑到遗传算法（GA）在求解车间调度问题中收敛速度慢、易陷入局部最优的问题，将郊狼优化算法（COA）的组内郊狼成长、生与死进行改进并与GA结合，提出一种带随机动态分组的遗传-郊狼混合算法。最后，通过算例验证了模型的正确性，并将混合算法与原算法进行对比，验证其优越性。     

Hierarchical Production Planning in Flexible Automated Workshops with Delay Interaction

This paper addresses the hierarchical production planning (HPP) problem for flexible automated workshops (FAWs) with delay interaction, each with a number of flexible manufacturing systems (FMSs). The delay interaction aspect arises from taking into consideration the transfer of parts between FMSs. Any job which requires processing on more than one FMS cannot be transferred directly from one FMS to the next. Instead a semi-finished-product completed in one period must be put into shop storage until the next period at which it can be transferred to the next FMS for further processing. The objective is to decompose medium-term plans (assigned to an FAW by ERP/MRP II) into short-term plans (to be executed by FMSs in the FAW) so as to obtain the lowest production cost. The HPP problem is formulated in this paper by a nonlinear programming model whose constraints are linear but whose objective function is piecewise linear. For the convenience of solving the nonlinear programming model, it is transformed into a linear programming model. Because the model for a general workshop is too large to be solved by the simplex method on a personal computer within acceptable time, Karmarkar’s algorithm and an interaction/prediction algorithm, respectively, are used to solve the model, the former for medium- or small-scale problems and the latter for large-scale problems. With the implementations of these algorithms and with many HPP examples, Karmarkar’s algorithm, the interaction/prediction algorithm and the linear programming method in Matlab 5.0 are compared, showing that the proposed approaches are very effective.     

敏捷制造环境中车间的随机生产计划方法

研究了敏感制造环境下柔性自动化车间的髹机生产计划方法。     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

Manufacturing-to-Sale Planning Model for Fuel Oil Production

The fuel oil refinery production industry in Taiwan has entered a completely competitive free open market. In such an environ-ment there exist a variety of uncertain factors, and a traditional production planning model will not be able to deal with the new situation. This study develops a responsive and flexible manufacturing-to-sale planning system to deal with uncertain manufacturing factors. The major objective of this study is to model the problem of the uncertain nature faced by the Chinese Petroleum Corporation (CPC) and to establish a manufacturing-to-sale planning model for solving the problem. A linear programming technique is suggested for developing the optimal strategy for use in production plans. Fuzzy theory is adopted for dealing with demand/cost uncertainties. A possibilistic linear programming model is thus formulated in this study. The possible uncertain fluctuations on demand/cost are included in the model. Therefore, the strategy for creating maximum profit for the company can be obtained via the proposed modelling procedures.     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

分布式多工厂单件制造企业提前/拖期生产计划研究

描述了分布式多工厂单件制造企业交货期下的提前／拖期生产计划,以实现最小化提前／拖期惩罚费用、生产成本以及产品运输费用之和为目标,建立了０－１规划数学模型,利用分枝定界算法得到了生产计划调度方案。计算结果证明了模型的有效性。     

面向半导体生产线生产计划的混合智能算法研究

针对传统半导体企业生产计划优化方法不能体现市场和生产环境中不确定性的不足,将模糊机会约束规划应用于半导体生产计划优化模型的建立,通过结合了模糊模拟和遗传算法的混合智能算法对半导体企业生产计划进行求解并通过实例及验证来说明该方法的可行性和有效性。     

Loading problems with tool management in flexible manufacturing systems: A few integer programming models

In automated production systems like flexible manufacturing systems (FMSs), an important issue is to find an adequate workload for each machine for each time period. Many integer linear programming (ILP) models have been proposed to solve the FMS loading problems, but not all of them take tools into account. Those that do not consider tooling are quite unrealistic, especially when setup times are important with respect to processing times. When tool loading has to be handled by the model, the load assignment may have to be changed completely.In this article we consider FMSs with a tool management of the following type: the system works in time periods whose durations are fixed or not; and tools are loaded on the machines at the beginning of each time period and stay there for the whole time period. Tool changes may occur only at the end of each time period when the system is stopped.We present some integer programming models for handling these situations with several types of objectives. Emphasis is laid on the ILP formulations. Computational complexities are discussed.     

A Dynamic Tool Requirement Planning Model for Flexible Manufacturing Systems

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.