A bio-inspired hybrid computation for managing and scheduling virtual resources using cloud concepts

N. C. Brintha, Shajulin Benedict, J. T.Winowlin Jappes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Resource allocation and scheduling is one of the major issues in manufacturing industries which are constrained to offer dynamic and virtualized resources to end users in-order to maximize the profit. Cloud manufacturing is a new paradigm that can satisfy the requirements of modern manufacturing industries. In this work, two variants of heuristic algorithm are used to solve resource scheduling issues in casting industries. Particle swarm optimization algorithm is used in this work, because it can solve large scale optimization problems with better search speed, and genetic algorithms can be used to provide solution for non-linear and highly intricate engineering problems. This work uses a hybrid approach which combines the advantages of genetic algorithm with particle swarm optimization in-order to provide global convergence at effective and optimal cost. Experimentation was carried out for casting of engine block in manufacturing industry and the simulation results shows that PSO with GA provides global optimal convergence and also produces effective results with respect to time, cost and resource utilization.

Original languageEnglish
Pages (from-to)565-572
Number of pages8
JournalApplied Mathematics and Information Sciences
Issue number2
StatePublished - 2017
Externally publishedYes


  • Cloud manufacturing
  • Engine block
  • GA-PSO algorithm
  • Makespan
  • PSO algorithm
  • Tasks and resources


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