Performability of systems based on renewal process models

A system of N statistically identical machines is modeled using renewal theory through a unique architecture and state space. The model is very useful in evaluating flexible manufacturing systems (FMS) in particular. Each machine consists of multiple part types that are subject to individual failure. A multiple stage repair process composed of integrable sojourn time distributions requires access to spare parts to repair down machines. A performability measure is used to gauge system effectiveness for this partially degradable system, and an example is given     

Manufacturing cell formation with production data using neural networks

Batch type production strategies need adoption of cellular manufacturing (CM) in order to improve operational effectiveness by reducing manufacturing lead time and costs related to inventory and material handling. CM necessitates that parts are to be grouped into part families based on their similarities in manufacturing and design attributes. Then, machines are allocated into machine cells to produce the identified part families so that productivity and flexibility of the system can be improved. Zero-one part-machine incidence matrix (PMIM) generated from route sheet information is commonly presented as input for clustering of parts and machines. An entry of ‘1’ in PMIM indicates that the part is visiting the machine and zero otherwise. The output is generated in the form of block diagonal structure where each block represents a machine cell having more than one machines and a part family. The major limitations of this approach lies in the fact that important production factors like operation time, sequence of operations, and lot size of the parts are not accounted for. In this paper, an attempt has been made to propose a clustering methodology based on adaptive resonance theory (ART) for addressing these issues. Initially, a methodology considering only the operation sequence of the parts has been proposed. Then, the methodology is suitably modified to deal with combination of operation sequence and operation time of the parts to address generalized cell formation (CF) problem. A new performance measure is proposed to quantify the performance of the proposed methodology. The performance of the proposed algorithm is tested with benchmark problems from open literature and the results are compared with the existing methods. The results clearly indicate that the proposed methodology outperforms the existing methods in most cases.     

COS:度量分布式大数据处理系统的效率

【目的】在大数据处理领域,分布式计算系统得到广泛应用,它们的可扩展性得到重点关注,但其绝对性能往往没有得到重视。我们希望提出科学合理、与时俱进的度量标准,对分布式系统的性能进行评估。【方法】本文通过对比特定任务的单机实现和分布式实现来讨论分布式系统的性能,提出COS(Configuration that Outperforms a Single machine)这一指标,来衡量分布式系统在达到单台机器的性能时,需要的硬件资源数量。我们选取k-means聚类和逻辑回归两个经典机器学习算法,对其进行单机多线程实现,并通过向量化计算、优化内存分配与访问等方式对性能进行了优化,为分布式多机系统的性能提供参考。【结果】以Apache Spark作为对标系统,实验发现无论是使用其原生编程接口,还是经过悉心优化的机器学习库,都要使用数倍甚至数百倍的机器,才能达到单机多线程实现的性能。【局限】分布式系统与单机实现进行性能对比并不是完全公平的,分布式系统的额外开销客观存在。【结论】但COS指标仍能反映分布式系统存在的绝对性能较差、没有充分利用硬件优势等问题。     

Performance analysis of manufacturing systems composed of modular machines using the universal generating function

The modularity and reconfigurability of the building blocks of modern manufacturing systems have to be considered when evaluating their performance. This paper proposes a model for evaluating system availability and expected production rates for manufacturing systems that are composed of unreliable modular machines with multiple functionally parallel production units. These units are treated as independent modules, where the breakdown/stoppage of one unit does not necessitate the failure/stoppage of the whole machine and its production. The considered systems are multi-state manufacturing systems (MSMS) that can handle multiple parts simultaneously, and their structure is that of nonbuffered flow lines allowing paralleling of identical multi-state modular machines (MSMM) in each production stage. In spite of the inherent computational complexity of the proposed analysis, due to the large number of system states, it was made possible by the use of the universal generating function (UGF) technique, which proved efficient for large MSMS. The proposed model was applied to a number of case studies for demonstration and verification. The case studies were based on a family of engine front covers. The results show that machines with a larger number of modules, usually thought of as having lower availability, provided higher overall system availability in the case of machines with multiple spindles. Based on the new analysis and results, it is recommended that system designers favorably consider machines with multiple spindles rather than increasing the number of machines in parallel. These results provide an important support for the use of modular/reconfigurable equipment compared with traditional equipment, in spite of the higher cost.     

Part family formation based on a new similarity coefficient which considers alternative routes during machine failure

A number of research papers have used different types of similarity and dissimilarity coefficients for determining part families. In cellular manufacturing systems, most machines are capable of performing more than one operation, which makes parts rerouting feasible. When a part is rerouted, it affects the cell performance. Most of the suggested approaches in the literature develop a new similarity coefficient based on mathematical analysis, however, these methods tend to disregard alternative routes during machine failure. The main objective of this paper is to identify part families based on a new similarity coefficient which considers the number of alternative routes available during machine failure. Based on the new similarity coefficient, the part families were identified by using a p-median model.     

A new environment-aware scheduling method for remanufacturing system with non-dedicated reprocessing lines using improved flower pollination algorithm

As the resource crisis and environmental pollution become increasingly prominent, remanufacturing is currently a popular research field for addressing these issues. However, scheduling for remanufacturing is more difficult than that for traditional manufacturing because the entire remanufacturing system (RMS) involves three cooperative subsystems: disassembly, reprocessing, and reassembly. Few studies have focused on the scheduling of the entire RMS and addressed the quality differences of defective components using non-dedicated reprocessing lines. Thus, this paper proposes a new environment-aware scheduling model for RMS, which considers not only three subsystems simultaneously, but also the non-dedicated reprocessing lines related to the recycled quality of defective components. The proposed model also integrates environmental factors by considering the carbon emissions of machines to take advantage of the environmental benefits in remanufacturing. To solve the proposed model, an improved flower pollination algorithm with new two-dimensional representation scheme is employed, which not only utilizes the self-adaptive parameter but also integrates path relinking technique, local search strategy, and elite replacement strategy. Experiments are performed to illustrate the effectiveness of the proposed algorithm for solving the remanufacturing scheduling problem by comparing it with six baseline algorithms.     

基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护

在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.如何利用现有资源,提高生产效率,实时地对系统性能进行评估与预测,并对基于小批量生产的实时调度进行优化改进,在分布式柔性生产系统中具有重要的研究意义.因此,基于退化机器模型的多批次串行生产线的性能进行分析,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先采用马尔科夫分析方法建立数学模型;随后,提出精确方法来计算此生产系统模型实时的性能指标,并针对该模型下的调度问题,设计最优完成时间指标优化算法;此外,提出基于退化机器模型的预测性维护策略以减少完成时间;最后,通过数值实验验证该算法的可行性和有效性.     

The sustainable cell formation problem: manufacturing cell creation with machine modification costs

An approach for manufacturing cell formation with machine modification is presented. In cell formation it is often important in practice to be able to reassign parts to additional machine types in order to create better cell configurations. This involves extending the set of parts that certain individual machines can process. Such extensions may be cheaper than simply purchasing additional machines. Thus, there is the possibility of machine modification to reduce inter-cell travel. The cost of such modifications must be balanced by the consequent reduction in inter-cell travel cost. The extended machine cell formation problem to be described involves the specification of which individual machines should be modified to enable them to process additional part types, part-machine assignment, and the grouping of individual machines for cell formation. The objective is to minimize the sum of the machine modification costs and the inter-cell travel. We call this the sustainable cell formation problem (SCFP). As far as the authors are aware, there have not been any solution procedures for this important problem reported in the open literature. It is our purpose to fill this gap by presenting a mixed integer programming model of the SCFP. We also propose and analyze greedy and tabu search heuristics for the design of large-scale systems related to the SCFP. Computational experience with the solution procedures indicates that they are likely to be useful additions to the production engineer's toolkit.     

A stochastic model for the analysis of a two-machine flexible manufacturing cell

This paper presents a stochastic model to determine the performance of a flexible manufacturing cell (FMC) under variable operational conditions, including random machining times, random loading and unloading times, and random pallet transfer times. The FMC under study consists of two machines, pallet handling system, and a loading/unloading robot. After delivering the blanks by the pallet to the cell, the robot loads the first machine followed by the second. Unloading of a part starts with the machine that finishes its part first, followed by the next machine. When the machining of all parts on the pallet is completed, the handling system moves the pallet with finished parts out and brings in a new pallet with blanks. A model with these characteristics turns out to be a Markov chain with a transition matrix of size 5n+3, where n is the number of parts on the pallet. In this paper, we present exact numerical solutions and economic analysis to evaluate FMC systems, to determine optimal pallet capacity and robot speed that minimize total FMC cost per unit of production.     

An object-oriented approach for multi-objective flexible job-shop scheduling problem

Flexible manufacturing systems are very complex to control and it is difficult to generate controlling systems for this problem domain. Flexible job-shop scheduling problem (FJSP) is one of the instances in this domain. It is a problem which inherits the job-shop scheduling problem (JSP) characteristics. FJSP has additional routing sub-problem in addition to JSP. In routing sub-problem each operation is assigned to a machine out of a set of capable machines. In scheduling sub-problem the sequence of assigned operations is obtained while optimizing the objective function(s). In this paper an object-oriented (OO) approach is presented for multi-objective FJSP along with simulated annealing optimization algorithm. Solution approaches in the literature generally use two-string encoding scheme to represent this problem. However, OO analysis, design and programming methodology help to present this problem on a single encoding scheme effectively which result in a practical integration of the problem solution to manufacturing control systems where OO paradigm is frequently used. OO design of FJSP is achieved by using UML class diagram and this design reduces the problem encoding to a single data structure where operation object of FJSP could hold its data about alternative machines in its own data structure hierarchically. Many-to-many associations between operations and machines are transformed into two one-to-many associations by inserting a new class between them. Minimization of the following three objective functions are considered in this paper: maximum completion time, workload of the most loaded machine and total workload of all machines. Some benchmark sets are run in order to show the effectiveness of the proposed approach. It is proved that using OO approach for multi-objective FJSP contributes to not only building effective manufacturing control systems but also achieving effective solutions.     

Intelligent manufacture of laser cutting, punching and bending parts

It is possible to carry out the flexible manufacture of small parts using existing machine tools. Various computer programs and expert systems are required for this purpose. Continuous further development of the installation is possible with expert systems. The most important software component is a CAD system suitable for the input of sheet parts and which is externally accessible, along with expert systems, for the determination of developed projections and bending sequences and also for the generation of laser and punching plans for the sheet blanks. The system described here, for the flexible manufacture of small parts, was designed for best possible compatibility with different machines and adaptibility to new information.     

Design of cellular manufacturing systems with assembly considerations

This research proposes incorporating assembly aspects associated with a product into the design of Cellular manufacturing System (CMS). The literature on CMS design implicitly assumes that finished part is the end product by itself. In practice, often, manufacturers produce parts which are assembled into a finished product. The methodology employs a part–subassembly matrix derived from the product structure in addition to the part–machine matrix. A mathematical programming model is developed which determines an assignment of parts, machines and subassemblies to manufacturing cells. The proposed model employs a new similarity coefficient between part, machine and subassembly. The model resulted in a nonlinear program with 0-1 variables. A case study has been analyzed based on a published part–machine matrix and a randomly generated product structure. The analysis reveals that it may be required to forego some of the efficiencies of Group Technology (GT) in order to achieve integration of assembly operations with production of parts. From a practical stand point of view it is preferred to have a system design which has a mix of GT and integration efficiencies, compared to a design which outperforms on GT criteria and completely lacks integration of assembly operations with production of parts.     

Use of (max, +) algebra for scheduling and optimization of HVLV systems subject to preventive maintenance

High-Variety, Low-Volume (HVLV) manufacturing systems are built to produce parts of several types in small quantities and under multiple production objectives. They relate to job-shop systems well known by researchers. One of the most studied assumptions of HVLV systems scheduling is considering that machines may be periodically unavailable during the production scheduling. This article deals with an analytical integrating method using (max, +) algebra to model HVLV scheduling problems subject to preventive maintenance (PM) while considering machines availability constraints. Each machine is subject to PM while maintaining flexibility for the start time of the maintenance activities during the planning period. The proposed model controls the placement of maintenance activities along the production operations. Indeed, the sequencing of maintenance activities on the machines depends on the criteria to minimize and may be different for each criteria value. For preventive maintenance, the proposed model aims to generate the best sequencing between activities while respecting the planning program that satisfy the optimal criteria values. In order to illustrate the performance of the proposed methodology, a simulation example is given.     

Intelligent Choice of Machine Learning Methods for Predictive Maintenance of Intelligent Machines

Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are explained. Then factors for the forecast are explained. Requirements for the data are collected and algorithms for machine learning are presented. Based on the relationships found, a process model is presented that shows a fast implementation of the predictive maintenance for machines.     

TRIPOS—a portable operating system for mini-computers

There are several successful operating systems for mini-computers written in high level languages and the time is now ripe for the development of portable systems for such machines. The system described in this paper is primarily designed to provide a friendly interactive multiprocessing environment for a single user. From his point of view, substantial parts of the system are completely machine independent. These include, for instance, the filing system, the command language, text editors, overlaying facilities and interprocess communication primitives. The system is suitable for many different application areas ranging over process control, data acquisition, data communication, text handling, data base systems and teaching.     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.     

Genetic algorithm for maximizing the parts flow within cells in manufacturing cell design

In this paper, we propose a cell design model with the objective of maximizing the total parts flow within cells considering the data of process plans for parts, production volume, and cell size. A relationship between machines is calculated on the basis of the process plans for parts obtained from process plan sheets. Then the machines are classified into machine cells using the relationship. The model is formulated as a 0–1 integer programming and a genetic algorithm approach is developed to solve the model. The proposed approach is tested and proved using actual industrial data. The approach provide a best solution constantly, also its procedure is extremely efficient and flexible for solving large problems for cell design.     

Model referenced monitoring and diagnosis — application to the manufacturing system

This paper describes “model referenced monitoring and diagnosis” a systematic method of monitoring and diagnosis. In this method, a model is used either to generate standard values for the monitoring parameters, or to derive a systematic diagnostic algorithm. As examples of the model referenced monitoring in manufacturing systems, the cutting torque prediction system and tool breakage detection system are described. In the former example, the cutting torque values at each point in time, which can be used as reference values in monitoring the abnormal cutting condition, are evaluated based on a solid modelling system. In the latter, an autoregressive (AR) model is adaptively fitted to the cutting torque signal in order to detect any sudden change in the cutting state due to tool breakage. Two examples are also described for the case of model referenced diagnosis; the diagnosis of sequentially controlled machines using the state graph model, and diagnosis by means of a failure causality model. The former method is applicable to machines controlled by sequence control. Based on the state graph of the machine and the controller, the diagnostic programme can be generated in combination with the control programme. The failure causality model represents the propagation of the effects of failures in the machine. All possible combinations of failure causes are obtained by solving the simultaneous Boolean equations derived from the model.     

基于Petri网的JSP动态分类调度

以带有控制器的 Petri 网为建模工具对柔性生产调度中的离散事件建模,通过构建Petri 网控制器使系统的运行满足期望的目标,同时利用混合遗传算法获得调度结果,用于解决作业车间的加工受到机床、操作工人等资源制约条件下的动态优化调度。为了保证生产的平稳性,最大限度地维持车间的生产能力,提出了针对不同的扰动进行分类处理的新方法,首先基于机床故障修复时间、工人离岗时间及取消订单包含任务的多少进行分类调度,然后根据机床故障修复后以及工人回岗后剩余任务的多少决定是否进行再一次的调度,最后对算法进行了仿真研究。