C.K. Kwong

A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment

Quality function deployment (QFD) is an important tool in product planning that could contribute to increase in customer satisfaction and shorten product design and development time. During the QFD process, determination of the importance weights of customer requirements is a crucial and essential step. The analytic hierarchy process (AHP) has been used in weighting the importance. However, due to the vagueness and uncertainty existing in the importance attributed to judgement of customer requirements, the crisp pairwise comparison in the conventional AHP seems to be insufficient and imprecise to capture the degree of importance of customer requirements. In this paper, fuzzy number is introduced in the pairwise comparison of AHP. An AHP based on fuzzy scales is proposed to determine the importance weights of customer requirements. The new approach can improve the imprecise ranking of customer requirements which is based on the conventional AHP. Finally, an example of bicycle splashguard design is used to illustrate the proposed approach.

Combining scoring method and fuzzy expert systems approach to supplier assessment: a case study

Very often, manufacturing companies have been faced with the problem of assessment and selection of suppliers for their product development. Some methods and techniques have been developed to assist these companies in performing the assessment. However, these methods and techniques lack tge capability to deal with the instrumental and conceptual uncertainties that are involved in the supplier assessment and selection. Fuzzy expert system is an alternative approach from which the heuristics and knowledge of supplier assessment can be captured and the impreciseness and uncertainties due to the human subjectivity, that are common in the process of the supplier assessment, can be handled. In this paper, a combined scoring method with fuzzy expert systems approach is introduced to perform the supplier assessment. With the use of the fuzzy concept, the error due to human judgement in the scoring method could be minimized. First, current methods and techniques of supplier assessment are reviewed in this paper. There follows the description of a case study of combined scoring method and fuzzy expert systems approach to supplier assessment. Some results of the prototype system trial‐run are discussed in the final part of this paper.

Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding

Determination of initial process ’meters for injection molding is a highly skilled job and based on skilled operator’s “know-how” and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach. Facing with the global competition, the current trial-and-error practice becomes inadequate. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process ’meter setting of injection molding is described. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. A computer-aided system for the determination of initial process ’meter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. The preliminary validation tests of the system have indicated that the system can determine a set of initial process ’meters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.

A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach

Process design of injection moulding involves the selection of the injection moulding machine, mould design, production scheduling, cost estimation, and determination of injection moulding parameters. An expert system approach has been used to derive the process solution for injection moulding over the past few years. However, this approach is found to be incapable of determining the injection moulding parameters owing to the fragile nature of the knowledge for setting the moulding parameters. In addition, the existing expert systems for process design lack proper architecture for organising heterogeneous knowledge sources. In this paper, the combination of a blackboard-based expert system and a case-based reasoning approach is introduced to eliminate the deficiency of the existing expert-system approach to process design, from which a computational system for the process design of injection moulding, named CSPD, has been developed. CSPD first derives the process solution including the selection of the injection moulding machine and the mould base, tooling cost, processing cost estimation, and production scheduling based on the blackboard-based expert-system approach. It is then followed by the determination of the injection moulding parameters based on the case-based reasoning approach and the previously derived partial solution.

Integrating supplier selection in optimal product family design

Product family design and supplier selection are traditionally separated in two successive stages. First, product development teams determine optimal levels of the attributes of product components for each product variant of a product family, and purchasing departments then choose the qualified suppliers with the lowest cost. However, decoupling the two decision processes may lead to suboptimal solutions with regard to the total market profit of a company. In this article, a unified optimisation model, which integrates product family design with supplier selection, is established with the objective of maximising the total profit of a product family. Consumer purchase behaviour, supplier availability and outsourcing-related cost are considered in the model. A linear programming embedded genetic algorithm was developed to solve the proposed model. A case study is presented to illustrate the feasibility and effectiveness of the proposed model and algorithms.

Genetic optimization of JIT operation schedules for fabric-cutting process in apparel manufacture

Fashion products require a significant amount of customization due to differences in body measurements, diverse preferences on style and replacement cycle. It is necessary for today’s apparel industry to be responsive to the ever-changing fashion market. Just-in-time production is a must-go direction for apparel manufacturing. Apparel industry tends to generate more production orders, which are split into smaller jobs in order to provide customers with timely and customized fashion products. It makes the difficult task of production planning even more challenging if the due times of production orders are fuzzy and resource competing. In this paper, genetic algorithms and fuzzy set theory are used to generate just-in-time fabric-cutting schedules in a dynamic and fuzzy cutting environment. Two sets of real production data were collected to validate the proposed genetic optimization method. Experimental results demonstrate that the genetically optimized schedules improve the internal satisfaction of downstream production departments and reduce the production cost simultaneously.

Design of maintenance system in MRPII

Very limited research has attempted to consider maintenance strategies in the design of MRPII. The manufacturers who need to optimise the return of their assets and facilities using systematic maintenance management will find that the MRPII system is unable to provide them with the solution. Proper design and integration of maintenance management into MRPII enable the manufacturers not only to manage their production planning and scheduling activities but also to analyse their maintenance history, carry out cost analysis and study the failure trends to determine how the available labour and materials in maintenance can be used effectively. In order to overcome the weakness of the MRPII system in the management of maintenance activities, this paper describes some research work that has been carried out using the integrated definition method (IDEF) model to systematically integrate maintenance into MRPII. Moreover, in order to illustrate the methodology, a lamp manufacturing company with a lot of highly automated equipment and facilities which depends on modern maintenance strategies as well as MRPII‐type production planning and control is described.

Determination of fault-tolerant fabric-cutting schedules in a just-in-time apparel manufacturing environment

In apparel manufacturing, accurate upstream fabric-cutting planning is crucial for the smoothness of downstream sewing operations. Effective and reliable fabric-cutting schedules are difficult to obtain because the apparel manufacturing environment is fuzzy and dynamic. In this paper, genetic algorithms and fuzzy-set theory are used to generate fault-tolerant fabric-cutting schedules in a just-in-time production environment. The proposed method is demonstrated by two cases with production data collected from a Hong Kong-owned garment production plant in China. Results of the two cases preliminarily show that the genetically improved fault-tolerant schedules effectively satisfy the demand for downstream production units, guarantee consistent and reliable system performance, and also reduce production costs through reduced operator idle time. More cases will be conducted in order to further validate the effectiveness of the proposed method.

A hybrid artificial intelligence system for optical lens design

The current approach to optical lens design still relies very much on the lens designers experience and knowledge. In the initial lens design, it is normally performed by lens designers and then is optimised by using some optimisation techniques. This research aims to design and develop a computer-aided optical lens design system for automating the entire design process. In this paper, various approaches to optical lens design are briefly reviewed first. It is then followed by the descriptions of a hybrid artificial intelligence (AI) approach, based on the case based reasoning and genetic algorithm, to optical lens design. A prototype optical lens design system based on the hybrid approach was proposed and developed. The system not only could generate an initial and optimal optical lens design automatically but also demonstrate that the design could be done in lean knowledge paradigm.

An orthogonal array based genetic algorithm for developing neural network based process models of fluid dispensing

Fluid dispensing is a popular process in the semiconductor manufacturing industry, commonly being used in die-bonding as well as microchip encapsulation of electronic packaging. Modelling the fluid dispensing process is important to understanding the process behaviour as well as determining the optimum operating conditions of the process for a high-yield, low-cost and robust operation. In this paper, an approach to integrating neural networks with a modified genetic algorithm is presented to model the fluid dispensing process for electronic packaging. The modified genetic algorithm is proposed by incorporating the crossover operator with an orthogonal array. We compare the modified genetic algorithm with the standard genetic algorithm. The results indicate that a better quality encapsulation can be obtained based on the modified genetic algorithm.