满足产品需求条件下的车间最优随机生产计划与控制

根据实际需要建立关联方程有延迟且以正好满足产品需求为约束条件的车间生产计划与控制的随机非线性规划模型，即一种求解动态优化问题的静态优化模型，为求解方便将其转化成线性规划模型。提出分别用卡马卡算法和基于卡马卡算法的关联预测法来求解柔性自动化车间（FAM）最优随机生产计划与控制问题，并编制了相应软件。通过算例研究，比较了上述2种方法和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FAW产品需求计划最优分解成由FAW中各柔性制造系统（FMS）执行的短期随机计划，尤其适合FMS之间工件传输需经出入库并有1个生产周期延迟的情况。     

柔性流水车间调度模型与混合引力算法

针对柔性流水车间调度问题,利用机器特定事件点来描述工件的机器选择,再以最小化最大完工为目标,考虑工艺约束和时间约束构建了柔性流水车间调度的混合整数线性规划模型,用GAMS/Cplex找到小规模问题的全局最优解。为快速求解大规模问题的近优解,提出了结合瓶颈启发式的引力搜索算法,利用瓶颈移动技术和John Son方法的解码机制,寻找最小化最大完工时间的最优调度方案。实验结果表明,所提出的模型及算法能高效地求解以最小化最大完工时间为目标的柔性流水车间调度问题。     

随机工时下柔性加工车间的鲁棒优化调度方法

为了提高柔性加工车间调度方案的可行性、保障生产过程的稳定性,提出一种鲁棒优化调度方法。引入两个不确定参数来描述随机工时的波动程度和约束条件的允许违背程度,提出随机变量服从概率分布时一般线性规划问题的鲁棒优化方法。采用该方法将含随机工时而难以求解的随机型柔性加工车间调度模型转化为确定型鲁棒对等模型。基于该模型,将随机工时融入适应度函数中,结合遗传进化的全局优化和邻域搜索的空间拓展能力研制出鲁棒调度算法,同步实现工件排序和机器分配的双重决策。案例测试表明,所提方法可以在较短计算时间内、以较小性能损失、将近95%的置信度获得当前最优解。     

基于改进粒子群算法的智能排产研究

车间的排产对于提高车间的生产效率至关重要，尤其对于柔性机加车间，智能排产可以极大提高加工的自动化与智能化水平。排产问题已经被证实是一个NP-hard问题，以最小化最大完工时间为目标，构建了柔性机加车间排产问题的数学模型，并提出了一种改进的粒子群算法对模型进行求解，在标准粒子群算法的基础上增加了变异和局部搜索的步骤，既减少了算法陷入局部最优的概率，又提高了算法的求解精度。并利用某航空发动机的柔性机加生产线验证了模型和算法的有效性。     

基于遗传退火算法的质检扰动应对方法

针对工序质检结果使原作业计划不能有效指导车间生产的现状,研究考虑工序质检的柔性作业车间动态调度问题.建立以最小化工件的最大完工时间和最小化排产方案变更差异为目标的混合整数规划模型,并提出一种基于局面评价的遗传退火算法.该算法将遗传算法的种群和变异概念引入模拟退火算法,利用模拟退火算法多次获得局部最优解以及大规模变异跳出局部最优的机制,获得最终全局近优解.在解码规则中直接考虑调度目标,提出基于局面评价的解码机制,避免产生劣质解,从而减小解空间.采用所提算法对文献中的案例进行扩充和求解,并与3种算法对比,验证了所提算法在解决该类问题上的有效性和优越性.     

变扰动强度下柔性装配作业车间调度性能分析

柔性装配作业车间是柔性作业车间的一类现实化扩展,其调度问题既要考虑复杂的加工路径柔性,还要考虑零件间的装配关联约束,以及由其带来的关联零件生产进度协同难题.首先给出了柔性装配作业车间调度问题的数学模型;然后考虑现实生产中普遍存在的随机扰动,采用了完全反应式与预测-反应式两类动态调度策略,并提出了相应的优先度规则算法和周...     

带时间窗约束的AGV集配货绿色路径规划问题研究

针对柔性制造车间背景下带时间窗约束的自动化导引运输车（automated guided vehicle,AGV）集配货绿色路径规划问题,以最小化AGV集配货过程能耗及时间偏离能耗作为组合优化目标,构建AGV绿色车辆路径规划模型,根据所研究问题特性,提出了一种改进变邻域搜索的混合遗传算法（GA-VNS）对其进行求解,并设计了5种邻域结构来提高算法寻优能力。通过对Solomon算例测试集进行求解,并与国际已知最优解进行数据对比,验证文章所提算法的可行性;进一步以某柔性制造车间某一生产时段的AGV物流运输任务作为实验案例,分别使用所设计的算法、GA和VNS算法对问题进行求解,数值实验结果表明了文章所提模型及算法的优化、适用性,为车间实现节能减排的发展目标提供一种可行方案。     

随机需求环境下的作业能力规划模型研究

为研究市场需求的随机性对企业能力扩张决策的影响,根据确定性能力规划模型的一般形式,构建了随机市场需求环境下的能力规划模型。该模型以随机环境下期望成本最小为目标函数,引人随机需求变量,建立了市场需求的随机约束。基于随机线性约束的二阶段方法,得到了随机能力规划问题的确定性等价形式,提出了一种将遗传算法和随机线性规划二阶段模型相结合的随机能力规划模型求解算法。该算法在事先得到能力规划变量的情况下,使用二阶段方法求解随机需求约束下的产品组合问题,并以产品组合问题的最优解作为适应度函数,通过遗传操作,搜索能力规划的整数变量,获得随机能力规划问题的最优解。     

基于多目标优化的空间直线度误差评定

根据最小区域定义及数学规划理论,建立了空间直线度评定的非线性规划模型,指出了这模型实质上是多目标优化的问题并将该优化问题转化成单目标优化问题。由于该非线性规划模型还是凸的、二次的,因此提出了用逐次二次规划的解法（SQP法）来实施。SQP法在评定过程中保留了模型中的非线性信息,对初始参数的要求低,且稳定、可靠、效率高。几个算例的结果均满足凸规划全局最优判别准则,这就有力的验证了上述结论。     

基于交替迭代遗传算法的成批车间生产计划与调度的集成优化

研究了一类零部件混批生产车间需要加工准备的多周期成批生产计划与调度的集成优化问题,建立了生产计划和调度集成优化的非线性混合整数规划模型.并提出了一种交替式混合遗传协调优化算法求解该集成优化问题.其主要思想是给定计划用一混合遗传算法求调度,反过来给定已求调度用另一混合遗传算法求取新计划,如此不断交替使用两个混合遗传算法实现计划与调度的同时优化.仿真结果证明了该方法的可行性和有效性.     

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。     

A production planning in highly automated manufacturing system considering multiple process plans with different energy requirements

This paper focuses on a production planning problem in a highly automated manufacturing system considering multiple process plans with different energy requirements. The system consists of several closely interconnected sub-systems such as the processing system, the material (part) handling system, the tool transport system and the auxiliary system responsible for a supply of cooling/lubricants and a waste disposal. We propose a methodology for an estimation of energy consumption and material flows that are incurred at a system level with respect to multiple process plans for a part type. In addition, this study focuses on a production planning problem with the objective to minimize the weighted sum of energy consumption, inventory holding cost and backorder cost on a FMS considering multiple process plans. The production planning model is developed as a linear programming model. The benefit coming from the adoption of suggested model has been addressed with reference to a real industrial use case study.     

An image acquisition system for real-time seam tracking

Process planning is a function that establishes the technological requirements necessary to convert a part from its raw material to the finished form. Generally, the result of process planning is delivered to the workshop to guide the manufacturing process in the form of process plan. However, a part always has multi alternative process plans for the processing means and techniques are not unique, therefore, optimization and selection of process plans is an important task of flexible process planning. In this paper, the flexibility of process planning and the AND/OR network adopted to represent the flexibility of process plans were described, and a mathematical model for the optimization of flexible process planning based on the AND/OR network was established. On this basis, a new heuristic method, called cross-entropy (CE) approach, was proposed to optimize flexible process planning. In order to facilitate the implementation of the CE-based approach, the new sample representation and probability distribution parameter were introduced; meanwhile, the new sample generation mechanism was presented and the updating expression of probability distribution parameter was deduced. Case studies, used for comparing this approach with genetic algorithm (GA) and genetic programming (GP)-based approach, were discussed to indicate the performance and adaptability of the proposed CE-based approach in terms of the solution quality and computational efficiency of the algorithm. The results show that the CE-based approach is effective for the optimization research of flexible process planning.     

DNC系统设备组动态重构技术的研究

研究了DNC环境下设备组重构技术与FMS环境下动态重构单元的区别与联系,在借鉴FMS的生产计划方法的基础上,提出了DNC系统设备组重构决策新理论,并研制了一套适合设备组重构的实用模型及算法。本模型及算法适用于DNC系统制造自动化车间集成化生产管理及调度。     

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。     

Production planning of a flexible manufacturing system in a material requirements planning environment

Early flexible manufacturing system (FMS) production planning models exhibited a variety of planning objectives; typically, these objectives were independent of the overall production environment. More recently, some researchers have proposed hierarchical production planning and scheduling models for FMS. In this article, we examine production planning of FMS in a material requirements planning (MRP) environment. We propose a hierarchical structure that integrates FMS production planning into a closed-loop MRP system. This structure gives rise to the FMS/MRP rough-cut capacity planning (FMRCP) problem, the FMS/MRP grouping and loading (FMGL) problem, and the FMS/MRP detailed scheduling problem.We examine the FMRCP and FMGL problems in detail and present mathematical programming models for each of these problems. In particular, the FMRCP problem is modeled as a generalized assignment problem (GAP), and a GAP-based heuristic procedure is defined for the problem. We define a two-phase heuristic for the FMGL problem and present computational experience with both heuristics. The FMRCP heuristic is shown to solve problems that exhibit a dependent-demand relation within the FMS and with FMS capacity utilization as high as 99 percent. The FMGL heuristic requires very little CPU time and obtains solutions to the test problems that are on average within 1.5 percent of a theoretical lower bound.This FMS/MRP production planning framework, together with the resulting models, constitutes an important step in the integration of FMS technology with MRP production planning. The hierarchical planning mechanism directly provides for system-level MRP planning priorities to induce appropriate production planning and control objectives on the FMS while simultaneously allowing for necessary feedback from the FMS. Moreover, by demonstrating the tractability of the FMRCP and FMGL problems, this research establishes the necessary groundwork upon which to explore systemwide issues pertaining to the coordination of the hierarchical structure.     

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.     

A case study in FMS production planning and dispatching

The speedy development and extensive application of computers have helped play a significant role in a new technological revolution. The importance of FMS flexibility in producing a variety of products and adapting rapidly to customer requirements makes FMSs attractive. Further, FMSs are most appropriate for largevariety and medium- to high-volume production environments. However, the module of the FMS production planning system is not perfect. This paper focuses on a new scheme for FMS production planning and dispatching under the realistic assumptions promoted by a particular flexible manufacturing factory. Some practical constraints such as fixture uniqueness, limited tool magazine capacity, and a given number of pallets are considered. The simulation results indicate that the scheme provides a good production plan, according to the short-term plans from the MIS Department. Some conclusions are drawn and a discussion is presented.