A methodology for developing a web-based factory simulator for manufacturing education

Historically, manufacturing engineering education has focused on teaching mathematical models using simplifying assumptions that can mask the realities of complex manufacturing systems. Recent pedagogical approaches to manufacturing education have focused on developing a more holistic view of the manufacturing enterprise. In this paper, we describe the contents and development methodology of a Virtual Factory Teaching System (VFTS) whose aim is to provide a workspace that illustrates the concepts of factory management and design for complex manufacturing systems. The VFTS is unique in its integration of four domains: web-based simulations, engineering education, the Internet, and virtual factories. Evolutionary development of the VFTS is accomplished by separating the simulation model from the graphical interface and user interaction.     

Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system

A methodology is presented for the dynamic scheduling of flexible manufacturing systems (FMSs). A two-level control hierarchy is suggested. The higher level is used for determining a dominant decision criterion and relevant scheduling rules, based on an analysis of the actual shop status. The lower level uses simulation for determining the best scheduling policy to be selected. Simulation is used to evaluate different control options, and once a control decision is made, it is operated in real time to serve as the FMS controller. The suggested scheduling and control scheme is being developed, implemented and tested in a physical computer integrated manufacturing (CIM)/FMS environment at the CIM and Robotics Lab of the Faculty of Industrial Engineering and Management, Technion. This will serve as a test-bed to study the performance of the FMS under different scheduling rules and control options, and to recommend the best combination of control policies and parameters for specific system conditions and global production objectives.     

Two strategies for fitting real data to Rasch polytomous models

A comparative study of the results provided by two strategies for fitting data to Latent Trait Theory Models has been performed. The first, called Total-Persons-Items (TPI), is structured in three phases: 1) assessment of item fit, 2) assessment of person fit; and finally, 3) overall fit of data to the models (items and persons). The second strategy, the Total-Items-Persons (TIP), changes the order of the phases: 1) assessment of person fit, 2) assessment of item fit and, 3) overall fit of data to the models. To verify the results of these two strategies, a set of 30 items, designed to measure religious attitude, was administered to a sample of 821 persons. The Latent Trait Theory Models used were the Partial Credit Model and the Rating Scale Model. The results underline an important difference between the two procedures: the TPI maximizes the number of persons with good fit and the TIP maximizes the number of items with good fit. Moreover, a procedure for controlling the sensitivity of fit to sample size is proposed.     

Product complexity and design of inspection strategies for assembly manufacturing processes

In the manufacturing field, the assembly process heavily affects product final quality and cost. Specific studies, concerning the causes of the assembly defects, showed that operator errors account for high percentage of the total defects. Also, models linking the assembly complexity with the operator-induced defect rate were developed. Basing on these models, the present paper proposes a new paradigm for designing inspection strategies in case of short-run productions, for which traditional approaches may not be carried out. Specifically, defect generation models are developed to get a priori predictions of the probability of occurrence of defects, which are useful for designing effective inspection procedures. The proposed methodology is applied to a case study concerning the assembly of mechanical components in the manufacturing of hardness testing machines.     

A comparison of tool management strategies and part selection rules for a flexible manufacturing system

An important element in the successful operation of flexible manufacturing systems (FMS) is the management of the tooling component. This paper reports on one aspect of tool management for FMS operations. Four tool allocation and scheduling strategies are compared in the presence of three part selection rules through a simulation study of a five-machine FMS with an automated tool handling system. The tool allocation strategies are similar to those used in industry while the part selection rules are synthesized from the literature on FMS scheduling under tooling constraints. The use of different tooling strategies produces significantly different outcomes in FMS performance.     

Optimal core acquisition and pricing strategies for hybrid manufacturing and remanufacturing systems

In this study, we combine two aspects of remanufacturing, namely product acquisition management and marketing (pricing) of the remanufactured products. We consider an original equipment manufacturer (OEM) who decides on the acquisition prices offered for returns from different quality types and on selling prices of new and remanufactured products, in a single period setting. We develop a procedure for determining the optimal prices and corresponding profit of the OEM, and conduct a sensitivity analysis to understand the effect of different model parameters on the optimal strategies and profit. An important managerial insight is that the optimal solution is not to have the same profit per remanufactured item for all return types, but to if the minimum cost for acquisition and remanufacturing of some core type is lower.     

Capacity-driven acceptance of customer orders for a multi-stage batch manufacturing system: models and algorithms

An automotive parts manufacturer produces a wide variety of parts in a job shop environment. Many of the manufacturing operations have substantial setups. When a client phones in an order, the manufacturer must decide quickly whether or not it has the capacity required to accept the order. We develop a simplified formulation of the order acceptance problem. We formulate the discrete-time version as an integer program. The problem is NP-hard, but in 51 out of 51 test problems the LP relaxation is tight. For larger problems we test several heuristics. Three of the heuristics look promising: simulated annealing, a genetic algorithm, and a linear-programming-based heuristic.     

A framework to engineer production control strategies and its application in electronics manufacturing

One of the most challenging tasks of companies with complex and discrete production systems is to determine an appropriate production control strategy (PCS). The PCS is crucial, as it influences inventory, costs, and service. We propose a framework to address the most important questions of PCS engineering: limiting work in process, positioning the order penetration point (OPP), and coping with demand uncertainty upstream the OPP. Thereby, uncertain advance demand information in the form of forecasts is considered and desirable characteristics of push and pull strategies are combined. A serial manufacturing system with multiple products, variable processing times, and uncertain advance demand information is examined. The model is formulated as a queuing network model and solved numerically by discrete-event simulation. The framework and a resulting hybrid control strategy are applied to a case study from electronics manufacturing, where a significant improvement potential is identified.     

Developing and marketing patent information services to small and medium enterprises (SMEs) in Birmingham, UK

The Patent Information Service of Birmingham Central Library was re-assessed in the light of changing customer requirements and new opportunities to better meet those needs. A substantial funding injection enabled research to be carried out and the results put into practice, e.g., in expanding the range of services – with particular emphasis on helping small and medium enterprise (SME) businesses, and in positive marketing of the services, rather than the previous passive provision of services. The resulting integrated business and intellectual property service was then re-branded as ‘Business Insight' and given wide promotion in the Birmingham area.     

Petri net models of flexible and automated manufacturing systems: a survey

Petri nets (PNs) have recently emerged as a promising approach for modelling flexible and automated manufacturing systems. PNs are a graphical and mathematical modelling technique that is useful for modelling concurrent, asynchronous, distributed, parallel, nondeterministic, and stochastic systems, as attested by a steady stream of papers which appeared throughout the 1980s. Since 1989, there has been an explosion of interest in using PNs to model, simulate, and analyse manufacturing systems. We present an overview of PN theory. We then present a schema for categorizing PN models of manufacturing systems, followed by a survey of PN models of flow lines, automatic transfer lines, job shops, flexible manufacturing systems, and assembly systems. This discussion represents the most comprehensive survey of applications of PNs to manufacturing through 1994. Finally, we present a summary and some conclusions.     

Support vector machines for recognizing shifts in correlated and other manufacturing processes

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries where the data from the facilities are often autocorrelated. This is often true in piece-part manufacturing industries that are highly automated and integrated. Several attempts have been made in the literature to extend traditional SPC techniques to deal with autocorrelated parameters. However, these extensions pose several serious limitations. The literature discusses several machine-learning methods based on radial basis function (RBF) networks and multi-layer perceptron (MLP) networks to address the limitations, with some success. This paper demonstrates that support vector machines (SVMs) can be extremely effective in minimizing both Type-I errors (probability that the method would wrongly declare the process to be out of control or generate a false alarm) and Type-II errors (probability that the method will be unable to detect a true shift or trend present in the process) in these autocorrelated processes. Even while employing the simplest type of polynomial kernels, the SVMs were extremely good at detecting shifts in papermaking and viscosity datasets (available in the literature) and performed as well or better than traditional as well as machine learning methods. It was also observed that SVMs are good at minimizing both Type-I and Type-II errors even in monitoring non-correlated processes. When tested on datasets available in the literature, they once again performed as well or better than the classical Shewhart control charts and other machine learning methods.     

Risk Influence Analysis A methodology for identification and assessment of risk reduction strategies

This article describes Risk Influence Analysis, a methodology for the identification and assessment of risk reduction strategies in large-scale distributed systems such as transportation systems. The approach focuses on the identification of risk-influencing factors, i.e. sets of relatively stable conditions influencing the risk level. Risk reduction strategies are defined by sets of actions designed to change the state of risk-influencing factors. The expected effects of risk reduction strategies are evaluated by expert judgement supported by available data for the analysis object.Quality issues concerning the relevance and trustworthiness of the Risk Influence Analysis methodology are identified. The most important threats to the trustworthiness of the analysis are believed to be: (1) inadequate validity of expert judgements in estimating the effects of risk reduction strategies; and (2) incompleteness of the qualitative analysis results.     

Quality improvement of chemical-mechanical wafer planarization process in semiconductor manufacturing using a combined generalized linear modelling - non-linear programming approach

This paper is aimed at how to develop and utilize a specialized response surface method, combined with state-of-the-art mathematical programming techniques, for quality improvements of the chemical-mechanical planarization (CMP) process in semiconductor manufacturing. CMP is one of the fastest growing technologies that enables to polish the topography of interlayer dielectrics (ILDs) and to obtain a high degree of global planarity due to increasingly stringent requirements of photolithography between process steps. A wafer held on a carrier is rotated against a polishing pad in the presence of a silica-based alkaline slurry while applying a down-force onto it. Two major challenging works posed by CMP involve maintaining stable removal rate with polishing time and achieving acceptable within-wafer non-uniformity (WIWNU) over an entire die. In this research, to robustly characterize and therefore optimize such a still unclear and fully complex process, the response surface methodology (RSM) as an external modelling technique and non-linear programming (NLP) approaches as an optimum-seeking procedure are proposed to the bicriteria situation. An example with real CMP data is rigorously investigated, revealing that not only does the proposed method flexibly and appropriately portray CMP, but also helps locate the optimal parameter settings that attain better polishing quality.     

Using root cause analysis to address patient satisfaction and other improvement opportunities

BACKGROUND: Despite the considerable attention that health care organizations are devoting to the measurement of patient satisfaction, there is often confusion about how to systematically use these data to improve an organization's performance. A model to use in applying traditional quality improvement methods and tools to patient satisfaction problems includes five primary steps: (1) identifying opportunities, (2) prioritizing opportunities, (3) conducting root cause analysis, (4) designing and testing potential solutions, and (5) implementing the proposed solution. PATIENT SATISFACTION SURVEYS: A satisfaction survey serves best as a high-level screening device, not as a tool to provide highly detailed information about the root causes of patient dissatisfaction. The primary purpose of the survey in the model is to identify improvement opportunities and areas of significant improvement or deterioration. Secondary tools such as brief patient interviews or focus groups may better serve to probe intensively into the problem areas identified by the survey. These tools allow for a direct dialog with the patient to uncover root causes of dissatisfaction and establish potential solutions. DISCUSSION: Although the primary focus of this model has been patient satisfaction issues, the basic steps could easily be applied to virtually any improvement opportunity. Improvement teams should commit to a schedule of 90-minute weekly meetings for 7 weeks. The model, a simple translation of traditional improvement methods and tools to address the unique issues facing patient satisfaction improvement teams, can save improvement teams considerable time, resources, and frustration as they design and launch initiatives to improve patient satisfaction.     

Approaches to chemical alternatives assessment (CAA) for the substitution of hazardous substances in small- and medium-sized enterprises (SMEs)

Six alternatives assessment frameworks have been reviewed for specific features that might affect the implementation of alternatives assessment and cause regrettable substitution. These features are: the assessments included, the assessment flowchart structure, the inclusion of the assessors with limited resources (e.g., SMEs) in terms of resource intensity, the tools and methods included or guided to, and the indicators. The purpose of this review was to point out the existing important differences among the frameworks and also to stress the possibility of future improvements for the application of frameworks in SMEs. In general, it has been determined that, although there are similar features (e.g., hazard assessment methods) among the reviewed frameworks, there are also serious differences that might affect the assessment outcome, such as the use of physicochemical properties, the scope of life cycle thinking, and decision methods. These differences are caused by the exclusion of particular assessments, as well as the differences among the assessment methods used and the flowchart structure of the framework that incorporates these assessments. Ideally, the frameworks should give the same results under the same circumstances. Also, frameworks usually ignore the follow-up stage of the alternatives assessment, which is an important shortcoming of the frameworks. Common approaches, such as the exclusion of assessments or the use of the sequential elimination method seem to be a temporary solution to the existing problem of the implementation of these frameworks by SMEs. Common principles and methods should be in place to be able to minimize those differences among frameworks toward an optimized framework that enables assessors with limited resources to conduct a comprehensive assessment that is necessary to avoid a regrettable substitution.     

The value and limitations of using process models to describe the manufacturing organization

Process models offer a systematic, well-denned way of representing the structure of a firm's manufacturing operations. They record the activities that are performed in order to achieve a well-defined purpose of some kind (especially a commercial one), together with the activities' inter-dependencies. In notations such as that of IDEF_o these models have a hierarchical decomposition, in which activities are successively decomposed into more detailed activities, connected by a pattern of constraints of various kinds. Process models can be used to substantiate a number of claims about the satisfactoriness of a firm's operating structures: claims that its activities can be carried out with a greater degree of concurrency, for instance, or that there are redundant or duplicated activities, or spans of control that do not match readily-identified processes. This paper describes a number of observations that were made about the practice of process modelling in an engineering firm. It discusses, in particular, the limited expressive power of process modelling notations and the problems encountered when a normative approach is taken to the analysis of a model-when the analysis refers to some notion of an ideal factory (such as a cellular organization).     

On deadlock control for a class of generalised Petri net models of flexible manufacturing systems

This paper develops a deadlock prevention policy for a class of Petri nets that can model flexible manufacturing systems with assembly and disassembly operations. Siphons in a plant Petri net model are divided into elementary and dependent siphons according to the linear dependency of their characteristic T-vectors. The proposed approach is to make every siphon satisfy the controlled-siphon property (the cs-property), i.e., at any reachable marking, any siphon is max-marked, so that no deadlock states can be reached. The satisfiability of the cs-property is achieved by explicitly adding a monitor for each elementary siphon. The max-controllability of a dependent siphon is ensured by properly supervising its elementary siphons. More permissive behaviour of the non-blocking supervisor is obtained through the rearrangement of the output arcs of the monitors. Compared with existing policies reported in the literature, the advantage of the present method is that a small number of monitors are added and the iterative computing process is accordingly avoided. Finally, the application of the proposed method to an FMS example is presented.     

Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies

This article presents initial results in the search for analytical models that can predict the performance of one-machine systems under periodic and event-driven rescheduling strategies in an environment where different job types arrive dynamically for processing and set-up must incur when production changes from one product type to another. The scheduling algorithm considered uses a first-in firstout dispatching rule to sequence jobs and it also groups jobs with similar types to save set-up time. The analytical models can estimate important performance measures like average flow time and machine utilization, which can then be used to determine optimal rescheduling parameters. Simulation experiments are used to show that the analytical models accurately predict the performance of the single machine under the scheduling algorithm proposed.