A new strategy for Phase I analysis in SPC

The Phase I analysis in statistical process control usually includes a task of filtering out out‐of‐control data in the historical data set via control charting. The conventional procedure for this is an iterative procedure that first uses all the samples to set up initial trial control limits and discards all the ‘out‐of‐control’ samples accordingly, and then iteratively repeats the screening step on the remaining samples until no more ‘out‐of‐control’ samples are detected. For simplicity, the ‘out‐of‐control’ samples here refer to the samples with their monitoring statistics exceeding the trial control limits. It is found in this study that this procedure throws away too many useful in‐control samples. To overcome this drawback, we propose and study a new iterative procedure that discards only one ‘out‐of‐control’ sample (i.e. the most extreme one) at each iteration. Our simulation study, using the Shewhart X Chart for illustration, demonstrates that the new one‐at‐a‐time procedure reduces dramatically the occurrences of false alarms. For cost‐saving, we further suggest a new strategy on when to stop and inspect the process to look for assignable causes for samples signaling out‐of‐control alarms. To determine the control limits, both the traditional method that controls the individual false‐alarm‐rate and the Bonferroni method that controls the overall false‐alarm‐rate are considered. The performances of the proposed schemes are evaluated and compared in terms of the false‐alarm rate and the detecting power via simulation studies. Copyright © 2009 John Wiley & Sons, Ltd.     

Statistical process monitoring using an empirical Bayes multivariate process control chart

In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory, we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright © 2001 John Wiley & Sons, Ltd.     

Inspection resource assignment in a multistage manufacturing system with an inspection error model

Producing high-quality products at low cost is always one concern for a multi-stage manufacturing system. That is, production costs and inspection efficiency should receive equal importance. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources during process planning. Product quality and the possible costs can then be concurrently considered when evaluating a manufacturing plan. Except for finite inspection station classes, the limited number of inspection stations of each inspection station class is considered to solve the inspection allocation problem in this research. Rather than utilizing a constant inspection error or a specified inspection error probability distribution determined by previous observations, the inspection allocation problem is solved using relative cost models in which the inspection error model is embedded. The inspection allocation problem can then be solved by practically reflecting the inspection error when tolerances are rapidly changed to satisfy customer requirements. Since determining the optimal inspection allocation plan seems impractical as the problem size becomes quite large, two heuristic methods have been developed by considering the defective rate, manufacturing cost and earliest stage priority in this research. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. A feasible manufacturing plan can then be determined and confirmed during process planning by concurrently solving the inspection allocation problem.     

Time evolution of entanglement and bistability of two coupled quantum dots in a driven cavity

Abstract

The time evolution of entanglement between two quantum dots (QDs) trapped inside a cavity driven by a coherent quantized field is studied. In the presence of dissipation, entanglement shows many interesting features such as sudden death and revival, and finite steady state value after sudden death. We also investigate dependence of entanglement on dot variables and its relation to bistability. It is found that entanglement vanishes when the cavity field intensity approaches the upper branch of the bistability curve. When the cavity is driven by a modulated field in the presence of dissipation, it can periodically generate entanglement, which is much larger than the maximum value attained in the steady-state for this system but the dots are never fully entangled.     

Physiographic Drainage-Inundation Model Based Flooding Vulnerability Assessment

Flooding vulnerability assessment is an important issue in Taiwan since Taiwan lies within the most active tropical cyclone formation zone of the Western Pacific. Huge economic damages and losses of human lives are occurred almost every year. This study aims to evaluate flooding vulnerability of a given area subject to large-scale land developments. A scoring-based approach associated with a physiographic drainage-inundation model is developed to quantitatively evaluate vulnerability for flooding. The flooding vulnerability index defined as the product of an exposure score and a hazard score. The exposure score assesses relative losses exposed to flooding, which is determined by land-uses classification. The hazard score measures flooding severity, which is simultaneously determined by inundation depth and duration that are obtained from the inundation model for a design storm. The Yenshui River basin located in southwestern Taiwan is used an example to illustrate the proposed method. The results show that the projected urbanization plan within the Yenshui River basin would increase flooding vulnerability from 0.371 to 0.472. However, this value is reduced to 0.388 when the mitigation measure has been implemented. The obtained spatial distribution of flooding vulnerability for a design storm provides decision-makers useful information to identify hotspots of the study area and evaluate effects of flood-mitigation measure on flooding risk-reduction.     

A hybrid two-phase encoding particle swarm optimization for total weighted completion time minimization in proportionate flexible flow shop scheduling

A proportionate flow shop (PFS) is a special case of the m machine flow shop problem. In a PFS, a fixed sequence of machines is arranged in s stages (s?>?1) with only a single machine at each stage, and the processing time for each job is the same on all machines. Notably, PFS problems have garnered considerable attention recently. A proportionate flexible flow shop (PFFS) scheduling problem combines the properties of PFS problems and parallel-identical-machine scheduling problems. However, few studies have investigated the PFFS problem. This study presents a hybrid two-phase encoding particle swarm optimization (TPEPSO) algorithm to the PFFS problem with a total weighted completion time objective. In the first phase, a sequence position value representation is designed based on the smallest position value rule to convert continuous position values into job sequences in the discrete PFFS problem. During the second phase, an absolute position value representation combined with a tabu search (TS) is applied starting from the current position of particles that can markedly improve swarm diversity and avoid premature convergence. The hybrid TPEPSO algorithm combines the cooperative and competitive characteristics of TPEPSO and TS. Furthermore, a candidate list strategy is designed for the TS to examine the neighborhood and concentrate on promising moves during each iteration. Experimental results demonstrate the robustness of the proposed hybrid TPEPSO algorithm in terms of solution quality. Moreover, the proposed hybrid TPEPSO algorithm is considerably faster than existing approaches for the same benchmark problems in literature.     

Creation of arbitrary spectra with an acousto-optic modulator and an injection-locked diode laser

We use a double-passed acousto-optic modulator (AOM), driven by an arbitrary waveform generator to produce multiple frequency components for a laser with arbitrary frequency spacings. A programmed sequence containing various sections of radio-frequency sinusoidal signal at different frequency is applied to drive the AOM. The diffracted light is used to injection-lock a diode laser. The combined techniques allow us to generate the multi-line spectra for the diode laser with arbitrary frequency spacings in the range of 100 MHz at a relatively high output power of 80 mW and a small power variation of 2%. Such a light source can be used in the application for laser cooling of molecules.     

Minimizing the total weighted completion time on a single machine under linear deterioration

This paper investigates a single-machine problem in which processing times of jobs are start time dependent and the aim is to minimize the total weighted completion time. Recent research has shown the complexity of this problem to be NP-hard; however, no optimal or heuristic algorithms have been proposed. In this paper, we explore the exact solution and propose several heuristic algorithms derived based on the impacts of model parameters. The effects of normal processing times and deterioration rates are also studied.     

Two-machine flowshop scheduling to minimize mean flow time under simple linear deterioration

A real industrial production phenomenon, referred to as deteriorating jobs, has drawn increasing attention. However, most research on this issue considers only single-machine problems. Motivated by this limitation, this paper considers a simple linear deterioration model in a two-machine flowshop where the objective is to minimize the mean flow time. Several dominance rules and three lower bounds are proposed to speed up the search for an optimal solution, and several heuristic algorithms are provided to derive near-optimal solutions. In addition, a computational experiment is conducted to evaluate their performances. Results indicate that the algorithms perform well, and a combined heuristic algorithm is recommended for practitioners.     

Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles

Plug-in hybrid electric vehicle (PHEV) technology is receiving attention as an approach to reducing US dependency on foreign oil and greenhouse gas (GHG) emissions from the transportation sector. PHEVs require large batteries for energy storage, which affect vehicle cost, weight, and performance. We construct PHEV simulation models to account for the effects of additional batteries on fuel consumption, cost, and GHG emissions over a range of charging frequencies (distance traveled between charges). We find that when charged frequently, every 20 miles or less, using average US electricity, small-capacity PHEVs are less expensive and release fewer GHGs than hybrid electric vehicles (HEVs) or conventional vehicles. For moderate charging intervals of 20–100 miles, PHEVs release fewer GHGs, but HEVs have lower lifetime costs. High fuel prices, low-cost batteries, or high carbon taxes combined with low-carbon electricity generation would make small-capacity PHEVs cost competitive for a wide range of drivers. In contrast, increased battery specific energy or carbon taxes without decarbonization of the electricity grid would have limited impact. Large-capacity PHEVs sized for 40 or more miles of electric-only travel do not offer the lowest lifetime cost in any scenario, although they could minimize GHG emissions for some drivers and provide potential to shift air pollutant emissions away from population centers. The tradeoffs identified in this analysis can provide a space for vehicle manufacturers, policymakers, and the public to identify optimal decisions for PHEV design, policy and use. Given the alignment of economic, environmental, and national security objectives, policies aimed at putting PHEVs on the road will likely be most effective if they focus on adoption of small-capacity PHEVs by urban drivers who can charge frequently.