Application of data computing for resource scheduling in manufacturing industries

Abstract Cloud based solutions and services can be used in production sectors to maximize profit and to develop complex products through reconfiguration of manufacturing supply chain through dynamic process. Scheduling and allocation of resources to tasks is a critical issue, since inappropriate allocation and scheduling may affect the utilization rate, cost and time. Cloud manufacturing encompass flexible services from manufacturing enterprise with IT based services to improve the performance of an application. This work considers Differential Evolutionary (DE) and Gravitational Search algorithm for implementation, since these approaches can narrow the solution search space and improves convergence rate. The tasks and resources used for the experimentation process were classified and incorporated from casting industries. The computational results on the identified workflow proves that, DEGSA algorithm maximizes resource utilization, maintains load balance and minimizes the computational time of jobs.