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An integrated design methodology has been developed for a run-to-run PID controller and SPC monitoring for the purpose of process disturbance rejection. In the paper, the process disturbance is assumed to be an ARMA (1.1) process. A detailed procedure is developed to design a PID controller which minimizes process variability. The performance of the PID controller is also discussed. A joint monitoring of input and output, using Bonferroni's approach, is then designed for the controlled process. The ARL performance is studied. One major contribution of the paper is to develop a complete procedure and design plots, which serve as tools to conduct all the aforementioned tasks. An example is provided to illustrate the integrated design approach. [ABSTRACT FROM AUTHOR]

The automated guided vehicle (AGV) system is emerging as the dominant technology to maximize the flexibility of material handling, while increasing the overall productivity of manufacturing operations. This paper presents a new way of finding the shortest flow path for an AGV system on a specific routing structure. Ah optimal solution of the system is determined by using an approach based on the Hopfield neural network with the simulated annealing (SA) procedure. In other words, the proposed approach reduces the total cost of an AGV delivery path from one workstation to another on the shop floor. By changing the temperature of the two-stage SA, a solution can be found that avoids potential collisions between AGVs. Both the flow path and the potential collision, which are major problems in AGV systems, may be solved simultaneously by the proposed neural network approach. Other advantages offered by the proposed method are its simplicity compared with operations research (OR) methods and a decreased number of needed AGVs. The performance of the approach is also investigated. [ABSTRACT FROM AUTHOR]

This paper examines a single-stage production system that manufactures multiple products under deteriorating equipment conditions. The machine condition worsens with production, and improves with maintenance. The condition of the process can be in any one of several discrete states, and transitions from state to state follow a semi-Markov process. In many production environments, the quality or yield of output depends heavily on the condition of the production process. The problem considers the trade-offs between manufacturing products that have a higher profit, a longer processing time, and therefore, a higher deterioration probability versus products that have a smaller profit, shorter processing time with a lower process deterioration probability. The firm needs to determine the optimal production choice in each state in a way that maximizes the long-run expected average reward per unit time. The paper makes three sets of contributions. First, it introduces the concept of critical ratios for the firm's manufacturing decision at each state regarding whether to switch from one product to another. Second, through the use of critical ratios, the main result shows that the optimal production choice for each state can be determined independently of the actions taken in other states, despite the complex interconnections between the production decisions and state transitions. Third, the paper provides generalizations that illustrate the depth, scope and richness of the proposed solution technique by extending the model in the number of machine states, to settings where maintenance is performed in intermediate states, and to settings where transition probabilities are influenced by both mean and variance of processing times. [ABSTRACT FROM AUTHOR]

When building a multivariate SPC model, it is commonly assumed that there is only one operational mode in the baseline data. However, multiple operational modes may exist. It is important to know the number of modes in the data in order to construct an effective process control system. Each operational mode appears as a cluster in the baseline data. This paper proposes a method to identify the correct number of clusters in baseline data. None of the existing methods for finding the number of clusters has all three of the following critical features: (i) the proposed method can determine if there is only one cluster, the most common case in baseline data; (ii) it can identify clusters that are convex or non-convex; and (iii) it is insensitive to user-specified parameters. The paper includes a comparison of the existing and proposed methods on four datasets. The proposed method consistently gives the correct number of clusters whereas the existing methods are unable to do so. [ABSTRACT FROM AUTHOR]

Variation-source identification has received considerable attention from the manufacturing quality improvement community. One widely used method is based on a pattern matching procedure, which identifies process faults by comparing the fault symptom, which is the principal eigenvector of the covariance matrix of the quality measurement, with fault signatures. The presence of unstructured noise as well as the uncertainty due to sampling will cause the direction of the fault symptom to deviate from the corresponding fault signature. The influences of these two effects on pattern matching procedures have previously been studied separately, by assuming either the absence of unstructured noise or the availability of large samples. This paper developes a robust pattern matching procedure that considers both effects simultaneously. Using a machining process as an illustrative example, the paper demonstrates that previous pattern matching procedures can have a remarkably low identification capability when the assumptions are not strictly satisfied. By contrast, our proposed method is more robust, maintaining a good identification probability, and would be a preferable tool for root-cause identification in manufacturing quality improvement. [ABSTRACT FROM AUTHOR]

The impact of transfer batching (also referred to as lot splitting ) on the performance of flowshops has received widespread attention in the literature. Most papers have emphasized the usefulness of lot splitting in cutting down average flow times, as it enables the overlapping of operations at different stages of the flowshop. However, while most analytical papers have studied deterministic flowshops, an important downside of lot splitting has been overlooked; i.e., the occurrence of idle time between the processing of consecutive sublots belonging to the same process batch (referred to as gap times ). Gap times add no value to the product; they merely increase the process batch makespan at the different stages. In deterministic systems, these gap times may be avoided by balancing the processing rates of the different machines in the shop; in stochastic settings, however, they may occur even when the system is perfectly balanced, due to the inherent variability in the setup and processing times. Studying a two-stage flowshop with a single product type, this paper provides insight into the behavior of the gap times, and develops an approximation for the process batch makespan at the second stage in terms of the system characteristics and the lot splitting policy. [ABSTRACT FROM AUTHOR]

This paper deals with tolerance charting for a component with both angular and square shoulder features. First of all, a unified surface model is proposed for a component with both angular and square-shoulder-featured surfaces. Then, a two-dimensional tolerance chart is constructed and parameterized for the machining of the component. Also, two general formulae are derived to calculate the surface changes due to machining cuts. The recording of the surface changes during all machining cuts means that the reverse dimensional chains and the “solid” stock removals can be identified automatically. Finally, the forward dimensional chains are obtained by inverting the reverse dimensional chains. Both the reverse and forward dimensional chains developed in this paper are two-dimensional, with both axial and radial dimensions. The approach presented in this paper is very useful in the development of a computer-aided angular tolerance charting system. [ABSTRACT FROM AUTHOR]

In the distributed and horizontally integrated manufacturing environment found in agile manufacturing, there is a great demand for new product development methods that are capable of generating new customized assembly designs based on mature component designs that might be dispersed at geographically distributed partner sites. To cater for this demand, this paper addresses the methodology for complex assembly variant design in agile manufacturing. It consists in fundamental research in two parts: (i) assembly modeling; and (ii) assembly variant design methodology. This paper, the first of a two-part series, presents the assembly variant design system architecture and the assembly modeling methodology. First, a complementary assembly modeling concept is proposed with two kinds of assembly models, the hierarchical assembly model and the relational assembly model. The first explicitly captures the hierarchical and functional relationships between constituent components whereas the second explicitly captures the mating relationships at the form-feature-level. These models are complementary in the sense that each of them models only a specific aspect of assembly-related information but together they include the required assembly-related information. They are further specialized to accommodate the features of assembly variant design. As a result, two kinds of assembly models, the assembly variants model and the assembly mating graph are generated. These assembly models serve as the basis for assembly variant design which is discussed in the companion paper. [ABSTRACT FROM AUTHOR]

This paper outlines approaches for assessing and classifying manufacturing and service operations in terms of their suitability for use of cross-trained (flexible) workers. We refer to our overall framework as agile workforce evaluation. The primary contributions of this paper are: (i) a strategic assessment framework that structures the key mechanisms by which cross-training can support organizational strategy; (ii) a tactical framework that identifies key factors to guide the selection of an architecture and worker coordination policy for implementing workforce agility; (iii) a classification of workforce agility architectures; (iv) a survey of a broad range of archetypical classes of worker coordination policies; (v) a survey of the literature with an operational perspective on workforce agility; and (vi) identification of opportunities for research and development of architectures for specific production environments. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the Exponentially Weighted Moving Average (EWMA) process mean estimator and its application to process adjustment. A novel dynamic-tuning EWMA estimator is proposed that has the capability of adjusting die control parameter dynamically in response to the underlying process random shifts. The current run's process mean is estimated using the EWMA equation and the newly adjusted control parameter. It is shown that the proposed estimator is very easy to implement and effective under many disturbance situations. Both industrial field data and Monte Carlo simulations are used to validate its performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the unbounded version of scheduling jobs with release dates and deadlines on a batch processing machine. NP-completeness is established for the case where all jobs have agreeable processing times and deadlines. Polynomial time algorithms are presented for the following cases: agreeable release dates and deadlines: agreeable release dates and processing times: a fixed number of distinct release dates, distinct processing times or distinct deadlines. [ABSTRACT FROM AUTHOR]

In this paper, an information theoretic approach is applied to analyze the performance of a decentralized control system. The control system plays the role of a correcting device which decreases the uncertainties associated with state variables of a production line by applying an appropriate "correcting signal" for each deviation from the target. In particular, a distributed feedback control policy is considered to govern a transfer production line, which consists of machines and buffers and processes a single part type in response to a stochastic demand. It is shown how the uncertainty of the demand propagates dynamically into the production system, causing uncertainties associated with buffer levels and machine production rates. The paper proposes upper estimates for these uncertainties as functions of the demand variance, parameters of the distributed controllers and some physical properties of the production line. The bounds are based on dynamic entropy measures of the system state and the control variables. Some practical implications into the area of decentralized controller design are proposed, an information-economical analysis is pre --sented and a numerical study is performed. [ABSTRACT FROM AUTHOR]

This paper is a case study that describes a hybrid system integrating fuzzy logic, neural networks and algorithmic optimization for use in the ceramics industry. A prediction module estimates two quality metrics of slip cast pieces through the simultaneous execution of two neural networks. A process improvement algorithm optimizes controllable process settings using the neural network prediction module in the objective function. An expert system module contains a hierarchy of two fuzzy logic rule bases. The rule bases prescribe processing times customized to individual production lines given ambient conditions, mold characteristics and the neural network predictions. This paper demonstrates the applicability of newer computational techniques to a very traditional manufacturing process and the system has been implemented at a major US plant. [ABSTRACT FROM AUTHOR]

Faster product development and increased competition in retail industries is resulting in shorter and shorter product life-cycles. This phenomenon is making it more difficult for a firm to accurately estimate random demand of such products and to plan their one-time order quantity accordingly. In this paper, we develop a model to assess the multiple effects of coordinated -- i.e., joint -- stocking and prior-sale discount decisions: (i) on the reduction of demand uncertainty; (ii) on maximization of the expected profit; and (iii) on the probability of achieving or exceeding it. We develop the joint optimal decisions that maximize the expected profit and discuss a procedure for computing the probability that the realized value of the (random) profit will exceed its maximum expected value. We present qualitative results on the varying effects of joint decisions on increasing the expected profit and the probability of achieving or exceeding it. We also describe a detailed numerical study examining the effects of varying parameter values on the percentage increase in expected profit with joint decision making. The paper concludes with extensions of the model that can deal with more general situations. [ABSTRACT FROM AUTHOR]

Some of the published models for the economic design of control charts use the expected cost per unit of output as the objective function to be minimized. In these models, the computation of steady-state probabilities that the process is in each possible state does not take into account the effect of the corrective action that may follow a signal from the control chart. As a result, the expected cost per unit is overestimated and the selection of the chart parameters is not optimal. The purpose of this paper is to eliminate this inaccuracy by proposing the exact formulation and to estimate the magnitude of errors resulting from the inaccurate formulation of the objective function. By solving numerical examples of joint design of &Xsline; and R charts, it is shown that these errors are typically very large and consequently it is imperative to use the exact formulation, proposed in this paper, to avoid inefficient and costly control chart designs. Finally, an additional opportunity for further cost improvements in process control is identified and discussed. [ABSTRACT FROM AUTHOR]

In many manufacturing settings, an item may require repair at one stage before it is passed on to the next manufacturing operation. This paper examines a single-stage manufacturing system in which all items require a processing operation, they are then inspected, and a fraction (1-p) require rework. Two priority-based scheduling roles, motivated by the goal of minimizing the mean flow time in the repair system, are considered. The LST of the flow time PDF for a random arrival is developed for each priority rule. From the LST, the mean and variance of customer flow times can be obtained. Computational experience with the two rules is reported along with a discussion of the preferability of each rule when both flow time mean and variance are considered as performance measures. [ABSTRACT FROM AUTHOR]

We consider a model which deals with the fabrication of products at a single facility for later assembly into end products. Each product requires several part types: one unique part and others that are common to all the products. Also each production batch requires a setup. Baker provides a dynamic programming algorithm to determine a production schedule for minimizing the mean completion time for the case where each product requires only two part types: one unique and the other which is common to all products. In this paper we extend his analysis to our more general case. [ABSTRACT FROM AUTHOR]

This paper deals with an optimal part selection problem to minimize the expected cost, in an automated manufacturing system in which a flexible manufacturing cell produces different parts for several production lines. The optimal control problem is formulated as an undiscounted semi-Markov decision process. Properties of the optimal policy are analyzed. Moreover, sufficiency conditions are derived for the optimal policy to be of control limit type. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology for functional capability analysis and optimal process adjustment for products with failures that occur when design parameters and process variables are within tolerance limits (in-specs). The proposed methodology defines a multivariate functional capability space (FC-Space) using a mathematical morphology operation, the Minkowski sum, in order to represent a unified model with (i) multidimensional design tolerance space; (ii) in-specs failure region(s); and, (iii) non-parametric, multivariate process measurements represented as Kernel Density Estimates (KDEs). The defined FC-Space allows the determination of a desired process fallout rate in the case of products with field failures that occur within design tolerances (in-specs). The outlined process adjustment approach identifies the optimum position of the process mean in order to minimize the overlap between the KDEs and in-specs failure regions, i.e., achieve the minimum possible process fallout rate for current process variation. The FC-Space-based process adjustment methodology is illustrated using a case study from the electronics industry where the in-specs failure region is identified based on warranty information analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the reliability of coordinate sensor systems used for process fault diagnosis in multistage manufacturing processes. When considering catastrophic sensor failure, the reliability of a coordinate sensor system is defined based on its diagnosability performance. A mathematical tool called matroid theory is applied to study the reliability of the coordinate sensor system; properties of the minimal paths and minimal cuts are derived; efficient methods are developed to evaluate the exact system reliability for two special types of systems; and an efficient algorithm to evaluate the min-max lower bound of system reliability is provided that does not require assuming that all minimal paths must be known in advance. The proposed models and the developed methods are illustrated and applied in two case studies for fault diagnosis of a multistage panel assembly process. [ABSTRACT FROM AUTHOR]

This paper proposes a W control chart that is able to detect sensor mean shift faults and distinguish them from potential process faults in discrete-part manufacturing processes. The control chart is set up based on a linear fault quality model. The sensitivity of the W chart to the occurrence of sensor faults is studied. An index called the sensitivity ratio is used to investigate the effects of sensor fault locations and the sensor layout on the sensitivity of the W chart to sensor faults. In comparison with traditional control charts, which directly monitor the product quality characteristics, the proposed W chart can effectively separate sensor faults from process faults. An automotive body assembly process is used as an example to demonstrate the performance of the W chart. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix] [ABSTRACT FROM AUTHOR]

In dealing with the effects of product deterioration in the context of reliability analysis, it may not be satisfactory to consider only the effects of time or age because usage is often another essential factor that accounts for deterioration. A two-dimensional warranty with consideration of both time and usage for deteriorating products would be more advantageous for manufacturers. In this paper, a two-dimensional warranty model in which the customer is expected to perform appropriate preventive maintenance is analyzed and the warranty policy that maximizes the manufacturers' profits is determined. The proposed approach provides manufacturers with guidelines on how to offer customers two-dimensional warranty programs with proper time and usage limits. A numerical example shows the effectiveness of the proposed approach. Sensitivity analyses are conducted to investigate the robustness of the derived optimal warranty policy. [ABSTRACT FROM AUTHOR]

Setup planning is a set of activities used to arrange manufacturing features into an appropriate sequence for processing. It has significant impact on the product quality, which is often measured in terms of dimensional variation in key product characteristics. Current approaches to setup planning are experience-based and tend to be conservative due to the selection of unnecessarily precise machines and fixtures to ensure final product quality. This is especially true in multi-stage machining processes (MMPs) since it is difficult to predict variation propagation and its impact on the quality of the final product. In this paper, a methodology is proposed to realize cost-effective, quality-assured setup planning for MMPs. Setup planning is formulated as an optimization problem based on quantitative evaluation of variation propagations. The optimal setup plan minimizes the cost related to process precision and satisfies the quality specifications. The proposed approach can significantly improve the effectiveness as well as the efficiency of the setup planning for MMPs. [ABSTRACT FROM AUTHOR]

This paper considers Statistical Process Control (SPC) when the process measurement is multivariate. In the literature, most existing multivariate SPC procedures assume that the in-control distribution of the multivariate process measurement is known and it is a Gaussian distribution. In applications, however, the measurement distribution is usually unknown and it needs to be estimated from data. Furthermore, multivariate measurements often do not follow a Gaussian distribution (e.g., cases when some measurement components are discrete). We demonstrate that results from conventional multivariate SPC procedures are usually unreliable when the data are non-Gaussian. Existing statistical tools for describing multivariate non-Gaussian data, or transforming the multivariate non-Gaussian data to multivariate Gaussian data, are limited, making appropriate multivariate SPC difficult in such cases. In this paper, we suggest a methodology for estimating the in-control multivariate measurement distribution when a set of in-control data is available, which is based on log-linear modeling and which takes into account the association structure among the measurement components. Based on this estimated in-control distribution, a multivariate CUSUM procedure for detecting shifts in the location parameter vector of the measurement distribution is also suggested for Phase II SPC. This procedure does not depend on the Gaussian distribution assumption; thus, it is appropriate to use for most multivariate SPC problems. [ABSTRACT FROM AUTHOR]

Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condition of the equipment. [ABSTRACT FROM AUTHOR]

This paper presents approximation methods for the performance analysis of long manufacturing lines, i.e. lines with more than two machines and one buffer, that have both quality and operational failures. We describe three different versions of long flow lines that differ in the locations of the inspection stations and in the sets of machines that each inspection station monitors. We explain a transformation method that approximates long manufacturing lines that have quality and operational failures with long lines that only have operational failures. Such lines can be evaluated by decomposition methods. We introduce other approximations to quantify the effects of the separation of inspections from operations. Comparison with simulation shows that the solution methods provide reliable performance estimates. [ABSTRACT FROM AUTHOR]

Statistical Process Control (SPC) techniques have been successfully used in manufacturing industries to trigger and identify the root cause of variations so as to promote quality improvement. This paper develops a SPC framework to identify important changes deserved in business activity monitoring. To model and track thousands of diversified customer behaviors, the proposed SPC system consists of efficient and robust profiling methods to accommodate different behavior patterns including business changes, structural breakdowns, and unnecessary errors. Several customer profiling techniques are discussed and the activity monitoring performance based on the profiling algorithms is compared in a simulation example and a customer churn detection example in a telecommunications setting. The enhanced system will allow business managers and engineers to establish successful customer loyalty programs for churn prevention and fraud detection. [ABSTRACT FROM AUTHOR]

Variation-source identification in manufacturing processes is highly desired since it enables improvements in product quality. Recently, data-driven variation-source identification has received considerable attention. This paper presents a systematic variation-source identification method by assuming a linear model between the quality measurements and process faults. The noise term in the model is assumed to have a simple form. The variation-source identification is achieved through the testing of the common eigenspace between the fault signatures and the covariance matrix of the newly collected samples. Three types of fault signatures are constructed from either one or two covariance matrices for pattern matching. A systematic procedure to construct the signature is presented. A case study of a machining operation is conducted to illustrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Model selection and validation are critical in predicting the performance of manufacturing processes. The correct selection of variables minimizes the model mismatch error whereas the selection of suitable models reduces the model estimation error. Models are validated to minimize the model prediction error. In this paper, the relevant literature is reviewed and a procedure is proposed for the selection and cross-validation of predictive regression analysis and neural network models. Specifications on surface roughness and tolerances impact on manufacturing process plans, and differentiate product quality, and ultimately the product cost and lead times. Experimental data from a turning surface roughness study is used to demonstrate the developed concepts with regression and neural network techniques being used for the purpose of predictive rather than descriptive modeling. [ABSTRACT FROM AUTHOR]

Cycle-based signals are generally obtained through the automatic sensing of critical process variables during each repetitive operation cycle of a manufacturing process, and they thus contain a significant amount of information about the process condition. Increasing attention has been paid recently to the problem of effectively monitoring these signals as an aid to the detection of process changes. In general, either based on process engineering knowledge or on historical data analysis, it is possible to obtain process faults and the corresponding signal patterns (the direction and magnitude of a mean shift). In order to fully utilize such fault pattern information in process monitoring, this paper proposes a directionally variant control chart obtained through the effective combination of a multivariate χ 2 chart and a univariate projection chart. It is shown that the addition of the univariate projection chart can improve the detection power for pre-known process faults, however, this may be at the cost of a deterioration in the detection power for unknown faults. A detailed quantitative analysis is provided to justify the application conditions of the proposed chart. A case study of cycle-based tonnage monitoring of a forging process is presented to illustrate the design procedures and the effectiveness of the proposed control chart system. [ABSTRACT FROM AUTHOR]

In-process optical coordinate measuring machines offer the potential to diagnose the sources of the variations that are responsible for product quality defects. Such a sensor system can thus help manufacturers to improve product quality and reduce process downtime. The effective use of sensor data in the diagnosis of the sources of variations depends on the optimal design of the sensor system, which is often also called the problem of sensor placement. This paper addresses coordinate sensor placement for the diagnosis of dimensional variation sources in assembly processes. Sensitivity indices for the detection of the process mean and variance components are defined as the design criteria and are derived in terms of process layout and sensor deployment information. Exchange algorithms, originally developed for optimal experimental design, are revised and then used to maximize the detection sensitivity. A sort-and-cut procedure is proposed, which is able to significantly improve the algorithm efficiency of the current exchange routine. The resulting optimal sensor layout and its implications are illustrated in the specific context of a panel assembly process. [ABSTRACT FROM AUTHOR]

Recent developments in sensing and computer technology have resulted in most manufacturing processes becoming a data-rich environment. A cycle-based signal refers to an analog or digital signal that is obtained during each repetition of an operation cycle in a manufacturing process. It is a very important class of in-process sensing signals for manufacturing processes because it contains extensive information on the process condition and product quality (e.g., the forming force signal in forging processes). In contrast with currently available supervised classification approaches that heavily depend on the training dataset or engineering field knowledge, this paper aims to develop an automatic feature selection method for the unsupervised clustering of cycle-base signals. First, principal component analysis is applied to the raw signals. Then a new method is proposed to select information containing principal components to allow clustering to be performed. The dimension of the problem can be significantly reduced through the use of these two steps. Finally, a model-based clustering method is applied to the selected principal components to find the clusters in the cycle-based signals. A numerical example and a real-world example of a forging process are used to illustrate the effectiveness of the proposed method. The proposed technique is an important data pre-processing technique for the monitoring and diagnostic system development using cycle-based signals for manufacturing processes. [ABSTRACT FROM AUTHOR]

This paper considers a single-machine scheduling problem where the decision authorities and information are distributed in multiple subproduction systems. Subproduction systems share the single machine and must cooperate with one another to achieve a global goal of minimizing a linear function of the completion times of the jobs; e.g., total weighted completion times. It is assumed that neither the subproduction systems nor the shared machine have complete information about the entire system. The associated scheduling problems are formulated as zero-one integer programs. The solution approach is based on Lagrangian relaxation techniques modified to require less global information. Specifically, there is no need for a global upper bound, or a single master problem that has a complete view of all the coupling constraints. The proposed methodology exhibits a promising performance when experimentally compared to the Lagrangian relaxation with a subgradient method with the added benefit that can be applied to situations with more restrictive information sharing. [ABSTRACT FROM AUTHOR]

The paper describes an optimization model for allocating solder-paste printing inspection that explicitly considers the economic tradeoff between board yield and inspection accuracy. The paper also shows that the use of a heuristic solution method for solving the post-printing inspection allocation model is effective and efficient for high-volume electronics manufacturing. The model has been developed using real production and visual inspection data provided by a high-volume electronics manufacturer located in Huntsville, AL. In addition, randomly generated problems are used to evaluate the performance of the proposed heuristic. Results from a large case study show that when compared with a full post-printing inspection, the heuristic approach provides a solution that can increase the total expected gains by 15%. [ABSTRACT FROM AUTHOR]

This paper presents an overlapping decomposition method to approximate the throughput of production systems with rework loops. The idea is to decompose the system into overlapped serial production lines, with the overlapping machines modified to accommodate the interactions with machines and buffers in other lines. The convergence of the iterative procedure and the uniqueness of the solution are proved analytically. The accuracy of the estimate is justified numerically and illustrated by a case study at an automotive assembly plant. [ABSTRACT FROM AUTHOR]

Coordinate measurement systems (CMSs) dominate the dimensional control and diagnostics of various manufacturing processes. However, CMSs have inherent errors caused by the lack of a tracing ability for some of the measured part features. This is important for product inspection and process variation reduction in a number of automated manufacturing systems, such as for example the automotive body assembly process. The lack of a feature tracing ability means that instead of measuring a given feature, the CMS may actually measure the area around the selected feature. In this paper, a principle for the part feature tracing ability and the resultant feature-based measurement error analysis are developed to estimate the aforementioned deficiencies in the CMSs. The impact of feature type and part(s) positional variation on the feature-based measurement error is explored. The proposed approach is applicable to both contact and non-contact CMSs including both mechanical and optical coordinate measuring machines. An analysis of the error for different measurement algorithms is presented. We show that the developed feature-based measurement error can have a significant impact on the measurement accuracy and hence on process control and the diagnostic algorithms currently used in manufacturing. A feature-based error map and error compensation approach are also developed and presented. Simulations, experimental results and two industrial case studies illustrate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simplified real-life identical parallel machine scheduling problem with sequence-dependent setup times and job splitting to minimize makespan. We propose a heuristic to solve this problem. Our method is composed of two parts. The problem is first reduced into a single machine scheduling problem with sequence-dependent setup times. This reduced problem can be transformed into a Traveling Salesman Problem (TSP), which can be efficiently solved using Little's method. In the second part, a feasible initial solution to the original problem is obtained by exploiting the results of the first part. This initial solution is then improved in a step by step manner, taking into account the setup times and job splitting. We develop a lower bound and evaluate the performances of our heuristic on a large number of randomly generated instances. The solution given by our heuristic is less than 4.88% from the lower bound. [ABSTRACT FROM AUTHOR]

In this paper, a structure and architecture for the rapid realization of a simulation-based real-time shop floor control system for a discrete part manufacturing system is presented. The research focuses on automatic simulation model and execution system generation from a production resource model. An Automatic Execution Model Generator (AEMG) has been designed and implemented for generating a Message-based Part State Graph (MPSG)-based shop level execution model. An Automatic Simulation Model Generator (ASMG) has been designed and implemented for generating an Arena simulation model based on a resource model (MS Access 97) and an MPSG-based shop level execution model. A commercial finite capacity scheduler. Tempo, has been used to provide schedule information for the Arena simulation model. This research has been implemented and tested for six manufacturing systems, including The Pennsylvania State University CIM laboratory. [ABSTRACT FROM AUTHOR]

The optimal allocation of buffers in production lines is an important research issue in the design of a manufacturing system. We present a new hybrid algorithm for this complex design problem: the hybrid Nested Partitions (NP) and Tabu Search (TS) method. The Nested Partitions method is globally convergent and can utilize many of the existing heuristic methods to speed up its convergence. In this paper, we incorporate the Tabu Search heuristic into the NP framework and demonstrate through numerical examples that using the hybrid method results in superior solutions. Our numerical results illustrate that the new algorithm is very efficient for buffer allocation problems in large production lines. [ABSTRACT FROM AUTHOR]

The Model Predictivc Control (MPC) method has been widely adopted as a useful tool to keep quality on target in manufacturing processes. However, the conventional MPC methods are inadequate for large-scale manufacturing processes particularly in the presence of disturbances. The goal of this paper is to propose a Partial Least Square (PLS)-based MPC methodology to accommodate the characteristics of a large-scale manufacturing process. The detailed objectives are: (i) to identify a reliable prediction model that handles the large-scale "short and fat" data; (ii) to design an effective control model that both maximizes the required quality and minimizes the labor costs associated with changing the process parameters: and (iii) to develop an efficient optimization algorithm that reduces the computational burden of the large-scale optimization. The case study and experimental results demonstrate that the presented MPC methodology provides the set of optimal process parameters for quality improvement. In particular, the quality deviations are reduced by 99.4%. the labor costs by 84.2%, and the computational time by 98.8%. As a result, the proposed MPC method will save on both costs and time in achieving the desired quality for a large-scale manufacturing process. [ABSTRACT FROM AUTHOR]

Many manufacturing processes have, by nature. Multiple Input and Multiple Output (MIMO) variables. However, despite its frequent use in practice. MIMO process feedback control has not been fully investigated in the literature Under a linear MIMO model, this paper proposes a Multivariate Exponentially Weighted Moving Average (MEWMA) controller. The stability conditions of this controller and the feasible region of its associated discount factor are derived. A discount factor which minimizes the total Mean Square Error (MSE) of the process output is obtained under the assumption that the process disturbance is either a white noise series or an IMA(1,1) series. An example is given to demonstrate the applicability of the proposed method. Some related issues are also discussed. [ABSTRACT FROM AUTHOR]

With he fast development of the world economy, distributed virtual manufacturing is becoming increasingly important since it can respond rapidly to market changes arid make resource sharing more efficient among manufacturing partners. In this environment, partners may be located at different geographical locations, and co-operation among partners is a vital task and thus concurrent planning and scheduling has become a challenging research topic. This paper presents a computerized model that can integrate these manufacturing functions and resolve some of the critical problems in distributed virtual manufacturing, such as virtual cooperation, optimal partner selection, etc. This integration model is realized through a multi-agent approach that provides a practical approach for software integration in a distributed environment. A cost function is proposed and adopted for optimal partner selection in a virtual enterprise, which not only considers a partner's manufacturing capability and process requirements, but also the processing time, partner's location and product due date. Through establishing a virtual manufacturing model in a simulated environment, the proposal was validated with a case study. It shows that the proposed methodology can satisfy the distribution and agility requirements. This approach is able to contribute to the reduction of product cost, improving product quality and shortening lead time, compared to the sequential approach in the normal engineering and production practice. [ABSTRACT FROM AUTHOR]

This paper presents an analytical method for evaluating the performance of production lines with a finite buffer and two unreliable machines. Unlike in earlier papers, each machine can fail in more than one way. For each failure mode, geometrically distributed times to failure and times to repair are specified. The method evaluates the steady-state probabilities of the slates of the system with a computational effort that depends only on the number of failure modes considered and not on the capacity of the buffer. A comparison of performance of the method with those obtained with existing techniques that consider only one failure mode is reported. [ABSTRACT FROM AUTHOR]

This paper studies the effect of the number of component stations (parallelism), work transfer, processing lime distributions, buffers and buffer allocation schemes on throughput and interdeparture time variability of assembly systems. As an alternative to work transfer, variability transfer is introduced and its effectiveness is assessed. Previous research has indicated that the optimal throughput displays an anomaly at certain processing time distributions and, this phenomenon is now thoroughly analyzed and the underlying details are uncovered. This study also yields several new findings that convey important practical implications. [ABSTRACT FROM AUTHOR]

We present a general manufacturing-cell simulation model for evaluating the effects of world-class manufacturing practices on expected cell performance. The modular structure of the simulation provides the flexibility to analyze a wide variety of manufacturing cells. We formulate a comprehensive annualized cost function for evaluation and comparison of alternative cell configurations. A case study involving assembly of printed circuit hoards illustrates the potential benefits of using this tool for cell design and analysis. The simulation model is intended for use in a two-phase approach to cell design that is based on simulated experimentation and response surface analysis as detailed in a companion paper. [ABSTRACT FROM AUTHOR]

We present a two-phase approach to design and analysis of manufacturing cells based on simulated experimentation and response surface methodology using a general manufacturing-cell simulation model. The first phase involves factor-screening simulation experiments to identity design and operational factors that have a significant effect on cell performance as measured by a comprehensive annual cost function. In the second phase of experimentation, we construct simulation (response surface) metamodels to describe the relationship between the significant cell design and operational factors ( the controllable input parameters) and the resulting simulation-based estimate of expected annual cell cost (the output response). We use canonical and ridge analyses of the estimated response surface to estimate the levels of the quantitative input factors that minimize the cell's expected annual cost. We apply this methodology to an assembly cell for printed circuit hoards. Compared to be current cell operating policy. the simulation metamodel-based estimate of the optimum operating policy is predicted to yield average annual savings of approximately $425 000, which is a 20% reduction in annual cost. In a companion paper, we detail the structure and operation of the manufacturing-cell simulation model. [ABSTRACT FROM AUTHOR]

Manufacturing-related have traditionally made independence assumptions on associated stochastic processes in order to achieve tractability of analytical models and simplify Monte Carlo models. This paper aims to alert users to potential deleterious implications stemming from unfounded independence assumptions in traditional stochastic models of manufacturing systems. Specifically, it demonstrates the dramatic impact that appreciable autocorrelations can have on manufacturing performance measures through a preliminary study of prediction errors incurred in ignoring dependence. To this end, the study compared performance measures of common manufacturing models with renewal components to their autocorrelated counterparts, drawn From the TES (Transform-Expand-sample) class. TES models constitute a versatile class of stochastic processes, designed to capture empirical distributions and autocorrelation. simultaneously, and as such, are suitable for both Monte Carlo simulation and analytical modeling of autocorrelated time series. A brief overview of simple TES processes and their generation algorithms is also included. [ABSTRACT FROM AUTHOR]

Research studies on multi-agent systems have been recently boosted by manufacturing and logistics with deep motivations like the presence of independent human deciders with individual goals, the aspiration to dominate the complexity of decision-making in large organizations, the simplicity and robustness of self-reacting distributed systems. After a survey of the multi-agent paradigm and its applications, the paper introduces the notion of hybrid holonic system to study the effect of supervision on a system whose elements negotiate and cooperate in a rule-settled environment to obtain resources for system operation. The supervisor can spur or disincentive agents by assigning/denying resources to them. A simple single-decider optimization model referred to a real application is described, and solution methodologies for optimal resource allocation fitting different scenarios (centralized, distributed, multi-agent) are discussed, identifying ranges of autonomy, quantifying rewarding and defining a negotiation protocol between the agents and the supervisor. Aim of the paper is to describe through an example a general methodology for quantitative decision-making in multi-agent organizations. [ABSTRACT FROM AUTHOR]

This paper investigates possible implementations of the autonomous agents concept in flexible manufacturing control. The implementation issues and the effectiveness of different control architectures and algorithms are analyzed by means of a simulation model of a flexible job shop. Extensive experimental results are reported, allowing the evaluation of the trade-off between the degree of autonomy and system performance. [ABSTRACT FROM AUTHOR]

This paper synthesizes recent work by the author and his colleagues on the performance analysis of manufacturing systems and on real-time scheduling methods. It describes a decentralized scheduling/control policy and proposes a method for analyzing the performance of systems that are operated according to the policy. It presents three views of the production system: surplus-based, time-based, and token-based; and shows how they are equivalent in special cases. While there are no assurances of optimality, experience suggests that the policy has desirable characteristics. The paper also suggests how the physical resources and the control policy can be designed together. [ABSTRACT FROM AUTHOR]