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。     

Simulation study of a bottleneck-based dispatching policy for a maintenance workforce

Maintenance is important for production operations and for continuous improvement. Appropriate dispatching of the maintenance workforce to quickly respond to equipment failures and carry out preventive services can improve system productivity. The first-come-first-served policy is typically used in many manufacturing industries. In this paper, we present a priority-based dispatching policy, a dynamic bottleneck policy, based on the analysis of real-time data. In such a policy, priority is assigned to the bottleneck machine after a fixed time period, and the maintenance worker will service the high-priority machine (i.e. bottleneck machine) first when multiple service requests are received. It is shown by extensive simulation experiments that this policy can lead to a greater improvement in system throughput compared with the first-come-first-served policy. To implement such a policy, the appropriate time period for data collection and the frequency for carrying out bottleneck analysis are investigated. In addition, a sensitivity study suggests that the results obtained are insensitive to machine downtime, efficiency, and reliability models.     

Real time production improvement through bottleneck control

Variability is a key characteristic for evaluating the performance of a process. Small variability for a bottleneck machine can generate high production variability. Short-term production analysis and bottleneck identification are imperative for enabling optimal response to dynamic changes within the system. In comparison to the rich and abundant literature available on long-term analysis, only a small section of the literature addresses the dynamic bottleneck control policies, which may be used to maximise sustainable benefits. In this paper, a real time bottleneck control method is introduced to efficiently utilise the finite manufacturing resources and to mitigate the short-term production constraints by using two practical approaches: initial buffer adjustment and maintenance task prioritisation. The objective for real time bottleneck control is to obtain a continuous production improvement towards a balanced-line status to increase the throughput efficiently. The benefits of this method are presented by considering an industrial case study of an automotive assembly line. The results obtained from this case study show significant production improvements as compared to traditional approaches.     

Modified drum–buffer–rope scheduling mechanism for a non-identical parallel machine flow shop with processing-time variation

The drum–buffer–rope (DBR) is a scheduling mechanism under the Theory of Constraints (TOC) philosophy. In DBR, ‘drum’ is a production schedule on the capacity-constrained resources (CCRs), which controls the speed of production for the whole system; ‘rope’ is a mechanism to release the required material to the CCRs; and ‘buffer’ is used to protect the CCRs from starvation due to statistical fluctuations. For a non-identical parallel machine flow-shop environment, estimating an efficient rope and time buffer for DBR implementation is not an easy task because of the complexity of non-identical parallel machine loading. This paper proposes a new scheduling method, which is called the modified DBR (MOD-DBR). It applies a backward finite capacity scheduling technique, including machine loadings and detail scheduling, instead of the rope mechanism in DBR. The scheduling performances of MOD-DBR are evaluated under variable processing time situations. The experimental results indicate that the MOD-DBR without a time buffer outperformed the DBR with a considerable level of buffer on the average flow time, while they have the same performance on tardiness, constraint resource utilization, and throughput.     

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 tabu search approach for buffer allocation in production lines with unreliable machines

The optimal allocation of buffers is an important research issue in designing production lines. In this study, a tabu search (TS) algorithm is proposed to find near-optimal buffer allocation plans for a serial production line with unreliable machines. The main objective is to maximize the production rate, i.e. throughput, of the line. The efficiency of the proposed method is also tested to solve buffer allocation problems with the objective of total buffer size minimization. To estimate the throughput of the line with a given specific buffer allocation, an analytical decomposition approximation method is used. The performance of the tabu search algorithm is demonstrated on existing benchmark problems. The results obtained by the TS algorithm are clearly encouraging, as the TS algorithm is much better than the other algorithms for all considered benchmark problems.     

The use of maintenance indicators to evaluate the effects of maintenance programs on NPP performance and safety

Nuclear Power Plants (NPPs) under commercial operation are under continuous demand to meet higher levels of performance and safety by NPP owners, regulatory authorities and the public in general. Maintenance plays an important role in achieving such a goal, therefore, many programs are being conducted in order to improve their effectiveness. A common link between these programs is the necessity to evaluate how maintenance affects NPP performance and safety. This paper presents the foundation of a methodology for a maintenance evaluation program based on maintenance indicators and how it is applied to monitoring the effectiveness of the maintenance at the Cofrentes NPP. The methodology is general and might be applied in other fields of industrial engineering, particularly in those activities which devote many resources to maintain plant equipment, and also in those with less but very critical maintenance.     

Simulation of unbalanced buffer allocation in unreliable unpaced production lines

This article presents the results of a study investigating the performance of unpaced unreliable production lines (i.e. subject to breakdown) that are unbalanced in terms of their buffer storage sizes. Simulation is carried out for five, eight and 10 station lines with mean buffer space set at two, four and six units. Buffer capacity is allocated in different configurations for each of these lines. Performance indicators on throughput, idle time and average buffer level are analysed using a range of statistical tools, and relationships between the independent and dependent variables are determined. Overall results show that the best patterns for unreliable lines in terms of generating higher throughput rates (or lower idle times) as compared to a balanced line are those where total available buffer capacity is allocated as evenly as possible between workstations. In contrast, concentrating more buffer capacity towards the end of the line gives best average buffer level results.     

Throughput centered prioritization of machines in transfer lines

In an environment of scarce resources and complex production systems, prioritizing is key to confront the challenge of managing physical assets. In the literature, there exist a number of techniques to prioritize maintenance decisions that consider safety, technical and business perspectives. However, the effect of risk mitigating elements—such as intermediate buffers in production lines—on prioritization has not yet been investigated in depth. In this line, the work proposes a user-friendly graphical technique called the system efficiency influence diagram (SEID). Asset managers may use SEID to identify machines that have a greater impact on the system throughput, and thus set prioritized maintenance policies and/or redesign of buffers capacities. The tool provides insight to the analyst as it decomposes the influence of a given machine on the system throughput as a product of two elements: (1) system influence efficiency factor and (2) machine unavailability factor. We illustrate its applicability using three case studies: a four-machine transfer line, a vehicle assembly line, and an open-pit mining conveyor system. The results confirm that the machines with greater unavailability factors are not necessarily the most important for the efficiency of the production line, as it is the case when no intermediate buffers exist. As a decision aid tool, SEID emphasizes the need to move from a maintenance vision focused on machine availability, to a systems engineering perspective.     

Transient behavior of serial production lines with Bernoulli machines

The steady-state performance of production systems with unreliable machines has been analyzed extensively during the last 50 years. In contrast, the transient behavior of these systems remains practically unexplored. Transient characteristics, however, may have significant manufacturing implications. Indeed, if, for example, transients are sluggish and the steady state is reached only after a relatively long settling time, the production system may lose some of its throughput, thus leading to a lower efficiency. This paper is devoted to analytical and numerical investigation of the transient behavior of serial production lines with machines having the Bernoulli reliability model. The transients of the states (i.e., the probabilities of buffer occupancy) are described by the Second Largest Eigenvalue (SLE) of the transition matrix of the associated Markov chain. The transients of the outputs (i.e., production rate, PR, and work-in-process, WIP) are characterized by both the SLE and Pre-Exponential Factors (PEF). We study SLE and PEF as functions of machine efficiency, buffer capacity and the number of machines in the system. In addition, we analyze the settling times of PR and WIP and show that the former is often much shorter than the latter. Finally, we investigate production losses due to transients and show that they may be significant in serial lines with relatively large buffers and many machines. To avoid these losses, it is suggested that all buffers initially be half full. For two- and three-machine lines these analyzes are carried out analytically; longer lines are investigated by simulations.     

Throughput-based importance measures of multistate production systems

Many production systems are multistate, with a finite number of performance levels that are between perfect functioning and complete failure. Importance measures are often used in the maintenance planning of complicated systems, to observe the criticalities of components, reveal the system weakness, and thus to guide the allocation of limited maintenance resources. This paper compares several commonly used importance measures for multistate systems, and investigates their effectiveness and limitations with a simple example. These existing measures focus on the states of a system at some moment, while ignoring the dynamic behaviours in the long-term. For a production system, however, its throughput in a certain period, rather than the instantaneous performance, is the system property of interest. Therefore, two new long-term throughput-based importance measures: total throughput importance measure and maintenance effect importance measure are proposed in this paper, to answer the questions about the criticalities of different components and the long-term effects of successful maintenance activities on the throughput of a production system in a certain period. A case study on an offshore production system is conducted, to illustrate how the new importance measures work and what kind of implications can be provided to the maintenance crew.     

Optimal maintenance planning and crew allocation for multipurpose batch plants

A mathematical programming approach to optimize process plant performance subject to equipment failure is presented. The optimal production planning and scheduling in multipurpose process plants involves the efficient utilization of assets and resources to produce a number of products so as to satisfy market demands while optimizing a performance criterion. However, the degree of utilization of process plant components, within the time horizon of operation, critically depends on the level of equipment availability. The interactions between production and maintenance planning as well as the necessary links to quantify the strong interactions between them are studied. The preventive maintenance planning and crew allocation problem are used to demonstrate the effectiveness of the proposed approach. The overall problem is first formulated as an optimal control problem by integrating an aggregate production planning model with a continuous time Markov chain maintenance model. The resulting problem is then transformed into a mixed-integer linear programming model by using an Euler discretization scheme and appropriate linearizations of bilinear terms. Finally, extensions to include design aspects are also discussed. The applicability of the proposed approach is demonstrated by a number of illustrative examples.     

Integrating Six Sigma Culture and TPM Framework to Improve Manufacturing Performance in SMEs

Worldwide, small and medium enterprises (SMEs) have been accepted as the engine of economic growth, especially in the emerging economies, and for promoting equitable development their contribution as a component to nation's infrastructure is relatively high. Global competition forces SMEs to improve their competitiveness by enhancing their manufacturing performance. Hence, they have to pay attention to the reliability of their production processes as well as their commitment to quality management practices. In present times, because of automation and large‐scale mechanization, higher plant availability, better product quality and longer equipment life had assumed considerable significance among the SMEs. In order to meet the above challenges, adoption and practice of proactive maintenance strategies along with execution of best management practices, i.e. lean Six Sigma (LSS), has become essential for organizations to bring down maintenance and production costs. As evident from literature, both total productive maintenance (TPM) and Six Sigma are key business process strategies employed by SMEs to enhance their manufacturing performance. However, whilst there is significant research information available on implementing these systems in an independent manner, there is little information available relating to the integration of these approaches to provide a unified and highly effective implementation framework. The purpose of this paper is thus to develop an integrated model based on Six Sigma and TPM framework focusing on adding performance indicators. The goals of the study were achieved by utilizing various tools such as ‘brainstorming sessions, pareto analysis fish‐bone diagrams, histograms, FMEA, box plots, control charts and process capability plots for analysis’. While implementation of such twin framework requires greater management commitment in terms of training, resources and integration, they are also expected to provide higher levels of equipment and plant performance as evident from the results. The results show significant improvement in OEE level (Before 50, 54 and after 76, 83 for M/C –I and M/c‐II), reduction in rework (from 22% to 10%), reduction in maintenance versus operation cost (from 30% to 10%) and reduction in defect rate (from 24.82% to 5%) and average outgoing quality level (from 30% to 5%). The substantial improvement in sigma level from 2.16 and 2.64 to 4.01 and 4.12 for M/C –I and M/C‐II resulted in financial savings of approximately $2 m per annum. Copyright © 2013 John Wiley & Sons, Ltd.     

Plant-level maintenance decision support system for throughput improvement

In the current global scenario of extreme competition, factors such as productivity, availability, quality and cost of operations play a vital role in the success of a company. A critical component relating to all of the above is maintenance. The conventional maintenance decision support systems have primarily focused on maximising the gains of a single machine system. However, a real life application usually consists of multiple machines, and the operational level decisions are more complex. In this paper, an on line plant-level maintenance decision support system (PMDSS) is developed by combining the short term and long term decision making process to improve the overall system performance while continuously attempting to maximise immediate profits in the short term. The PMDSS works towards two basic aims: (1) unplanned downtime reduction by predicting the remaining useful life of the machines, and (2) efficient utilisation of the finite maintenance and production resources through identifying the throughput-critical machines. The benefits of this approach are presented by considering an industrial case study of an automotive assembly line. The results obtained using this PMDSS approach shows a big throughput improvement as compared to the conventional maintenance policies.     

Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation

Numerous studies have investigated the effects of unbalanced service times and inter-station buffer sizes on the efficiency of discrete part, unpaced production lines. There are two main disadvantages of many of these studies: (1) only some predetermined degree of imbalance and patterns of imbalance have been evaluated against the perfectly balanced configuration, making it hard to form a general conclusion on these factors; (2) only a single objective has been set as the target, which neglects the fact that different patterns of imbalance may outperform with respect to different performance measures. Therefore, the aim of this study is to introduce a new approach to investigate the performance of unpaced production lines by using multiple-objective optimisation. It has been found by equipping multi-objective optimisation with an efficient, equality constraints handling technique, both the optimal pattern and degree of imbalance, as well as the optimal relationship among these factors and the performance measures of a production system can be sought and analysed with some single optimisation runs. The results have illustrated that some very interesting relationships among the key performance measures studied, including system throughput, work-in-process and average buffer level, could only be observed within a truly multi-objective optimisation context. While these results may not be generalised to apply to any production lines, the genericity of the proposed simulation-based approach is believed to be applicable to study any real-world, complex production lines.     

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。     

An exploratory study of disaggregated clearing functions for production systems with multiple products

Clearing functions (CFs) have shown considerable promise for representing production capacity in production planning models due to their ability to capture the non-linear relationships between throughput, order releases and lead times. Most CFs developed to date use the total work in progress of all products, in units of processing time, as the state variable. In this paper, we investigate CFs for multi-product systems where the overall throughput of the system is affected by the product mix. We show that the aggregate work in process (WIP) variable used in the previous CF literature may lead to inaccurate estimates of expected throughput for individual products. To address this issue, we explore the use of multi-dimensional CFs (MDCFs) that use an extended definition of resource state based on the disaggregated WIP levels for individual products. Several new functional forms for MDCFs are postulated for single machine multi-product systems and their ability to represent system behaviour is assessed using simulation experiments. Results reveal that MDCFs are better able to predict system performance in the presence of mix-dependent capacity losses. We also discuss the extension of the MDCF approach to multi-stage production systems.     

Optimal threshold control for failure-prone tandem production systems

We consider the flow dynamics of a tandem production system formed by two failure-prone machines separated by a buffer stock. The production rates of the machines are regulated by a feedback mechanism which solves an associated optimal control problem with an average cost criterion. The cost structure penalizes both the entrance into and the sojourn on the buffer boundaries. The generic structure of the optimal control involves four buffer content thresholds. When the buffer content crosses these thresholds, the production rates are tuned to reduce the tendency to enter into the buffer boundaries. Using the fluid modelling framework, we obtain analytical results for the stationary buffer level distribution in the case where an operating machine can produce with, either a “nominal” or a “reduced” rate. In the stationary regime, the optimal positions of the buffer thresholds, the throughput and the average buffer content are presented.     

Computer simulation for quality and reliability engineering

Computer simulation has been used widely in many industries for many applications. A simulation model mimics a real world system, enabling an investigation of its operation. More recently simulation models have incorporated a visual display and interactive features to aid understanding and enhance the investigation. Computer simulation has many potential uses in quality and reliability engineering, for instance, modelling equipment failures, quality control strategies, maintenance requirements and operational logistics. A case study shows how simulation has been used to study the throughput, flexibility and robustness of a manufacturing plant design. Alternative simulation software packages are briefly discussed.     

Automatic Petri Net Simulation Model Generation for a Continuous Flow Transfer Line with Unreliable Machines

A production line is studied with several machines in series. The machines are separated with finite buffers. They can break down and have to be repaired. Production is assumed to be continuous, i.e. no individual products can be identified. Analytical models for the performance evaluation of such a system are scarce. Analytical results for the throughput can be obtained only under very restrictive assumptions. The performance study of longer transfer lines requires either approximation or simulation. A Petri net model is developed for an n‐machine ‐buffer system. The rules to design the Petri net transitions are given. The automatic generation of a discrete‐event simulation model from the Petri net model is explained. Finally, some potential applications of the Petri net are illustrated. These applications include the simulation of transfer lines with complex stochastic behaviour, the optimization of buffer size and allocation and the evaluation of approximation models for long transfer lines. Using the simulation model described in this paper, quality and reliability engineers can simulate the performance of a given continuous flow transfer line and evaluate the effect of performance‐increasing measures such as the insertion of extra buffer space or the use of more reliable machines. Copyright © 2004 John Wiley & Sons, Ltd.