Fuzzy Branch and Bound Optimization Technique for Solving Multi-objective Next-Release Problem

The next release problem (NRP) is a special kind of intellectual decision-making problem including multiple participants and stakeholders expressing their opinions on a set of objects for releasing the application’s next object. In the case of multi-object and multi-consumer applications designing the scheme for NRP is a fundamental and crucial process for developing an efficient application. The service provider of applications or software should provide and meet the requirements of consumers through consistent new next releases so that the maximum number of consumers will be satisfied with the minimum possible cost. This work develops a novel method for the multi-object and multi-consumer NRP in an application for this purpose a Fuzzy Branch and Bound Optimization (FBBO) NRP model is proposed with a fuzzy inference system and branch and bound as the backbone. The main aim of the proposed work is to optimize the multi-consumer’s requirement for the next releases, improving the degree of similarity and reducing the likelihood of system failure in large-scale organizations. For the experimental analysis of the proposed FBBO model, both benchmark and real-world classic and realistic datasets are acquired, and observed that the proposed model outperformed the existing models.