Multiobjective based Event based Project Scheduling using Optimized Neural Network based ACO System

In any software project management, developing third party software tools and scheduling tasks are challenging and important. Any software development projects are influenced by a large number of activities, which can greatly change the project plan. These activities may form groups of correlated tasks or event chains. Assessment planning is a crucial challenge in software engineering whose major goal is to schedule the persons to different tasks in such a way that the quality of the software product is optimal and the cost of the project should be minimum. In the traditional approach an event dependent scheduler ant colony optimization is applied on task scheduling. The ACO will develop an optimized plan, in the form of matrix, from all the iterations. And from that plan the EBS(Event Based Scheduler) will develop schedule based on events. ACO solves the problem of project scheduling, but it does not consider the updated task allocation matrix. The ACO is not a satisfactory model to solve the problem of project scheduling. The traditional ACO system also indicates the problem of allocating the identical activity for several numbers of employees in varying periods. In this proposed work, an improved ACO approach to optimal global search using a neural approach was introduced to schedule multiple tasks. An activity with specified number of tasks and relevant resources can be optimally scheduled using multi-objective approach. When an uncertain event occurs the remaining resources will be effectively calculated, also the remaining tasks to complete. And