Prof. Opiyo Elisha T. Omulo

Abstract:
The Information Technology industry has been very dynamic in health insurance for the last couple of decades. In this health insurance firms applications have been developed using different methodologies, and technologies. These applications cannot be integrated, which becomes an issue to health insurance interoperability. Health insurance is available to both individual and groups; there are many insurance companies in Kenya and there is no central database to extract stratified information about them. Attempts by many scholars to develop an interoperable system have not been successful largely due to lack of expertise, resources and the inherent complex nature of the health insurance system. There is also difficulty in accessing the required data by different people due to the fact that the applications of these insurance firms are independent. They are designed in any manner, and can use different technologies. This study proposed an agent-based platform which allowed users to receive and exchange data and information from distributed sources. The health insurance business was used as a case study to implement multi-agents approach in this study. In this case study, different agents were used to represent different functional areas in the developed system. The reactivity, proactive, sociability characteristics of multi-agents achieved health insurance interoperability and accomplished the health insurance business requirements. In this study we reviewed pure theory, evaluated and developed an agent based system which allowed health insurance firms to share data and also data sharing platform that enabled collaboration among multiple health insurance institutions in Kenya and also the experimental results showed that the platform could be extended easily to support a large number of concurrent client connections.

In this paper the scheduling of n independent jobs on m non-identical machines is considered for a large concrete schedule space for 30 jobs and 6 machines. The schedule space is about 1023 which is large enough to render exhaustive systematic search for the optimal schedule limited. The schedules are generated by agents that represent the jobs as they randomly select the machines on which the jobs should be processed. The schedules that are generated are evaluated using the makespan which is the total time taken for all the jobs to be processed. The optimal schedule, which is also the best schedule, is the one with the minimum or least makespan for any given set of job and machine properties. It is shown that the empirical best schedules that are generated are optimal when the job and machine properties are held to some uniform constant values. It is also shown that even when the job and machine properties are not uniform the empirical best schedules closely approximate the optimal schedules. This makes the agent-based approach to optimal schedule generation viable.