Genetic algorithm-assisted fuzzy clustering framework to solve resource-constrained project problems

Abstract

Resource-constrained problems for technology-based applications/services are common due to pervasive utilization and in-definite user/demand densities. Traditional resource allocation methods consume high allocation time and make it difficult to predict the possible solutions from the collection of resources. Various range of solutions through optimizations are provided for addressing the issues that, however, result in imbalanced solutions. This article assimilates genetic algorithm (GA) and fuzzy clustering process and introduces resource-constrained reduction framework. The proposed framework utilizes a GA for mutating the allocation and availability possibilities of the resources for different problems. The possibilities of solutions are tailored across various demands preventing replications. Post this process, the fuzzy clustering segregates the optimal, sub-optimal, and non-optimal solutions based on the mutation rate from the genetic process. This reduces the complexity of handling heterogeneous resources for varying demand, user, and problem densities. Based on the clustering process, the crossover features are tailored across multiple resource allocation instances that mitigate the existing constraints. This proposed framework improves the problem-addressing ability (11.44%) and improves resource allocation (8.08%), constraint mitigation (11.1%), and allocation time (11.85%).

Keywords

• demand allocation
• fuzzy clustering
• genetic algorithm
• resource constrained