T. C. Wong

A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach

When developing new products it is important for design teams to understand customer perceptions of consumer products because the success of such products is heavily dependent upon the associated customer satisfaction level. The chance of a new products success in a marketplace is higher if users are satisfied with it. In this study, a new methodology of generating customer satisfaction models using a neuro-fuzzy approach is proposed. In contrast to previous research, non-linear and explicit customer satisfaction models can be developed with the use of the proposed methodology. An example of notebook computer design is used to illustrate the methodology. The proposed methodology was measured against the benchmark of statistical regression to determine its effectiveness. Experimental results suggested that the proposed approach outperformed the statistical regression method in terms of mean absolute errors and variance of errors.

Flexible job-shop scheduling problem under resource constraints

A flexible job-shop-scheduling problem is an extension of classical job-shop problems that permit an operation of each job to be processed by more than one machine. The research methodology is to assign operations to machines (assignment) and determine the processing order of jobs on machines (sequencing) such that the system objectives can be optimized. This problem can explore very well the common nature of many real manufacturing environments under resource constraints. A genetic algorithm-based approach is developed to solve the problem. Using the proposed approach, a resource-constrained operations–machines assignment problem and flexible job-shop scheduling problem can be solved iteratively. In this connection, the flexibility embedded in the flexible shop floor, which is important to todays manufacturers, can be quantified under different levels of resource availability.

Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.

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 comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop

Very often, studies of job shop scheduling problem (JSSP) ignore assembly relationship and lot splitting. If an assembly stage is appended to JSSP for the final product, the problem then becomes assembly job shop scheduling problem (AJSSP). To allow lot splitting, lot streaming (LS) technique is examined in which jobs may be split into a number of smaller sub-jobs for parallel processing on different stages such that the system performance may be improved. In this study, the system objective is defined as the makespan minimization. In order to investigate the impact of LS on the system objective under different real-life operating conditions, part sharing ratio (PSR) and system congestion index (SCI) are considered. PSR is used to differentiate product-specific components from general-purpose, common components, and SCI for creating different starting conditions of the shop floor. Both PSR and CSI are useful as part sharing (also known as component commonality) is a common practice for manufacturing with assembly operations and system loading is a significant factor in influencing the shop floor performance. Since the complexity of AJSSP is NP-hard, a hybrid genetic algorithm (HGA) and a hybrid particle swarm optimization (HPSO) are proposed and developed to solve AJSSP in consideration of LS technique. Computational results show that for all test problems under various system conditions, HGA can significantly outperform HPSO. Also, equal-sized lot splitting is found to be the most beneficial LS strategy especially for medium-to-large problem size.

Modelling customer satisfaction for product development using genetic programming

Product development involves several processes in which product planning is the first one. Several tasks normally are required to be conducted in the product-planning process and one of them is to determine settings of design attributes for products. Facing with fierce competition in marketplaces, companies try to determine the settings such that the best customer satisfaction of products could be obtained. To achieve this, models that relate customer satisfaction to design attributes need to be developed first. Previous research has adopted various modelling techniques to develop the models, but those models are not able to address interaction terms or higher-order terms in relating customer satisfaction to design attributes, or they are the black-box type models. In this paper, a method based on genetic programming (GP) is presented to generate models for relating customer satisfaction to design attributes. The GP is first used to construct branches of a tree representing structures of a model where interaction terms and higher-order terms can be addressed. Then an orthogonal least-squares algorithm is used to determine the coefficients of the model. The models thus developed are explicit and consist of interaction terms and higher-order terms in relating customer satisfaction to design attributes. A case study of a digital camera design is used to illustrate the proposed method.

Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network

Emergency Department (ED) plays a critical role in healthcare systems by providing emergency care to patients in need. The quality of ED services, measured by waiting time and length of stay, is significantly affected by patient arrivals. Increased patient arrivals could undermine service timeliness, thus putting patients in severe conditions at risk. These factors lead to the following research questions that have rarely been studied before: What are the variables directly associated with patient arrivals in the ED? What is the nature of association between these variables and patient arrivals? Which variable is the most influential and why? To address the above questions, we proposed a three-stage method in this paper. First, a data-driven method is used to identify contributing variables directly correlated with the daily arrivals of Categories 3 and 4 patients (i.e., non-critical patients). Second, the association between contributing variables and daily patient arrival is modeled by using artificial neural network (ANN), and the modeling ability is compared with that of nonlinear least square regression (NLLSR) and multiple linear regression (MLR) in terms of mean average percentage error (MAPE). Third, four types of relative importance (RI) of input variables based on ANN are compared, and their statistical reliability is tested by the MLR-based RI. We applied this three-stage method to one year of data of patient arrivals at a local ED. The contribution of this paper is twofold. Theoretically, this paper emphasizes the importance of using data-driven selection of variables for complex system modeling, and then provides a comprehensive comparison of RI using different computational methods. Practically, this work is a novel attempt of applying ANN to model patient arrivals, and the result can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.

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.

Analyzing supply chain operation models with the PC-algorithm and the neural network

Research highlights? We study the relations and magnitudes of influences among key factors in a supply chain models. ? Our method is a two-stage approach using (i) the PC-algorithm and (ii) the neural network. ? Using (i), we obtain the skeleton graph describing relations among the factors. ? Internal operation and collective efficacy are deemed the most critical factors based on the graph. ? Using (ii), we quantify the relative importance of other factors in predicting the critical factors. Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on.