Resource Scheduling using Cloud in Chemical & Electro Chemical Coating Application
N.C. Brintha1, J.T. Winowlin Jappes2
1N.C.Brintha, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, (Tamil Nadu), India.
2J.T. Winowlin Jappes, Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 99-106 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A10991291S419/19©BEIESP | DOI: 10.35940/ijeat.A1099.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the advancements and use of computer based technologies in manufacturing sectors, there has been a drastic change in how manufacturing industries perform their business. Manufacturing tied up with cloud computing technologies can help the customers, suppliers and manufacturers in several ways and hence can maximize profit in production lines. However, even though there are several policies for scheduling workflows in production, effective mapping of tasks with resources is always a challenging issue. If scheduling policies are inappropriate, it will have a negative impact on cost, time and therefore may affect the overall performance of the workflow. This work proposes a Multi-Objective Genetic Algorithm (MOGA) based scheduling model to schedule resources related to chemical and electro chemical coating. The major objective of this work is to reduce makespan, improve resource utilization and also minimize the overall cost of the workflow. This information obtained from the workflow can be used for better decision making when several tasks has to be done in parallel by migrating the tasks to the site of resource availability. The computational results also shows that MOGA performs well in makespan minimization, resource utilization and cost minimization because of its convergence speed and robustness. The analysis results prove that, MOGA can be optimal for estimating the path of where the work can be done such that the makespan and cost is minimized.
Keywords: Coating, Chemical Process, Cloud Computing, Manufacturing, Multi-Objective Genetic Algorithm (MOGA).
Scope of the Article: Cloud Computing