Optimisation of Resource Allocation in Large-Scale Engineering Projects Using AI-Based Decision Models

Abstract

In software development, varied decisions need to be made to ensure the fulfilment. Customers frequently seek a wide range of functions in large software projects, resulting in a vast set of requirements. Due to project timeframes and resource constraints, implementing all of the requirements is usually not possible. Setting priorities for a large number of requirements takes time and is challenging. As a result, an organised method of prioritising and subsequently choosing the primary set of needs based on several factors is required. Diverse techniques were available to prioritise the requirements effectively. But the accuracy and time consumption for Requirements Prioritisation were not optimised. Also, during the large-scale Requirements Prioritisation, multiple aspects such as time, cost are not considered. Therefore, three novel methods are proposed for enhancing the performance of large-scale Requirements Prioritisation with better accuracy and less time. Many resource plans were affected by the unexpected joining and leaving events of human resources, which may cause uncertainty. This uncertainty can also affect the quality of the project delivery. Appointing a developer to the first allotted task until the completion of the same may reduce the flexibility of human resources, even though the developer can do other tasks. Optimised Event-Based Scheduler handles this uncertainty and resource flexibility. It is pretty commonplace that we need more time for scheduling if the developer's record is enormous. Subsequently, the search space is also big, and in the long run, the resource allocation is not on time.