基于仿真的汽车发动机再制造生产线缓冲区容量优化

以汽车发动机核心部件为对象,以在制品数量(WIP)为主要评价指标,重点研究回收件到达率、可再制造率、加工时间等不确定因素对缓冲区容量的影响.基于离散事件仿真技术建立系统动态运行模型,采用正交试验设计方法完成了仿真试验的方案设计.通过仿真再制造生产线动态平衡及缓冲区容量的内在变化规律,获得生产线优化运行所需的最小缓冲区容量.案例研究表明,所提出的方法能有效确定再制造系统缓冲区的最佳容量.     

随机批量运输的柔性作业车间AGV配置优化

针对柔性作业车间中物料搬运系统的AGV数量配置问题,以最小化AGV购置成本为目标,建立具有系统产出率和生产周期双重约束的优化模型。由于该优化问题是一个随机非线性的整数规划问题,且约束条件无法用决策变量的封闭形式表示,为此,提出一种基于仿真的粒子群优化算法求解该问题。针对具有随机批量运输特征的柔性作业车间,基于离散事件仿真平台构建系统的性能估算模型,提出一种嵌入仿真模型的粒子群优化算法求解AGV数量配置的优化方案。通过仿真算例实验以及不同优化方法对比,对比结果显示,该方法较其他算法在优化结果的优越性和稳定性上分别平均提高了8%和8.9%。分析实际应用案例确定了优化的配置方案,结果验证了所提方法的有效性,具有实际应用价值。     

考虑系统波动因素的DBR瓶颈缓冲区容量设置

DBR方法中的缓冲区容量设置直接影响产品的生产提前期和系统的有效产出,最终影响产品的市场竞争力。针对静态设置的缓冲区容量不能根据生产系统波动程度进行调整的问题,提出一种动态设置缓冲区容量的方法。首先分析影响缓冲区容量设置的3个因素,采用G/G/1排队模型衡量瓶颈前工序生产自然波动、瓶颈前工序数量和瓶颈工序随机波动对缓冲区容量设置的影响,并考虑避免机器故障和机器维修导致的瓶颈“饥饿”,求解机器故障因素和机器维修因素所需要的最小缓冲区容量。在此基础上,提出一种基于系统仿真的动态调节缓冲区容量的方法。最后通过案例分析,验证了所提方法的有效性和实用性。     

考虑有限物流运输能力的智能车间AGV调度算法研究

以大批量生产模式智能车间为背景,针对单环布局下多自动导航小车 （AGV） 的调度问题进行研究。首先,对典型智能车间特点进行了分析,利用Plant Simulation仿真平台建立了基于实际生产场景的仿真模型;其次,提出了基于软时间窗的AGV调度规则和基于备选调度规则集的6个运行机制;最后,通过设计仿真实验对上述7个调度规则进行了分析和比较。实验结果表明,基于软时间窗AGV调度方法总体上表现更好。这为企业配置和调度AGV小车提供了决策支持。     

基于动态时间窗的泊车系统路径规划研究

针对智能立体停车库中自动导引运输车(automated guided vehicle,AGV)存取车路径规划问题,采用分时利用策略,将Dijkstra算法和时间窗法有效结合,提出了一种基于动态时间窗的泊车系统路径规划方法。首先,通过引入优先级策略为接收任务的AGV设定优先级;其次,采用Dijkstra算法,按照任务优先级高低次序,依次为接受任务的AGV规划出最短可行路径;最后,在已知AGV可行路径基础上,通过对可行路径各路段的时间窗进行初始化、实时更新以及实时排布处理,实现多AGV的无冲突路径规划。为验证所提方法的可行性和有效性,以4台AGV同时工作的智能立体停车库为实例进行仿真测试。结果显示:所提出的路径规划方法不仅有效解决了目前多AGV路径规划柔性差、易出现死锁、碰撞冲突等问题,而且可在有效解决路径冲突的前提下,为接受任务的AGV规划出一条时间最短的优化路径。所提方法具有较好的鲁棒性和柔性,有效提高了智能立体停车系统整体运行效率,降低了存取车等待时间。     

随机性因素对机器人单元性能的影响分析

为了分析机器人单元性能参数的随机性对系统性能的影响,针对一个典型的机器人制造单元,利用e MPlant仿真平台建立仿真模型,并根据不同情形设计和实施了一系列实验。仿真结果显示工件到达时间等参数的随机性降低了系统性能。通过改变输入缓冲区容量等系统配置方法,可提高随机环境下系统的性能。说明可通过调整配置参数提高具有不确定环节的机器人制造单元的系统性能指标。     

快速重组制造系统的构建原理及其应用

快速重组制造系统是一种适应21世纪全球化市场竞争的新型制造系统。提出了基于组态式柔性制造单元组成的陈列式布局的快速重组制造系统的创新结构体系,建立了以生产效益为该系统方案设计和运行决策目标的随机模型,应用排队网络模型和静态马尔科夫工件运送方式得出系统重组时缓冲区容量的优化配置和最优工件运送策略的算法,研制了作为构建快速重组制造系统核心的新颖的组态式柔性制造单元的原型,并给出生产工程中应用所创建的阵列式可重组制造系统的成功案例。     

车身车间AGV物料搬运系统小车数量配置规划

随着人力成本的不断高涨以及AGV技术的发展与应用,越来越多的汽车制造企业在构建生产物流体系时都在考虑运用AGV技术实现物料搬运自动化,从而降低企业的生产物流成本,提高生产物流的技术能级。从已实施的AGV自动化物料搬运项目看,AGV小车成本占了近70%的项目总体采购成本,因此AGV系统的小车数量规划问题是成功规划与实施AGV自动化物料搬运项目的关键问题之一。以上海通用汽车X工厂车身车间AGV自动化物料搬运项目中的AGV小车数量配置问题为研究对象,对AGV系统中的小车数量计算与规划方法进行了研究,在整理总结现有研究现状的基础上,首先归纳提出一种快速简便的理论计算模型,可以帮助企业在前期规划时参考使用;其次,借助排队论模型以最小总成本为目标,建立另一种数量配置计算模型,并对两种不同模型的计算结果进行了分析讨论,为企业在后续同类项目中合理规划AGV小车数量提供了参考分析方法。     

基于图论的AGV数量配置与调度优化方法

为了提高无人仓库的自动引导车（automated guided vehicle,AGV）系统运行效率,研究了大规模场景下无人仓库的AGV数量配置与调度问题。以最小化AGV数量和AGV总运输成本为目标,抽象出任务之间的时空约束来构建AGV共享网络,将数量配置与调度优化问题转化成图论当中的加权最小路径覆盖问题。计算结果表明：对比直接求解数学规划模型,图论方法在大规模场景下求解高效稳定,能够在满足任务时间要求的情况下,用更少的AGV数量以及对应运输成本最小的调度方案完成任务;针对300个任务规模的数量配置与调度问题,图论方法能够在4 s内完成求解,与数学规划模型的求解速度差距达到3个数量级,AGV数量减少10.3%。     

基于混沌粒子群优化算法的AGV路径规划研究

目的 优化物流AGV路径最优问题。方法 提出一种改进的混沌粒子群优化算法,采用基于Bézier曲线的路径规划模型,通过调整Bézier曲线的控制点数量,显著改善AGV轨迹路线的长度和平滑度。结果 采用混沌粒子群滤波算法（CPSO）最优化处理Bézier曲线的控制点数,引入适应度函数,评估是否满足终止标准,如果达到最大迭代次数或者在给定迭代次数时未修改最优解则终止CPSO算法,最后利用选取的控制点计算出更短、更平滑的轨迹路线,提高了算法的寻优能力。结论 采用CPSO算法初始化Bézier曲线可以获得更加平滑的最短路径。     

具有自动装卸搬运机器人的制造单元建模与分析

针对具有自动装卸搬运机器人的制造单元，着重考虑其双重资源约束的特点，提出了该系统的有限缓冲开排队网的建模方法。根据工件的加工、装卸与搬运等工艺流程，定义该系统的排队网模型的各种状态，基于连续时间马尔可夫链，采用精确解法对该模型进行分析求解，获得系统一系列的稳态性能指标。为了验证排队网建模方法的有效性，建立其对应的仿真模型，分别采用排队网模型的数值计算与仿真模型的仿真统计2种方法求出系统的性能指标进行对比，并基于排队网模型对该制造单元的系统性能进行分析，为该类制造单元的资源配置优化和设施布局优化等问题提供重要的理论支撑。     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.     

Using evolutionary algorithms and simulation for the optimization of manufacturing systems

This paper is concerned with an optimization method for manufacturing systems. This method can be applied to optimize problems with any type of variables (variables from a real set, e.g., conveyor speed; an integer set, e.g., size of buffer; or any general set, e.g., dispatching rules). It is based on the association of an evolutionary algorithm and a simulation model. Extensions of Michalewicz's evolutionary operators and algorithm are proposed to tackle manufacturing system problems. The particular case of stochastic models is discussed. This method is applied to an example: the configuration of a workshop producing plastic yoghurt pots. The criterion to optimize is the cost of the workshop and the three variables are the size of a silo, the size of a warehouse and a choice between two manufacturing methods. The application has been realized by connecting an evolutionary algorithm programmed in C and a simulation language.     

A robust cell formation approach for varying product demands

In cellular manufacturing environments, manufacturing cells are generally formed based on deterministic product demands. In this paper, we consider a system configuration problem with product demands expressed in a number of probabilistic scenarios. An optimization model integrating cell formation and part allocation is developed to generate a robust system configuration to minimize machine cost and expected inter-cell material handling cost. A two-stage Tabu search based heuristic algorithm is developed to find the optimal or near optimal solutions to the NP-hard problem. Numerical examples show that this model leads to an appropriate compromise between system configuration costs and expected material handling costs to meet the varying product demands. These example problems also show that the proposed algorithm is effective and computationally efficient for small or medium size problems.     

面向定制生产系统的缓冲区优化配置方法

针对定制型装备制造企业中具有有限缓冲区的开排队网制造单元,其车间负荷界限即缓冲设置难以确定的问题（buffer allocation problem,BAP）,文章对每阶段具有有限缓冲区且含有多台加工设备的三阶段柔性流水车间(flexible flow shop,FFS)进行排队网建模,应用状态空间分解法对该模型进行分析求解,获得系统的一系列性能指标值。为了对该方法的有效性进行验证,对该模型设计仿真实验,并利用扩展法对模型进行求解,将数值结果进行比较分析,验证了利用该方法对FFS缓冲区进行优化配置的合理性,这对较大规模的多节点每阶段具有多台设备的流水车间负荷界限的有效设定及其规划具有参考和指导意义。     

Effect of manufacturing system constructs on pick-up and drop-off strategies of multiple-load AGVs

This research investigates the interaction between manufacturing system constructs and the operation strategies in a multiple-load Automated Guided Vehicle System (AGVS) when AGVs in a system can carry two or more loads. The load pick-up problem arises when an AGV stops at a pick-up queue and has to decide which part(s) in the queue should be picked up. Since an AGV can carry multiple loads, a drop-off rule is then needed to determine the next stop for the AGV to deliver one or more loads. Several real-time composite heuristic rules for selecting load and determining the next stop are proposed and evaluated in two manufacturing system constructs: the jobshop and the flexible manufacturing system (FMS). A number of simulation models are developed to obtain statistics on various performance measures of the two system constructs under different experimental conditions. The simulation results reveal that the pick-up rules affect the system more than the drop-off rules. In general, rules to avoid starving and blocking in workstations perform better than the rules for shortest distance in throughput. However, the rules perform differently in jobshop and FMS based on other performance measures, indicating an interaction between system constructs and load selection strategies. The difference in rule performance within the same construct is also affected by several AGVS design parameters. Overall our study suggests that no load pick-up rule is always a champion, and the design of an efficient multiple-load AGVS must consider all issues in a global fashion.     

Load selection of automated guided vehicles in flexible manufacturing systems

This paper examines the operation of multiple-load AGVs in a flexible manufacturing system where AGVs in the system are capable of carrying two or more loads. The load selection problem arises when an AGV stops at a pick-up queue and has to decide which part(s) should be picked up. Five heuristic rules that may be used to select the load to be carried were suggested and evaluated under a hypothetical flexible manufacturing system with the aid of computer simulation. The results revealed that the variable-route-part-priority (VP) rule and fixed-route-part-priority (FP) rule generated significantly higher throughput than their counterparts, while the ’pick-all-send-nearest’ (PN) rule outperformed the other rules in part flowtime and work-in-process level. The results also suggest that when the carrying capacity of the AGV increases, the performance differences among the rules also increase. This finding sustains the need to explore an efficient operation strategy of multiple-load AGVs in flexible manufacturing systems.     

Analysis and solution to the single-level batch production smoothing problem

Many companies use mixed-model production systems running under the Just-in-Time philosophy in order to efficiently meet customer demands for a variety of products. Such systems require demand be stable and production sequence be leveled. The production smoothing problem aims at finding level schedules in which the appearances of products are dispersed over the horizon as uniformly as possible. In this paper, the production smoothing problem is extended to a more general manufacturing environment where a single machine can be identified as either the final or the bottleneck stage of the system and products may have arbitrary non-zero setup and processing time requirements on this single machine. An optimization model is built for the problem and a two phase solution methodology is developed. The first phase problem is shown to be NP-hard and a parametric heuristic procedure is proposed for its solution. In contrast, the second phase problem is shown to be efficiently solvable and currently available solution methods are adopted from the literature. A computational study is designed to test the proposed two phase solution methodology and also the parametric heuristic procedure. Computational results show that the proposed two phase solution methodology enables effective and efficient control of the studied manufacturing system, and the heuristic procedure developed for the first phase problem is time efficient and promises near optimal solutions for a variety of test instances.