Chen-Fang Tsai

Service Selection Based on Fuzzy TOPSIS Method

With rapid development of service-oriented architecture and cloud computing, web services have been widely adopted for developing various kinds of applications. A set of non-functional requirements such as QoS has become important criteria for service selection. The nature of QoS based service selection can be treated as a multiple criteria group decision making (MCDM) problem. This article presents an evaluation method based on the technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to help service consumers and providers to analyze available web services with fuzzy opinions. A set of pre-defined linguistic variables parameterized by triangular fuzzy numbers, are used by the group to evaluate the weights of various criteria and the ratings of each alternative web service. As a result, the available alternative web services can be ordered according to group preference. Finally, a numerical example is provided to demonstrate the computational efficiency of the proposed fuzzy TOPSIS method.

Analysis of grid service composition with BPEL4WS

The open grid services infrastructure (OGSI) defines a distributed system framework by integrating grid and Web services technologies to facilitate resource sharing. In OGSI, Web services are supplemented with additional features in order to meet the requirements of grid computing. However, the issue of grid service composition is not well addressed in the OGSI framework. We apply BPEL4WS (business process execution language for Web services) as a business workflow description language for the composition of grid services. We provide an in depth analysis of BPEL4WS and OGSI in terms of their similarities and differences in areas such as life cycle management, Web service instantiation and instance group management. Based on our analysis we propose a high-level architecture to compliment OGSI with BPEL4WS for defining process workflow among grid services. We describe a prototype system which shows how the proposed architecture can be used in modelling or orchestrating grid services with BPEL4WS.

A user centric service-oriented modeling approach

With rapid development of service-oriented architecture and cloud computing, web services have been widely employed on the Internet. Quality of Service (QoS) is a very important criterion for service consumers to measure and select services. The selection of web services with respect to non-functional QoS criteria can be considered as a Multiple Criteria Decision Making (MCDM) problem when multiple consumers need to share a number of services. This paper describes a new user centric service-oriented modeling approach which is featured by integrating fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Service Component Architecture (SCA) to facilitate web service selection and composition and to effectively satisfy a group of service consumers’ subjective requirements and preferences in the dynamic environment. The main contribution of this method is able to translate a group of users’ fuzzy requirements to services as well as model different levels of hardware and software as services to meet the requirements. We also design a simulated environment that includes 8*8 LED matrix on a circuit board that corresponds to an office with different appliances to demonstrate the dynamic service selection and binding. The simulation is used to assess the computational efficiency of the fuzzy TOPSIS method and the effectiveness of the proposed system.

An intelligent adaptive system for the optimal variable selections of R&D and quality supply chains

Abstract The cost of research & development (R&D) and quality management are always regarded as two major parts of total cost. The variable performance of R&D and quality design is an important index that will reflect the effectiveness of the cost reduction. This research has attempted to simultaneously vary all of the variables to achieve the global optimum for the optimal variable selections of R&D and quality design. Genetic algorithm (GA) can treat all of the variables for the global search. In this study, fuzzy refinement with orthogonal arrays was effective in improving the performance of the GA, and also showed the benefits of a good chromosome structure on the behavior of GA. It is also proposed the postponement design with temporal concept, to select the effective variables for the cost reduction of R&D and quality management design. The experimental results showed that tempo-postponement design will increase the flexibility and quick response for supply chain management. Hence, this approach can act as a useful guideline for researchers working on the optimization of the key variable selections for R&D and quality model design.

A hybrid model for cloud providers and consumers to agree on QoS of cloud services

This paper describes a new agenda based approach which facilitates multi-issue negotiation process between service consumer and service provider over the quality of service (QoS) requirements in cloud services. We assume that the order of agenda (issues) has impact on the negotiation outcome, but a group of service consumers and providers may have conflicting preferences over the importance of issues. In a multiple-issue negotiation process it is difficult to reach an optimal outcome when their preferences and the relative importance of their associated issues are not known to each other. The proposed agenda based preference ordering approach helps consumers and providers to reach a consensus over the issues and to construct a common preference sequence to improve the efficiency of the issue-by-issue negotiation outcome. Consequently, a co-evolutionary negotiation model based on the result of preference ordering approach is introduced for the agents to negotiate and reach an agreement, if there is any. The contribution of the research is a new mechanism that is able to formulate issue sequence along with a co-evolutionary negotiation approach that can effectively facilitate negotiation over QoS issues in cloud computing. Finally, a case study is provided to illustrate the proposed approach. This research has proposed a new negotiation mechanism to deal with the Cloud Computing Market.The negotiation mechanism can efficiently and effectively identify a possible agreement in a large and complex search space.A case study is adopted to evaluate and demonstrate the proposed mechanism.

Chromosome refinement for optimising multiple supply chains

Multiple supply chains management is a multiple criteria optimisation problem with a large and complex search space. It is a broad field in which the aim is to optimise and facilitate supply chain operations by balancing between quality improvement and cost reduction. This research proposes a dynamic adaptive Genetic Algorithm that includes a chromosome refinement procedure to adjust the structure and order of the genes within the chromosome. The method improves the search efficiency in a complex space by locating near-global optimal solutions. The proposed approach is implemented as a process parameter controller that allows the values of the production and quality control variables to be adjusted at the run-time. A case study involving various evaluation criteria and a number of variables in multiple supply chains within a mixed production problem domain is adopted to demonstrate the effectiveness of the proposed approach.

Degree of satisfaction in agent negotiation

Agent negotiation over multiple issues is often seen as the process of searching for a solution in a complex and large space. Depending on the negotiation mechanism such search space can be dynamic wherein agents may be cooperative or non-cooperative. In a realistic negotiation, agents are unwilling to reveal their utility functions to their opponents or collaborators. These important characteristics of negotiation increase the complexity of the design of efficient and effective negotiation agents. We propose an approach that combines a co-evolutionary mechanism with the notion of degree of satisfaction. The former effectively searches the space, while the latter improves negotiation efficiency. Agents under the proposed scheme can carry out cooperative, or non-cooperative, without revealing their utility functions. The proposed approach is implemented as a prototype system and evaluated through a number of experiments. The evaluation shows the effectiveness of the proposed approach in cooperation and non-cooperation.

An Effective Chromosome Representation for Optimising Product Quality

Optimising variables in the quality control of production can be a complex issue, as it may involve many different constraints and expectations on the quality of products which are normally provided from different organisations to form a multiple supply chain. The challenge of measuring product interdependence across various supply chains and identifying a trade-off between quality and cost is not trivial. In this research, which applies dynamic genetic algorithms, we propose a new approach to representing the problem domain within the chromosome which takes advantage of schema evolution and domain knowledge to refine the chromosome structure. As a result, different weightings can be derived and applied to the genes in order to improve searching efficiency of the genetic algorithms (GA). An example of multiple supply chains has been used to evaluate the proposed approach. The results show that the proposed approach outperforms traditional GA approaches.

Ontological On-line Analytical Processing for Integrating Energy Sensor Data

Abstract In this paper we propose an ontological OLAP (on-line analytical processing) framework to integrate distributed energy sensor data. The OLAP data cube in the framework annotated with semantics with other supporting mechanisms can deal with the issues of the schema inconsistency that may result from integration of heterogeneous data sources. The proposed approach provides a way of storing, reusing and composing OLAP cubes in order to increase system usability. A prototype of the proposed framework based on a number of existing tools such as protégé, Jess, and Fuzzy J has been developed to demonstrate its feasibility.

A dynamic evolutionary mechanism for mixed-production

This paper describes a new dynamic evolutionary mechanism which assists process engineers in devising efficient processes for manufacturing high quality items where the mixed production approach is adopted. An adaptive system, including the use of genetic algorithms (GA) as a dynamic searching mechanism, is designed in order to maximize the stability of the quality control in the mixed production processes. GA is an effective approach in optimization as it is able to alter manufacturing variables so as to reach a global optimum in complex production processes such as multiple quality chains. The choice of the GA operators and its parameters, however, is a significant problem and inappropriate selection of chromosome structure can lead to poor performance. In order to deal with these issues, a dynamic parameter and operator setting approach with a mechanism based on quality control chart theory, is proposed. The approach allows a trade-off between exploration and exploitation processes in the search. The mechanism applies evolution evidence to supervise and adjust the GA parameter settings at run time. A prototype system has been implemented and applied to optimization problems in multiple quality chains. The experimental results have revealed that the dynamic setting approach can improve the performance of a GA process in multiple quality chains. The results also established that the dynamic setting approach is superior to a static one.