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Background Polyethylene glycol (PEG) is widely used in industry and medicine. Anti-PEG antibodies have been developed for characterizing PEGylated drugs and other applications. However, the underlying mechanism for specific PEG binding has not been elucidated. Methods The Fab of two cognate anti-PEG antibodies 3.3 and 2B5 were each crystallized in complex with PEG, and their structures were determined by X-ray diffraction. The PEG-Fab interactions in these two crystals were analyzed and compared with those in a PEG-containing crystal of an unrelated anti-hemagglutinin 32D6-Fab. The PEG-binding stoichiometry was examined by using analytical ultracentrifuge (AUC). Results A common PEG-binding mode to 3.3 and 2B5 is seen with an S-shaped core PEG fragment bound to two dyad-related Fab molecules. A nearby satellite binding site may accommodate parts of a longer PEG molecule. The core PEG fragment mainly interacts with the heavy-chain residues D31, W33, L102, Y103 and Y104, making extensive contacts with the aromatic side chains. At the center of each half-circle of the S-shaped PEG, a water molecule makes alternating hydrogen bonds to the ether oxygen atoms, in a similar configuration to that of a crown ether-bound lysine. Each satellite fragment is clamped between two arginine residues, R52 from the heavy chain and R29 from the light chain, and also interacts with several aromatic side chains. In contrast, the non-specifically bound PEG fragments in the 32D6-Fab crystal are located in the elbow region or at lattice contacts. The AUC data suggest that 3.3-Fab exists as a monomer in PEG-free solution but forms a dimer in the presence of PEG-550-MME, which is about the size of the S-shaped core PEG fragment. Conclusions The differing amino acids in 3.3 and 2B5 are not involved in PEG binding but engaged in dimer formation. In particular, the light-chain residue K53 of 2B5-Fab makes significant contacts with the other Fab in a dimer, whereas the corresponding N53 of 3.3-Fab does not. This difference in the protein-protein interaction between two Fab molecules in a dimer may explain the temperature dependence of 2B5 in PEG binding, as well as its inhibition by crown ether.

Semiconductor manufacturing has been extensively exploiting machine-learning (ML) to process equipment sensory data (ESD) for near-real time anomaly detection (AD). ESD characteristics are highly diversified and data lengths vary among processing steps and cycles. Cleansing ESD with minimum distortion (CMD) to fit the fixed-length input requirement by ML-based AD is critical to AD effectiveness and is challenging. This paper presents a novel CMD method of four innovations: i) statistical mode-based equalization of step data lengths for the least number of step data length changes, ii) importance indicator value (IIV) of a data sample based on its relative difference with the subsequent sample, and iii) step data segmentation into groups based on samples of significant IIVs and the least-entropy-group-to-cleanse-first rule, and iv) cleansing the least IIV sample(s) in the selected group for step data length equalization. CMD application to ESD demonstrates its characteristics preservation property. Simulation experiments are on an integration of data cleansing with an unsupervised ML-based AD system, STALAD. Comparisons with two benchmark methods over AD scenarios of small-scale drifts and shifts show that CMD not only is superior in facilitating accurate detection by STALAD but also helps detect anomaly much earlier than using the two benchmarks.

Background: Development of an efficacious egg-free mock-up H5N1 vaccine is key to our preparedness against pandemic avian flu. Methods: This is a single-center, randomized, observer-blinded phase I clinical trial evaluating the safety and immunogenicity of an alum-adjuvanted Madin–Darby canine kidney (MDCK)-derived inactivated whole-virion H5N1 influenza vaccine in healthy adults. Hemagglutination inhibition (HAI) and neutralizing antibody titers were measured using horse and turkey red blood cells (RBCs). Results: Thirty-six adult subjects were randomized to receive two doses of 0.5 mL of the MDCK-derived H5N1 alum-adjuvanted vaccine containing 7.5, 15, or 30 μg of hemagglutinin (HA) 21 days apart. The candidate vaccine was well tolerated and safe across the three dosing groups. The most frequent adverse event was injection site pain (46.5%). Both HAI and neutralizing antibody titers increased after each vaccination in all three dosing groups. The best HAI responses, namely a seroconversion rate of 91.7% and a geometric mean ratio of 9.51 were achieved with the HA dose of 30 μg assayed using horse RBCs at day 42. HAI titers against H5N1 avian influenza virus was significantly higher when measured using horse RBCs compared with turkey RBCs. Conclusions: This Phase I trial showed the MDCK-derived H5N1 candidate vaccine is safe and immunogenic. The source of RBCs has a significant impact on the measurement of HAI titers (ClinicalTrials.gov number: NCT01675284.). Keywords: Influenza A, Avian influenza, H5N1, Vaccination, Immune response

Influenza is a contagious acute respiratory disease caused by the influenza virus infection. Hemagglutinin (HA) is an important target in the therapeutic treatment and diagnostic detection of the influenza virus. Influenza A virus encompasses several different HA subtypes with different strains, which are constantly changing. In this study, we identified a fully human H1N1 neutralizing antibody (32D6) via an Epstein-Barr virus-immortalized B cell-based technology. 32D6 specifically neutralizes the clinically isolated H1N1 strains after the 2009 pandemic but not the earlier strains. The epitope was identified through X-ray crystallographic analysis of the 32D6-Fab/HA1 complex structure, which revealed a unique loop conformation located on the top surface of HA. The major region is composed of two peptide segments (residues 172–177 and 206–213), which form an abreast loop conformation. The residue T262 between the two loops forms a conformational epitope for recognition by 32D6. Three water molecules were observed at the interface of HA and the heavy chain, and they may constitute a stabilizing element for the 32D6-HA association. In addition, each 32D6-Fab is likely capable of blocking one HA trimer. This study provides important information on the strain specificity of 32D6 for the therapeutic treatment and detection of viral infection.

Monitoring the Equipment Health Indicator (EHI) of critical machines helps effectively to maintain process quality and reduce wafer scrap, rework, and machine breakdowns. To model and illustrate the integration of EHI in scheduling decisions to balance between productivity and quality risk, this paper presents two mixed integer linear programs to schedule jobs on heterogeneous parallel batching machines. The capability of a machine to process a job is categorized as preferred, acceptable, and unfavorable based on the job requirements. The quality risk of processing a job by a machine is a function of its EHI and the capability level of the machine for the job, which is modeled as a penalty in the objective function of trading-off between productivity and quality risk. The first model is static and assumes constant EHI of machines on the scheduling horizon, whereas the second model considers the EHI dynamics, i.e., the machine condition deteriorates over time based on the scheduled jobs. Numerical experiments indicate the potential applications of using EHI-integrated scheduling approaches to analyze and optimize the trade-off between productivity and quality risk.

With more and more people shopping online, companies deal with customer data input not only in high volume but also dynamic. In order to attract target customers more effectively and to provide customers with more personalized services, how to automatically extract personal preference from the real-time data and make real-time recommendation has been growing in importance for businesses in the competitive modern society. Current data analysis methods for online shopping recommendation largely rely on historical transaction record. Analyses have indicating that next items a customer would like to buy not only depend on one’s past historical records but on the item currently being put into the shopping cart. This paper designs an engine to combine each customer’s past transaction and current shopping cart data to dynamically infer one’s preference for the next items. The design, Transaction-Data Based Real-time Preference Inference Engine (TRPIE), consists of two innovative ideas. The first exploits the purchasing sequence information and turns one’s purchase history into a temporal series of data, where a customer’s dynamic purchasing behaviour information lies. The second is a design of a two-layer Recurrent Neural Network (RNN) for extracting personal purchasing preference pattern from the temporal series of data to infer preference of next items. A reference implementation of TRPIE design integrates existing tools such as Keras, tensorflowTM ,sklearnTM, and MlxtendTM. Test results over real data from 1,374 people show that prediction accuracy has doubled that obtained by a basket analysis method, which ignores sequentiality of purchasing items.&nbsp

In recent years, microgrids have developed as important parts of power systems and have provided affordable, reliable, and sustainable supplies of electricity. Each microgrid is managed as a single controllable entity with respect to the existing power system but demands for joint operation and sharing the benefits between a microgrid and its hosting utility. This paper is focused on the joint operation of a microgrid and its hosting utility, which cooperatively minimize daily generation costs through energy exchange, and presents a payment calculation scheme for power transactions based on a fair allocation of reduced generation costs. To fairly compensate for energy exchange between the micro- and utility grids, we adopt the cooperative game theoretic solution concept of Shapley value. We design a case study for a fictitious interconnection model between the Mueller microgrid in Austin, Texas and the utility grid in Taiwan. Our case study shows that when compared to standalone generations, both the micro- and utility grids are better off when they collaborate in power exchange regardless of their individual contributions to the power exchange coalition.

The telomerase-specific replication-competent oncolytic adenovirus, Telomelysin, was developed for virus-mediated preferential lysis of tumor cells. Its selectivity is derived from a human telomerase reverse transcriptase (hTERT) promoter-driven active viral replication, which occurs in cancer cells with high telomerase activity but not in normal cells lacking such activity. Because the TERT activity is elevated in most cases of hepatocellular carcinoma (HCC), the current study aims to investigate whether Telomelysin can be used for treatment of HCC. The oncolytic effect of Telomelysin has been investigated both in vitro using cell culture and in vivo using an immunocompetent in situ orthotopic HCC model. In this model, HCC developed spontaneously in the liver of HBx transgenic mice, which is pathologically and genetically similar to human HCC. In cell culture assay, Telomelysin lyses HCC cell lines at a low multiplicity of infection (MOI), ranging 0.77-6.35 (MOI [PFU/cell]). In the orthotopic HCC model, Telomelysin showed a potent oncolytic effect on HCC but spared normal liver tissue. Dose escalation analysis identified a safety dose of 1.25 × 10(8) PFU for this model. The effect of multiple injections of Telomelysin was also evaluated in this immunocompetent HCC model. We found that the virus replicates in HCC after a second intratumoral injection despite an immune response induced by the previous injection. This preclinical study shows that Telomelysin can be used for treatment of human HCC at an appropriate dosage and that its tumor-killing activity persists after multiple injections.

Currently, most independent system operators in the U.S. run auctions by minimizing the total bid cost ["bid cost minimization" (BCM)], and then calculate payments based on market clearing prices. Under this setup, the payment cost could be significantly higher than the minimized bid cost. Recently, an alternative auction mechanism that minimizes the consumer payment cost ["payment cost minimization" (PCM)] has been discussed. Literature has shown that for the same set of bids, PCM leads to reduced consumer payments. However, market participants may bid differently under the two auctions, and therefore, the payment reduction may not be realized. This necessitates the study of strategic behaviors of participants. In this paper, suppliers' bidding strategies in a day-ahead energy market are investigated for both auctions by using a game theoretic framework with Nash equilibrium as the solution concept. To simplify the solution process, the originally continuous strategies are discretized to form matrix games. Discretization may cause the loss of equilibria and the creation of artificial solutions. To reduce these side effects, "approximate Nash equilibria" are introduced to recover lost equilibria, and additional strategy samples are evaluated to eliminate artificially created solutions. Games are then solved by examining supplier payoffs obtained from running auctions. Characteristics of auctions are exploited, leading to improved computational efficiency. Numerical testing results show that the PCM leads to significant payment reductions and relatively small increases of production costs as compared to BCM. Also, the ?hockey-stick? bidding is more likely to occur under BCM. Finally, long-term impacts of PCM are discussed, and whether it would lower costs to consumers in the long run, including capacity payments, remains to be investigated.

Deregulated electricity markets in the U.S. currently use an auction mechanism that minimizes total supply bid costs to select bids and their levels. Payments are then settled based on market-clearing prices. Under this setup, the consumer payments could be significantly higher than the minimized bid costs obtained from auctions. This gives rise to ldquopayment cost minimization,rdquo an alternative auction mechanism that minimizes consumer payments. We previously presented an augmented Lagrangian and surrogate optimization framework to solve payment cost minimization problems without considering transmission. This paper extends that approach to incorporate transmission capacity constraints. The consideration of transmission constraints complicates the problem by entailing power flow and introducing locational marginal prices (LMPs). DC power flow is used for simplicity and LMPs are defined by ldquoeconomic dispatchrdquo for the selected supply bids. To characterize LMPs that appear in the payment cost objective function, Karush-Kuhn-Tucker (KKT) conditions of economic dispatch are established and embedded as constraints. The reformulated problem is difficult in view of the complex role of LMPs and the violation of constraint qualifications caused by the complementarity constraints of KKT conditions. Our key idea is to extend the surrogate optimization framework and use a regularization technique. Specific methods to satisfy the ldquosurrogate optimization conditionrdquo in the presence of transmission capacity constraints are highlighted. Numerical testing results of small examples and the IEEE Reliability Test System with randomly generated supply bids demonstrate the quality, effectiveness, and scalability of the method.

Group elevator scheduling has long been recognized as an important problem for building transportation efficiency, since unsatisfactory elevator service is one of the major complaints of building tenants. It now has a new significance driven by homeland security concerns. The problem, however, is difficult because of complicated elevator dynamics, uncertain traffic in various patterns, and the combinatorial nature of discrete optimization. With the advent of technologies, one important trend is to use advance information collected from devices such as destination entry, radio frequency identification, and sensor networks to reduce uncertainties and improve efficiency. How to effectively utilize such information remains an open and challenging issue. This paper presents the optimized scheduling of a group of elevators with destination entry and future traffic information for normal operations and coordinated emergency evacuation. Key problem characteristics are abstracted to establish a two-level separable formulation. A decomposition and coordination approach is then developed, where subproblems are solved by ordinal optimization-based local search, and top ranked nodes are selectively optimized by using dynamic programming. The approach is then extended to handle up-peak with little or no future traffic information, elevator parking for low intensity traffic, and coordinated emergency evacuation. Numerical testing results demonstrate near-optimal solution quality, computational efficiency, the value of future traffic information, and the potential of using elevators for emergency evacuation.

Semiconductor wafer fab operations are characterized by complex and reentrant production processes over many heterogeneous machine groups with stringent performance requirements. Efficient composition of good scheduling policies from combinatorial options of wafer release and machine dispatching rules has posed a significant challenge to competitive fab operations. In this paper, we design a fast simulation-based methodology by an innovative integration of ordinal optimization (OO) and design of experiments (DOEs) to efficiently select a good scheduling policy for fab operations. Instead of finding the exact performance among scheduling policies, our approach compares their relative orders of performance to a specified level of confidence. Our new approach consists of three stages: performance estimation model construction using DOE, policy option screening process, and final simulation evaluation with intelligent computing budget allocation. The exponential convergence of OO is integrated into all the three stages to significantly improve computational efficiency. Simulation results of applications to scheduling wafer fabrications not only screen out good scheduling policies but also provide insights about how factors such as wafer release and the dispatching of each machine group may affect production cycle times and smoothness under a reentrant process flow. Most of the OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. Such a high speedup enables decision makers to explore much larger problems. Note to Practitioners - This paper designs a fast simulation-based methodology to compose a good scheduling policy from various dispatching rules of fab operations. The methodology innovatively applies DOE to estimate performance of dispatching rule combinations (policies) over various machines groups in a fab, screens out good enough policy options by using OO over the performance estimation, and allocates computation time intelligently to simulate potentially good options. Our study shows that OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. The high speedup enables fab managers to identify good scheduling policies from the many combinations of wafer release and dispatching rules.

Motivated by foundry service provisioning in the semiconductor industry, this paper adopts a reverse auction-based mechanism to model job order assignment from a job owner (auctioneers) to a few qualified and competing foundry fabs (bidders). A job order owner announces job requirements and payments for fabs to bid on while qualified fabs bid on a job by offering the discount to payment and processing schedule of the job. This model aims at capturing the non-cooperative gaming among the job owner and the fabs because of the private information such as objectives, valuation of jobs, available capacity and constraints. Two integer-programming formulations are formulated for the reverse auction of job assignment; one is deterministic and models a common practice of mean value-based bidder decision-making while the other is stochastic and captures bidders' consideration of uncertainties and the associated risk in decision-making. A Lagrange relaxation-based, near-optimal scheduling method is developed to model a bidder's selection of job-to-bid and schedule for the deterministic formulation. The bidder's job selection and scheduling model for the stochastic formulation combines simulation and simple heuristics. A bidder decides the discount offer for each job-to-bid by a simple fixed-increment scheme. The auctioneer simply assigns the job to the bid with the highest discount offer in each round of bidding. Analyses show that the reverse auction model leads to an equilibrium solution, where no single bidder would unilaterally deviate from the auction result. Numerical study by using the reverse auction model demonstrates that consideration of uncertainties in bidders' decisions has a larger impact on performance of both bidders and the auctioneer than optimality of the mean value-based bidder scheduling algorithm.

Although digital terrestrial television has undergone significant development over the last two decades, no single protocol for transition has been poised to break out as bona fide replacement for terrestrial TV programs for those elderly in the rural area in Taiwan.

Deregulated electricity markets use an auction mechanism to select offers and their power levels for energy and ancillary services. A settlement mechanism is then used to determine the payments resulting from the selected offers. Currently, most independent system operators (ISOs) in the United States use an auction mechanism that minimizes the total offer costs but determine payment costs using a settlement mechanism that pays uniform market clearing prices (MCPs) to all selected offers. Under this setup, the auction and settlement mechanisms are inconsistent since minimized costs are different from payment costs. Illustrative examples in the literature have shown that for a given set of offers, if an auction mechanism that directly minimizes the payment costs is used, then payment costs can be significantly reduced as compared to minimizing offer costs. This observation has led to discussions among stakeholders and policymakers in the electricity markets as to which of the two auction mechanisms is more appropriate for ISOs to use. While methods for minimizing offer costs abound, limited approaches for minimization of payment costs have been reported. This paper presents an effective method for directly minimizing payment costs. In view of the specific features of the problem including the nonseparability of its objective function, the discontinuity of offer curves, and the maximum term in defining MCPs, our key idea is to use augmented Lagrangian relaxation and to form and solve offer and MCP subproblems by using the surrogate optimization framework. Numerical testing results demonstrate that the method is effective, and the resulting payment costs are significantly lower than what are obtained by minimizing the offer costs for a given set of offers.

A job shop is a typical environment for manufacturing low-volume and high-variety discrete parts, where parts are of various due dates, priorities, and sequences of production operations. Good scheduling of when to do what using which resource is critical and challenging for the competitiveness of job shops. The Lagrangian relaxation neural network (LRNN) presented by Luh et al. provides an effective solution to this problem. To further speed up the scheduling of large problems, the parallelism of the LRNN approach is exploited in this paper for hardware implementation. A parallel processor based on the single-instruction multiple-data-stream architecture and its associated instruction set are designed. The architecture is implemented in a single-poly quadruple-metal 0.35-/spl mu/m CMOS technology. Test results shows that the fabricated chip achieves 10 and 30 times speed-up when compared with several commercial digital signal processor chips and a 600-MHz PC, respectively.

Time-based competition and market globalization make it imperative for supply chains to have short and reliable order deliveries. This is difficult to achieve in view that activities of individual manufacturers are subject to various uncertainties such as unknown order arrivals and stochastic operations. Furthermore, delays of one manufacturer may propagate to it downstream manufacturers through precedence relationships. To stay competitive, it is critical to control variability and order lead-times across a chain, and to achieve delivering final products within specified target time windows with high probability. This is the key idea of achieving x-sigma delivery performance. In this paper, make-to-order supply chains with sequential workflows are considered. An effective solution methodology is developed to minimize overall order tardiness, earliness costs and delivery variability through effective scheduling and coordination. To accommodate new arrivals while fulfilling commitments of existing orders, a rescheduling approach is presented to generate high-quality schedules in a timely fashion. Numerical testing results demonstrate that the new approach is effective to schedule manufacturers across a chain to achieve the required three-sigma deliveries.

The concepts of a virtual lab (VF) and manufacturing service (MS) have been proposed over recent years as a way to face the fierce competition of the semiconductor industry. This paper proposes a way to build a VF with a flexible manufacturing service in a highly specialized and uncertain environment. The VF enabling framework contains manufacturing service, business process (BP), and infrastructure (IR) layers. A novel dynamic manufacturing service provision mechanism (DMSPM) was designed to compose objects flexibly among the three layers of the various manufacturing services. The skeleton of the scheme includes name mapping, business process binding, resource reservation binding, and manufacturing service management binding to allow flexible service composition. To assess the feasibility and potential of the framework and our DMSPM, an order commitment service (OCS) provided by a foundry fab was used as an example. A prototype system was designed and implemented to demonstrate that current IT, CASE tools, a lab information infrastructure, and data/information availability make DMSPM readily realizable and that it has good possibilities in VF and e-business developments.

In a free of charge or flat-rate Internet access environment, there often exists abusive and unfair usage of network resources. In this paper, the Internet access by the dormitory users at the National Taiwan University (NTU) serves as a conveyer problem. A quota-based service architecture combined with priority scheduling is developed to resolve the problem of unfair and abusive Internet access encountered in the NTU dormitory networks. The goal is to meet the basic traffic demands of the majority users while limiting abusive usage from ignorant heavy users. Two classes of services are provided. The regular class sets a quota on each user's traffic volume per quota control period. Out-of-profile or possibly abusive traffic is directed to a lower priority service class called the custody service and served when there is an excess bandwidth. The new policy and schemes are implemented on a quality of service router, a meter reading server, an accounting server and a Web-based service management server with minimal intrusion of user privacy and least disturbance to the existing service offering. The experimental results show that the original congestion at the bottleneck link was alleviated with a 48.9% reduction of the average packet drop rate. Abusive Internet access by the top 2% heavy users is greatly reduced by 57.82%. Astonishingly, over 91% users are pleased with this new policy and their network usages all increase. This is a win-win result. Under the new policy and implementation, users paying the same amount of service fee are now able to fairly share resources. A mathematical model for prediction of control effects is also presented. The experimental results are consistent with the prediction of the model.

This paper describes the design of a two-level hierarchical production scheduling engine, which captures the industrial practice of mass production semiconductor fabrication factories (fabs). The two levels of the hierarchy consist of a mid-term scheduler and a short-term scheduler, and are aimed at achieving coordination between the fab-wide objectives and local shop-floor operations. The mid-term scheduler maximizes weighted production flow to reduce the fab-wide cycle time and ensure on-time delivery by properly setting daily production target volumes and reference work-in-process (WIP) levels for individual part types and stages. Mid-term scheduling results are further broken down into more detailed schedules by the short-term scheduler. In addition to the same set of operational constraints in mid-term scheduling, the short-term scheduler includes the consideration of batching effects. It maximizes weighted production flow while tracking the daily production targets and the reference WIP levels specified by mid-term scheduling. The schedulers adopt a solution methodology with three ingredients; the Lagrange relaxation approach, network flow optimization, and Frank-Wolfe method. The scheduling tool is reasonably efficient in computation.

As manufacturing systems are often operated under distributed control, translating overall production goals, such as system output rate and cycle time, into local flow control targets for internal operations, such as work-in-process (WIP) levels and part release intervals, has been a significant and challenging research topic. This paper studies the problem for semiconductor wafer fabrication plants, where there are multiple part types, failure-prone machines, and deterministic reentrant process flows. Such a system is first modeled as a failure-free and reentrant open queuing network with an aggregated part type. By exploiting a decomposition-based approximation, a backward queuing network analysis (BQNA) is designed to derive flow control parameters in terms of means and variations of input processes for each machine group. These derived control parameters can be utilized to obtain managerially tangible requirements for cycle times and WIP levels and to create guidelines for distributed flow control leading to the desired overall production goals. Comparisons with simulation results for two example wafer fabrication models demonstrate the accuracy and computational efficiency of BQNA and its potential for real applications.

Computational efficiency is one of the major challenges of applying simulation to short-term operation scheduling of semiconductor wafer fabrication factories (fabs), which are characterized by re-entrant process flows, stringent production control requirements and fast changing technology and business environments. The paper explores the application of the ordinal optimization (OO)-based simulation technique to efficiently selecting good rules for scheduling wafer fabrications. An efficient simulation tool, which makes use of OO and optimal computing budget allocation techniques, is developed. Experiments with the OO-based simulation tool are conducted for static selection of good rules under different factors such as initial state, performance index and time horizon. Results indicate that one to two orders of computation time reduction over traditional simulations can be achieved and that what rules are good varies with factors of initial state, performance index and time horizon. These results motivate our further investigation about applications to dynamic selection of dispatching rules upon the occurrence of two significant uncertain events: holding of significant amount of wafers-in-process due to engineering causes and major machine failures. Results demonstrate the value of dynamic rule selection for uncertainty handling, the insightful selection of good rules and the needs for future research.

In this paper, an end-of-line quality control scheme based on wafer acceptance test (WAT) data is presented. Due to the multiple-stream and sequence-disorder effects typically present in the WAT data, an abnormal process shift caused by one machine at an in-line step may become vague for detection using end-of-line WAT data. A methodology for generating robust design parameters for the simultaneous application of Shewhart and EWMA control charts to WAT data is proposed. This SHEWMA scheme is implemented in a foundry environment and its detection and diagnosis-enhancing capabilities are validated using both numerical derivations and fab data. Results show that the SHEWMA scheme is superior to the current practices in detection speed. Its use is complementary to the existing in-line SPC for process integration.

This paper presents a Lagrangian relaxation-based approach for production scheduling of flexible flow shops (PSFFS) where sequence-dependent setup effects are significant. PSPFS is first formulated as a separable integer programming problem with synchronization constraints between production and machine usage. Lagrangian relaxation is then applied, and the scheduling is decomposed into part production and machine scheduling. In these subproblems, there are network flow structures in the equality constraints describing machine status change and production flow balance. Our iterative algorithm applies a minimum cost network flow algorithm to individual subproblems and adopts an efficient surrogate subgradient method to optimize Lagrangian multipliers. A machine availability-searching heuristic finally adjusts the solution to satisfy all synchronization constraints by exploiting the network structure, economic interpretation of Lagrangian multipliers, and the slack time policy. Numerical results of 16 cases, each having 20 test problems, demonstrate that differences between the schedules obtained by our approach and the true optima are on average within 15%, CPU times spent are all less than 17 min on a Pentium-II personal computer. Among the problem dimension factors, the number of part types has the most significant effect on both optimality and computation efficiency. Application of the methodology to daily scheduling of a realistic integrated circuit testing facility of 30 machines takes about 6 min of CPU time to generate a near-optimal solution.

An iterative mean value analysis (MVA) algorithm is developed in this paper for a class of queueing networks (QNs) with forking (MVAF). The algorithmic development is motivated by the performance evaluation of a Multicube multiprocessor system and its cache coherent protocol proposed by Goodman and Woest, 1988. We model the system as a multichain, multiclass, mixed QN with forking phenomena. The MVAF algorithm that we propose approximates a forking by independent flows at each iteration and iteratively applies MVA to the approximate QNs without forking. We prove that MVAF converges to the exact performance measures for the QN with forking. Comparisons among performance evaluations by MVAF, simulation and the mean value performance analysis by Leutennegger and Vernon, 1988, demonstrate both the validity and effectiveness of MVAF.

Under the effects of multiple-stream and sequence-disorder, process change caused by one machine at an in-line step may result in changes in both the mean and variance of end-of-line wafer acceptance test (WAT) data sequence. To speed up trend detection of WAT data without resorting to an intensive computing power, an end-of line SHEWMAC scheme is proposed, which combines a Shewhart, an exponentially weighted moving average (EWMA), and an exponentially weighted moving Cpk (EWMC) charts for jointly monitoring the mean and variance of wafer lot average sequence from WAT data. In view of the wide ranges of process conditions and low volume of each product in a foundry fab, a data normalization technique is adopted to aggregate data of similar products and a new design method is developed to generate a robust set of scheme parameters. Simulation and field data validation show that SHEWMAC is superior to the combined Shewhart-EWMA scheme in shift detection speed and is complementary to the in-line SPC.

The daily scheduling for a research and development (R&D) pilot line of semiconductor wafer fabrication is addressed. An integer programming problem formulation is given, which captures the salient features, such as high variety and very low volume, cyclic process flows, batching at diffusion machines, single mask for each photolithography operation, loop test, and engineering splitting and merging of wafer lots. A solution methodology for scheduling flow shops based on Lagrangian relaxation is extended to solve this class of problems. It may also effectively handle data inaccuracy and cope with production uncertainties. Numerical results demonstrate both the feasibility and potential of this method. >

In this paper, we focus on the design issues of applying the exponentially weighted moving average (EWMA) chart to end-of-line electrical test data. Since the sequence of end-of-line test data is not the same as the sequence in each process step, an abnormal trend in any of the process steps is more difficult to detect based on end-of-line test data than based on single step process data (if available). Our approach uses EWMA chart because the moving average is able to smooth out the sequence-disordered effect and the weighting factor allows us to choose an effective moving average size. The correlation among weighting factor, detection speed, and sequence-disordered effect is studied. Fab data is used to verify the effectiveness of EWMA chart for detecting process shifts if we appropriately choose the weighting factor based on the derived correlation.

In this paper, a Target Setting System (TSS) is designed as a computer aided decision support, which includes three modules: (1) Master Production Schedule Conversion (MPSC), (2) Capacity Estimation, and (3) Target Setting Main Function (TSMF). MPSC converts wafer release and output schedule into daily demanded moves of each production stage while CE estimates tool capacity by taking auto-regression of the actual move data. TSMF then takes the outputs of MPSC and CE and WIP distribution as its inputs, and adopts PULL, PUSH and iterative proportional capacity allocation schemes to calculate the daily target of each stage. TSS has been implemented for daily application. The scheduled total moves by TSS are within 5% of what have been actually achieved on the shop floor and about 50% of scheduled stage targets are within 15% difference with actual stage moves.

Manufacturing service provisioning is one of the most important objectives of a virtual fab (VF). In this paper, we design a dynamic binding mechanism for creating a manufacturing service in a VF, especially the order management perspective. The mechanism is developed on top of object-orient modeling of a virtual fab. Key steps of the mechanism include name and resource mapping, business and operation process binding, resource reservation and service management binding. A simple example serves to illustrate the ideas, which are further described by a computer-aided software engineering tool to demonstrate the potential of the dynamic binding mechanism for real applications.

Fast growth of foundry fabs, fierce global competition and advancements of information technology have motivated in the semiconductor industry the concept of virtual fab (VF) with emphasis on manufacturing service provisioning. In this paper, a conceptual framework is proposed for VFs, which consists of three layers: infrastructure, business process, and virtual product layers. Data warehouse, open real-time simulation service, dynamic binding and customer-oriented interface constitute the four key ingredients of the infrastructure. Basic manufacturing business processes in the context of order management are then analyzed and manufacturing services are carefully classified within the VF framework. How the proposed VF infrastructure facilitates these services and how service quality should be managed demand future research.

Automatic and rapid network service restoration (self-healing) when failures occur is of increasing importance for network operations and management as network speed and bandwidth increase. In this paper, we study the self-healing problem of an ATM network at the virtual path (VP) configuration level. We analyze the design issues of self-healing algorithms, and develop a state transition and bandwidth division model of individual VPs and links. On top of the model, we design a restoration algorithm that searches and activates alternate VPs for the failed VPs by combining a preplan method and a double search method. The preplan method activates preassigned backup VPs dynamically, while the double search method searches for alternate VPs from both the source and destination nodes of failed VPs. The double search method is also applied to reconstruction of the backup VP plan. Simulations demonstrate that our new algorithms outperform existing preplanned and dynamic self-healing algorithms in both restoration time and restoration ratio.

A backward analysis (BA) algorithm is designed to translate the desired output specifications of a re-entrant production line into internal flow requirements for machine groups. These requirements may serve as guidelines for distributed production flow control. Specific output specifications considered are the mean and variance of inter-departure time of the line output process, while production flow requirements are the target mean rates and upper bounds on squared coefficient of variation of individual machine input processes. Our BA is developed on top of an open queueing network (OQN) model of re-entrant lines. It combines decomposition-based queueing network analysis techniques to approximate, for a re-entrant OQN, the relationship between the characteristics of the output process and the characteristics of external input and machine service processes. The BA then derives, as two sets of linear equations, input flow requirements for machine groups from line output specifications and machine service characteristics. Preliminary simulation study demonstrates that our BA algorithm is reasonably accurate and fast and can be applied to deriving practically measurable production flow requirements.

In this paper, we focus on the design issues of applying an SPC control chart to end-of-line wafer acceptance test (WAT) data. Since the sequence of end-of-line data is not the same as the sequence in each process step, an abnormal trend in any of the process steps is more difficult to detect based on the end-of-line data than based on single process data (if available). To overcome this deficiency, we propose an exponentially weighted moving average method for sequence-disordered data (EWMA/SD). The basic idea is that the moving average can smooth out the sequence-disordered effect and weighting factors allow us to choose an effective window size so that the underlying trend can be seen. It is different from the traditional EWMA method as it has the capability of handling sequence-disordered data. An end-of-line trend detection system has been developed for validation of the method, which consists of three modules: a run length distribution generator, an optimal parameter generator, and an EWMA/SD control chart. Based on process characteristics, the corresponding run lengths of an EWMA/SD control chart for different parameter vectors are derived by the Markov chain approach. The optimal parameter vector is chosen as the one which meets the requirement for maximum false alarm rate and maximizes the detection speed at the same time. Results of simulation and field data validation show that EWMA/SD is able to smooth out the sequence-disordered data, be sensitive to process changes, and be robust to background noise.

We describe the need and desired features for broadband ISDN (B-ISDN) simulation environments and present the design and implementation details of a simulation testbed prototype. The simulation testbed is designed to be highly flexible for network topology/traffic descriptions and provide a friendly graphical user interface. We also show that interesting simulation results can be easily observed via the use of this testbed. >

In an asynchronous transfer mode (ATM)-based broadband integrated services digital network (BISDN), an ATM adaptation layer (AAL) is needed to adapt each non-ATM application to the ATM layer. This paper proposes an abstract AAL logic design methodology which combines the concept of 'quotient problem' defined by Calvert and Lam, 1987, with the concept of supervisory control in the context of discrete event system theory. A 5-step AAL synthesis algorithm is developed. It is shown that if an AAL does exist, then the algorithm indeed finds a desired AAL FSM. Analysis also indicates that the synthesis algorithm is of polynomial computation time complexity, which lays a foundation for future extension to realistic applications. >

This paper analyzes the properties of a proportional machine allocation scheme in a reentrant production line of one product and one machine group. The scheme allocates machines to each production stage proportional to the workload of the stage. It is proved that given initial work-in-process (WIP) distribution and a constant work release rate in the capacity of the line, there exists a unique, full machine capacity allocation for the line; when the proportional machine allocation scheme is repetitively applied over time, the whole reentrant line is eventually balanced. These results obtained under such a simplified scenario also provide some insights to the dynamics and resource allocation problem of a reentrant line.

Job shop is a typical environment for manufacturing high-variety and low-volume discrete parts. Good scheduling is critical and challenging to the competitiveness of job shops. The Lagrangian relaxation neural network (LRNN) provides an approach of quantifiable quality and successful industrial applications. To further speed up scheduling for large-scale problems, in this paper, the parallelism of the LRNN approach is exploited for hardware implementation. New designs include a SIMD architecture, its associated instruction set and detailed circuits. Logic level simulation of the circuit design shows consistent schedules with those obtained by a software implementation. The hardware implementation is expected to have a one to two orders speed-up over the software one.

We exploit the speed of an ordinal optimization (OO)-based simulation tool designed by Hsieh et al. (1999) to investigate dynamic selection of scheduling rules for semiconductor wafer fabrication (FAB). Although a scheduling rule is a combination of loading wafer release and dispatching rules, this paper specifically focuses on dispatching when significant amount of wafers-in-process are held due to engineering causes and when major machine failures occur. Four prominent dispatching rules combined with the wafer release policy of workload regulation constitute a basic set of rule options. The dispatching rule may be weekly selected based on FAB states over a four-week horizon. A total of 756 rule options are then evaluated and ranked by the OO-based simulation tool under the performance index of mean cycle time and throughput rate. Results demonstrate the value of dynamic rule selection for uncertainty handling, the insightful selection of good rules and the needs for further research.

In this paper a dynamic manufacturing service provisioning mechanism (DMSPM) is designed to flexibly create and execute manufacturing services under a virtual fab (VF) enabling framework The DMSPM exploits object-oriented (OO) technology to flexibly bind fab objects into services. It consists of four skeleton steps: name mapping, business process binding, resource reservation binding, and service management binding. To assess the feasibility and potential of DMSPM, the order commitment service (OCS) provided by a foundry serves as a study case. A prototype system is designed and implemented to realize DMSPM in the OCS application. The implementation demonstrated that current OO technology, CASE tools, Jab information infrastructure and data/information availability make DMSPM readily realizable and that DMSPM has a good potential for application to virtual Jab and e-business developments.