L. Y. Chan

Cumulative quantity control charts for monitoring production processes

Two commonly used statistical quality control charts, the c-chart and u-chart, are unsatisfactory for monitoring high-yield processes with low defect rates. To overcome this difficulty, a new type of control chart called the cumulative quantity control chart (CQC-chart) is introduced in this paper. The CQC-chart can be used no matter whether the process defect rate is low or not, and when the process defect rate is low or moderate the CQC-chart does not have the shortcoming of the c- and u-charts of showing up false alarm signals too frequently. The CQC-chart does not require rational subgrouping of samples (which is necessary for the c- and u-charts), and is appropriate for monitoring automated manufacturing processes.

Cell formation problem with consideration of both intracellular and intercellular movements

The cellular manufacturing system (CMS) is a well-known strategy which enhances production efficiency while simultaneously cutting down the system-wide operation cost. Most of the researchers have been focused on developing different approaches in order to identify machine-cells and part-families more efficiently. In recent years, researchers have also focused their studies more scrupulously by collectively considering CMS with production volume, operation sequence, alternative routing or even more. However, very few of them have tried to investigate both the allocation sequence of machines within the cells (intra-cell layout) and the sequence of the formed cells (inter-cell layout). Solving this problem is indeed very important in reducing the total intracellular and intercellular part movements which is especially significant with large production volume.  In this paper, a two-phase approach has been proposed to tackle the cell formation problem (CFP) with consideration of both intra-cell and inter-cell part movements. In the first phase, a mathematical model with multi-objective function is formed to obtain the machine cells and part families. Afterwards, in the second phase, another mathematical model with single-objective function is presented which optimizes the total intra-cell and inter-cell part movements. In other words, the scope of problem has been identified as a CFP together with the background objective of intra-cell and inter-cell layout problems (IAECLP). The primary assumption for IAECLP is that only linear layouts will be considered for both intra-cell and inter-cell. In other words, the machine within cells and the formed cells are arranged linearly. This paper studies formation of two mathematical models and used the part-machine incidence matrix with component operational sequence. The IAECLP is considered as a quadratic assignment problem (QAP). Since QAP and CFP are NP-hard, genetic algorithm (GA) has been employed as solving algorithm. GA is a widespread accepted heuristic search technique that has proven superior performances in complex optimization problems and further it is a popular and well-known methodology. The proposed algorithms for CFP and IAECLP have been implemented in JAVA programming language.

Availability Optimization of Systems Subject to Competing Risk

This paper considers a competing risk (degradation and sudden failure) maintenance situation. A maintenance model and a repair cost model are presented. The degradation state of the units is continuously monitored. When either the degradation level reaches a predetermined threshold or a sudden failure occurs before the unit reaches the degradation threshold level, the unit is immediately repaired (renewed) and restored to operation. The subsequent repair times increase with the number of renewals. This process is repeated until a predetermined time is reached for preventive maintenance to be performed. The optimal maintenance schedule that maximizes the unit availability subject to repair cost constraint is determined in terms of the degradation threshold level and the time to perform preventive maintenance.

The application of genetic algorithms to lot streaming in a job-shop scheduling problem

A new approach using genetic algorithms (GAs) is proposed to determine lot streaming (LS) conditions in a job-shop scheduling problem (JSP). LS refers to a situation that a job (lot) can be split into a number of smaller jobs (sub-lots) so that successive operations of the same job can be overlapped. Consequently, the completion time of the whole job can be shortened. By applying the proposed approach called LSGAVS, two sub-problems are solved simultaneously using GAs. The first problem is called the LS problem in which the LS conditions are determined and the second problem is called JSP after the LS conditions have been determined. Based on timeliness approach, a number of test problems will be studied to investigate the optimum the LS conditions such that all jobs can be finished close to their due dates in a job-shop environment. Computational results suggest that the proposed model, LSGAVS, works well with different objective measures and good solutions can be obtained with reasonable computational effort.

A resource-constrained assembly job shop scheduling problem with Lot Streaming technique

To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.

A two-stage decision procedure for monitoring processes with low fraction nonconforming

Abstract Decision procedures for monitoring industrial processes can be based on application of control charts. The commonly used p-chart and np-chart are unsatisfactory for monitoring high-quality processes with a low fraction nonconforming. To overcome this difficulty, one may develop models based on the number of items inspected until r (⩾1) nonconforming items are observed. The cumulative count control chart (CCC-chart) is such an example. Like many other control charts, the CCC-charts suggested in the literature are one-stage control charts in which a decision is made when a signal for out of control appears. A CCC-chart with a small value of r requires less items inspected in order to obtain a signal for out of control, but is less reliable in detecting shifts of p than a CCC-chart with a large value of r (because the standard deviation of the number of items inspected in order to observe the rth nonconforming item, when divided by the mean, is proportional to 1/ r ). In the present paper, inspired by the idea of double sampling procedures in acceptance sampling, a two-stage CCC-chart is proposed in order to improve the performance of the one-stage CCC-chart. Analytic expressions for the average number inspected (ANI) of this two-stage CCC-chart is obtained, which is important for further studies of the chart. As an application of this result, an economic model is used to calculate the optimal values of probabilities of false alarm set at the first and second stages of the two-stage CCC-chart so that an expected total cost can be minimized.

A study of EWMA chart with transformed exponential data

The exponentially weighted moving average (EWMA) chart has been shown to be effective in detecting small process shifts and predicting the process level at the next time period. In this paper, a new EWMA chart is proposed to monitor exponentially-distributed time-between-events (TBE) data. The proposed EWMA chart is set up after transforming the TBE data to approximate normal using the double square root (SQRT) transformation. The average run length (ARL) properties of an EWMA chart with transformed exponential data are investigated based on which design procedures are developed. Subsequently, the performance of the EWMA chart with transformed exponential data is compared to that of the X-MR chart, the cumulative quantity control (CQC) chart and the exponential EWMA chart. Furthermore, the robustness of the proposed EWMA chart to Weibull-distributed TBE data is examined, followed by an example to illustrate the design and application procedures. The EWMA chart with transformed exponential data performs well in monitoring exponentially-distributed TBE data.

Quality management system development some implications from case studies

The literature in the field of Industrial Engineering suggests that the improvement of quality management practices is a continuous development process. According to this suggestion, a Quality Management System (QMS) is developed gradually step-by-step and not in sudden dramatic jumps. It has also been pointed out by many authors that the improvement of quality management progressively leads to more efficient internal operations, followed by better satisfied external customers and eventually superior marketing and financial performance. However, the results from a number of case studies in our investigation reveal that those manufacturing companies in Hong Kong which realize that quality is an important strategic consideration for improving their sales and marketing performance invest heavily in quality management and develop their QMS close to standard suggestions in the literature but the transformation is too rapid. These manufacturing firms do not really improve their customer satisfaction by making internal operations more efficient. Instead, they put great effort in developing their QMS in order to satisfy their customers with quality products at low prices, resulting in market growth but not gains in financial benefits. This investigation suggests that quality management in such companies should be conducted in a cost-effective way and directed towards improving the operational efficiency and effectiveness of the entire organization rather than simply satisfying the customers.

A genetic algorithm-based approach to machine assignment problem

Over the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment. In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance.

An introduction of dominant genes in genetic algorithm for FMS

This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to deal with FMS scheduling problems with alternative production routing. In the traditional genetic algorithm (GA) approach, crossover and mutation rates should be pre-defined. However, different rates applied in different problems will directly influence the performance of genetic search. Determination of optimal rates in every run is time-consuming and not practical in reality due to the infinite number of possible combinations. In addition, this crossover rate governs the number of genes to be selected to undergo crossover, and this selection process is totally arbitrary. The selected genes may not represent the potential critical structure of the chromosome. To tackle this problem, GADG is proposed. This approach does not require a defined crossover rate, and the proposed similarity operator eliminates the determination of the mutation rate. This idea helps reduce the computational time remarkably and improve the performance of genetic search. The proposed GADG will identify and record the best genes and structure of each chromosome. A new crossover mechanism is designed to ensure the best genes and structures to undergo crossover. The performance of the proposed GADG is testified by comparing it with other existing methodologies, and the results show that it outperforms other approaches.