Hsing-Pei Kao

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.

Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming

In recent years, determining the best supplier has become a key strategic consideration in the competitive market. Since the decision commonly involves evaluating different criteria or attributes, supplier selection process is a multiple criteria decision-making (MCDM) problem. This study integrates the Taguchi loss function, analytical hierarchy process (AHP) and multi-choice goal programming (MCGP) model for solving the supplier selection problem. The advantage of this proposed method is that it allows decision makers to set multiple aspiration levels for the decision criteria. A numerical example of application is also presented.

An integrated information system for product design planning

In recent years, new product development (NPD) has become more and more competitive and globalized, and the planning phase is absolutely critical for the success of a NPD project. Quality function deployment (QFD) has been recognized as one of the most important approaches for NPD. Nevertheless, the issues of product design scheduling and costing have not been explicitly addressed in the deployment process. On the other hand, the design structure matrix (DSM), a popular representation and analysis technique, has led to its increasing use in a variety of contexts, including product development, project management and system engineering. In this paper, we develop a novel framework which integrates QFD and DSM to support product design planning, including product architecture/components deployment, design scheduling and costing. A knowledge-based database is developed to support the proposed framework. The details are presented and a prototype product design planning system is implemented with a practical application in the semiconductor industry, system-on-a-chip product design planning, to validate the completeness and benefits of the proposed approach.

Design and implementation of a fuzzy inference system for supporting customer requirements

Efficient and effective response to the requirements of customers is a major performance indicator. Failure to satisfy customer requirements implies operational weaknesses in a company. These weaknesses will damage both the rights of customers and the reputation of the company. The traditional method of handling customer requirement for a machine tool manufacturer was dominated by manual process and subjective decision. In this study, we improved the operation process of handling customer requirement. The framework of a customer requirement information system (CRIS) for machine tool manufacturers was then analyzed, integrating rule-based fuzzy inference and expert systems, and a prototype system developed. The CRIS supports both customers and service personnel in providing a systematic way of fulfilling and analyzing customer requirements. The system was installed and operated in a machine tool manufacturer and the performance was found promising.

An adaptive scheduling system with genetic algorithms for arranging employee training programs

An outstanding human resources system is one of the important competitive factors in modern enterprises. Employee training is a frequently used method for promoting the working capabilities for better human resources. Employee training programs cultivate personnel capabilities for enterprise operation. The training effect can be dramatically enhanced if curriculum is well designed and arranged. However, arranging curriculum is a difficult and lengthy task which enterprises traditionally devote a large amount of human and material resources to. We improved the process by proposing an optimal curriculum arrangement model in maintenance personnel training programs, and utilizing genetic algorithms as solution procedures. An adaptive computer aided training system for maintenance representative training was also developed to facilitate personnel training for machine tool industry.

An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming

Logistics customer service is an important factor in the success of supply chain management. The aim of this study is to propose a novel approach for customer service management. For the improvement of logistics service operations, the proposed method integrates quality function development (QFD), fuzzy extended analytic hierarchy process (FEAHP), and multi-segment goal programming (MSGP). The advantage of the method includes the consideration of various logistics goals and the flexibility of setting multi-aspiration levels of evaluation criteria.

An event-driven approach with makespan/cost tradeoff analysis for project portfolio scheduling

Project Portfolio Management (PPM) has become increasingly important in managing multiple projects simultaneously. However, during execution, the project portfolio is associated with considerable uncertainty that leads to resource contention among projects and schedule disruptions. Regarding the need to revise a schedule when unexpected events occur, we adopt an event-driven approach to develop a tradeoff decision framework for project portfolio scheduling and rescheduling. In the framework, High Level Petri nets, Activity-Based Costing and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied in sequence to generate feasible schedules, estimate their makespan and costs, and select the best compromise schedule. This framework is advantageous in the evaluation of scheduling performance from a multi-criteria perspective and the incorporation of both objective and subjective measurements in the decision process.

An empirical study on knowledge integration, technology innovation and experimental practice

In the global market, inter-firm collaborative product development has become an increasingly significant business strategy for enhanced product competitiveness. Engineering knowledge is a key asset for technology-based enterprises to successfully develop new products and processes. Experimental practice is a crucial process for knowledge integration and technology innovation. This research explores this in inter-firm collaborative product development through experimental practice. We conducted a series of in-depth case studies to investigate the patterns of knowledge integration in the collaborative development of system-on-a-chip (SoC) by semiconductor firms. Our studies focused on the central interactive process for engineering applications and experimental practice to enhance knowledge integration and technology innovation for rapid development. Furthermore, we identified factors critical for experimental practice in effective engineering knowledge integration and innovation.

Ontology-based knowledge management for joint venture projects

When enterprises engage in strategic joint venture projects, communication, knowledge sharing and management issues are inevitable and complex problems. Problems relating to communication, knowledge sharing and management issues generally occur in more than one phase of joint venture projects and involve various different domains. This study reviews issues related to knowledge management in the context of joint ventures (JV) from both macro and micro perspectives by using an ontology-based approach to analyze complex knowledge management (KM) issues in the context of the Integrated Circuit (IC) industry. This study presents a framework for an ontology-based model to analyze the knowledge management processing of joint ventures in the IC industry, and uses the IC foundry industry as an example to explore implementation issues. This framework uses a conceptual enterprise ontology (EO) model and domains of EO to analyze the process of EO. Moreover, data from the IC foundry industry is used as a case study. This study aimed to assist managers in increasing the possibility of success in dealing with complicated JV projects.

I2QFD: A blackboard-based multiagent system for supporting concurrent engineering projects

Although Concurrent Engineering has been accredited as a superior approach to product development, its intrinsic complexity in coordinating the multidisciplinary project team is further augmented when team members are remotely distributed. Assuming the teamwork is supported by an Intranet, this paper proposes the framework of a blackboard-based multiagent system, called I 2 QFD, to facilitate the communication and coordination of the design projects. Configured within a hierarchical structure, this system is defined with IDEF0 and high-level Petri nets to depict the operations of Intelligent Agencies and their affiliated agents. Based on temporal logic, the operation of the project team with I 2 QFD is simulated to manifest the dynamics of the design process as well as to generate the projects schedule that ensures the intended concurrency.