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。     

考虑缓冲区库存分配的并联系统预防维护策略研究

为提高并联系统生产过程的连续性,提出了考虑缓冲区库存分配的并联系统(3M1B)预防维护模型。首先,考虑并联系统每台设备的故障率与维护率,在并联系统中上下游设备之间建立缓冲区,构建3M1B系统。其次,考虑设备不完美生产的可能性,以缓冲区库存量与设备运行周期为决策变量,以设备最小生产成本率为目标函数建立预防维护模型,求解最佳的预防维护策略与缓冲区库存分配策略。针对此维护模型,采用离散迭代算法与遗传算法进行求解,并比较了2种算法的优劣,结果表明,对于此模型,离散迭代算法优于遗传算法。最后,通过算例验证模型的可行性和有效性。     

冷柜门体混流装配线排序问题研究

以某冷柜公司门组生产系统为对象,研究了具有有限缓冲的两生产阶段排序问题,考虑该车间的发泡设备约束,分别对预装线投产排序与发泡线模具排序进行研究,以使得预装线与发泡线的运行尽可能匹配,有效地减少了发泡模具空模数量与在制品库存数量。特别设计库存量计算算法来计算空模数量与在制品库存数量,通过改进遗传算法分别求解两个阶段的排序方案。最后通过案例分析及算例对比验证了算法的有效性。     

面向置换流水车间生产过程的瞬态产能计算方法

针对具有可重入环节的置换流水车间易发生堵塞、生产布局规划难等情况，受伯努利理论启发，分析了此置换流水车间的生产流程，建立基于瞬时状态概率的系统生产过程性能预测模型。首先，根据加工任务多样性和生产工艺可靠性，定义人机混合加工机器的状态表达函数以及可靠性模型，并利用瞬态跃迁描述模型中的算子，建立瞬态产能模型，对置换流水车间的映射关系进行解析。其次，通过状态函数定义并计算加工机器和缓冲区的状态，对置换流水车间进行逆向建模。最后，根据实际生产情况设置相应生产参数并进行实验仿真。仿真结果证明，该模型具备可行性，能够为实际具有可重入环节的置换流水车间中的布局优化和高效运行提供理论支撑。     

基于赋时Petri网的成组生产单元缓冲区建模

以CD造船厂的某成组生产单元为研究对象,在对不同功能和结构的在制品缓冲区进行分类的基础上,建立了该生产单元的扩展赋时库所Petri网模型;将此Petri网映射为eM-Plant软件中的仿真模型,通过对在制品缓冲区库存量仿真数据的监控和分析,讨论了该成组生产单元的瓶颈环节、设备的负荷状况、零件排序调度的优化与否以及生产的连续性和稳定性.     

基于工序集的多阶段混合流水车间调度问题研究北大核心

针对砂型铸造车间包含并行工序集与批处理集的多阶段调度问题,总结了该类问题的特点和难点,构建了以最小化最大完工时间为优化目标的多阶段混合流水车间调度模型,采用了一种改进人工蜂群算法求解该模型。在算法中提出了基于插入原理与前驱工序释放时间的分段解码方法来有效利用机器空闲时间段,并引入了动态触发邻域机制增强算法的局部搜索能力,最后通过仿真实验验证了本文算法,解决此类问题的可行性和有效性。     

基于工序集的多阶段混合流水车间调度问题研究

针对砂型铸造车间包含并行工序集与批处理集的多阶段调度问题,总结了该类问题的特点和难点,构建了以最小化最大完工时间为优化目标的多阶段混合流水车间调度模型,采用了一种改进人工蜂群算法求解该模型。在算法中提出了基于插入原理与前驱工序释放时间的分段解码方法来有效利用机器空闲时间段,并引入了动态触发邻域机制增强算法的局部搜索能力,最后通过仿真实验验证了本文算法,解决此类问题的可行性和有效性。     

不确定环境下复杂机械装配系统设备维修方法

为保障不确定环境下复杂机械装配系统的连续性并降低其维修成本,提出一种以马尔可夫决策理论为基础的设备维修策略动态选择方法。在综合考虑系统运行成本、缓冲库存成本、设备维修成本及停机损失成本的基础上,构建了装配系统可靠性成本模型。该模型以带有中间缓冲区的二级装配系统为研究对象,以设备状态和缓冲库存量为自变量,以可靠性成本为目标函数。分析了装配系统的不同运行状态,利用模拟退火算法和模糊非线性混合整数目标规划对可靠性成本模型求解,制定装配系统最优维修方法。该方法降低了装配系统停机时间,减少了设备维修次数,可为生产线设计和维修计划的制定提供依据。最后,通过算例分析验证了模型的有效性和可行性。     

带缓冲多阶段系统的生产批量与维护计划集成优化

以最小化生产与维护总成本为目标,通过引入役龄递减因子与失效率递增因子对设备故障率演化进行建模,综合考虑完美维护、非完美维护和故障维修,建立了在带缓冲多阶段的生产系统环境下,基于加工失效假设的生产与维护集成计划模型,并设计基于时间分解的模拟退火算法求解该问题。最后,算例分析验证了模型的有效性。     

不确定环境下等待时间受限的混合流水车间调度问题研究

带有等待时间受限约束的混合流水车间调度问题具有强NP难的特性,而实际生产过程中产品需求时间以及机器加工时间等各种不确定性因素,更为求解此类问题带来了复杂性和挑战。通过探索这类问题的特征,建立了两阶段随机规划模型,开发了L型切面的求解算法,在目标为成本期望最小的情况下,给出了这类问题的近似最优调度解。建立的模型针对于生产过程中的稳态,以更好地适应实际生产的需要。通过对算法性能进行理论分析和实验验证,证明了该算法的有效性。     

System performance analysis of flexible flow shop with match processing constraint

Blocking makes the analytical modelling of open queuing networks with finite buffers intractable because the product-form solutions are unavailable. In this paper, about mould manufacturing, a four-stage flexible flow shop with match processing constraint is first modelled as an open queuing network with finite buffers. According to the characteristics of the matching node, the inter-arrival time distributions of parts arriving at the nodes are assumed to be exponential or general. Next, based on approximation of queuing theory, the Decomposition of State Space Method and the Generalized Expansion Method are developed for system performance evaluation. Then, experiments to assess the accuracy of the proposed methods are reported by comparing the analytical results with simulations. Finally, a case of a real-life production system of mould manufacturing is studied to show the application of the proposed methods. The results of experiments reveal that the proposed methods are feasible and effective for system performance evaluation and they can even solve large-size practical problems in a reasonable time. The results in this paper can provide a basis for system design or resource planning, to solve buffer allocation problems and capacity configuration problems.     

蚁群系统结合指派规则求解HFS调度问题

以NP-难的最小化时间表长为目标的混合流水车间调度问题为研究对象。把工件在第1阶段开始加工的排序问题转化为旅行商问题,采用蚁群系统求得初始排序;在第1阶段后各阶段采用工件先到先服务规则选择工件、最先空闲机器优先规则选择机器以构建初始工件的机器指派与排序;充分利用已知的机器布局和工件加工时间特点,确定工件加工瓶颈阶段,并以此为基础对工件的机器指派与排序进行改进。用Carlier和Neron设计的Benchmark算例仿真后与著名的NEH算法比较,表明这种算法是有效的。     

具有两道工序的柔性同序加工车间任务投放策略

任务投放上限值是负荷控制理论中任务投放策略的关键量之一。为帮助任务投放决策者确定每个周期任务投放量的上限值,针对具有两道工序的柔性同序加工车间任务投放上限值确定问题,提出了一种在等周期条件下通过仿真求解任务投放量上限值的方法,并且通过仿真计算说明了这种方法的合理性与有效性。     

Reliability with finite buffer size for a multistate manufacturing system with parallel production lines

Abstract

This study examines system reliability for a manufacturing system with parallel production lines by creating a model of a multistate manufacturing system (MMS). System reliability is defined as the probability of demand satisfaction, which reflects the probability that the current capacity state of the MMS can successfully process a given demand. In particular, a buffer station with a finite size is taken into consideration to avoid blockage and starvation in the MMS. To the best of our knowledge, there is no existing research that considers a finite buffer size in a model of an MMS with parallel production lines. This study proceeds through the following phases. (i) In the model construction phase, the amount of input material, workload, and minimal capacity required by each workstation to satisfy a given demand level are studied by flow analysis; subsequently, a buffer usage matrix (BUM) is proposed to calculate buffer reliability. (ii) In the performance evaluation phase, system reliabilities with both infinite and finite buffer sizes are derived in terms of both minimal capacity vector and buffer reliability. (iii) In the case demonstration phase, two examples are utilized to illustrate the proposed method. Results of both examples show that the system reliability is overestimated with an infinite buffer size.     

Using queuing theory and simulated annealing to design the facility layout in an AGV-based modular manufacturing system

An automated guided vehicle-based flow production system is used for manufacturing prefabricated bathroom units. One unit can occupy a space of more than 10?m². Due to large time deviations in sequential processes, queues are formed and greater plant space is needed. Reducing work-in-progress helps to save plant space but renders manufacture less efficient. The research explores better workstation arrangements. An open queuing network (OQN) model was used to approximate the flow production system. Since the problem of workstation arrangement is a combinatorial optimisation problem, simulated annealing (SA) was applied to search for a good solution. The combination of an OQN model and SA provides a powerful tool to solve the facility layout problem for a stochastic flow production system. The experimental results show that the proposed approach has the potential to guide industrial layout design and practice.     

Queuing network analysis approach for estimating the sizes of the time buffers in Theory of Constraints-controlled production systems

The present paper describes an open queuing network modelling approach to estimate the size of the time buffers in production systems controlled by the Theory of Constraints philosophy. Workstations in the production network are modelled as GI/G/m queues and a queuing network analysis multiproduct open queuing network modelling method is used to estimate the average flow time to the time buffer origin and the standard deviation of flow time. Using these two values together with an assumption of normally distributed flow times and a chosen service level, the final time buffer length is determined. The queuing network analysis method has been modified to enable the modelling of production networks with machine failures, batch service and varying transfer batch sizes. The modelling approach has also been incorporated in a computerized tool that uses product specific information such as bill-of-material and routing data, and production network information such as resource data to estimate the sizes and location of the necessary time buffers for each product. Simulation experiments indicate that the procedure is sufficiently accurate to provide an initial quick estimate of the needed time buffer lengths at the design stage of the line.     

Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times

This paper studies the makespan minimisation scheduling problem in a two-stage hybrid flow shop. The first stage has one machine and the second stage has m identical parallel machines. Neither the processing time nor probability distribution of the processing time of each job is uncertain. We propose a robust (min–max regret) scheduling model. To solve the robust scheduling problem, which is NP-hard, we first derive some properties of the worst-case scenario for a given schedule. We then propose both exact and heuristic algorithms to solve this problem. In addition, computational experiments are conducted to evaluate the performance of the proposed algorithms.     

Real-time deadlock-free control strategy for single multi-load automated guided vehicle on a job shop manufacturing system

An unmanned automated job shop manufacturing system with a single multi-load automated guided vehicle, which traverses around a single-loop guidepath, is considered in this work. This type of shop design is often used as an independent sector of some complex AGV layouts, such as tandem, segmented bi-directional single-loop and divided configurations. The type of multi-load vehicle is a good alternative against using more single-load vehicles to serve a higher transportation demand. To an unmanned automated manufacturing system, the management of finite system resources, e.g. finite input/output queuing space and transporting carriers, plays a vital role in avoiding system deadlocks and machine blockages. The proposed control strategy for a single multi-load vehicle uses global shop real-time information to achieve the objectives: avoid shop deadlocks caused by inappropriate job movement as well as satisfy the system transport requirement. The efficiency of the proposed vehicle control strategy and the other two expanded strategies under various parameter designs are verified by computer simulation.     

An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that considers both setup and transportation times still has rare research. This paper addresses the energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time to minimise both makespan as economic objective and energy consumption as green objective. The mathematical model of the problem is formulated. To solve such a bi-objective problem effectively, an improved multi-objective evolutionary algorithm based on decomposition is proposed. With decomposition strategy, the problem is decomposed into several sub-problems. In each generation, a dynamic strategy is designed to mate the solutions corresponding to the sub-problems. After analysing the properties of the problem, two heuristics to generate new solutions with smaller total setup times are proposed for designing local intensification to improve exploitation ability. Computational tests are carried out by using the instances both from a real-world manufacturing enterprise and generated randomly with larger sizes. The comparisons show that dynamic mating strategy and local intensification are effective in improving performances and the proposed algorithm is more effective than the existing algorithms.