Open AccessResource Scheduling using Cloud in Chemical & Electro Chemical Coating Application
Author(s) -
N.C. Brintha,
J.T. Winowlin Jappes
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1099.1291s419
Subject(s) - job shop scheduling , computer science , workflow , cloud computing , scheduling (production processes) , minification , genetic algorithm scheduling , distributed computing , operations research , schedule , flow shop scheduling , mathematical optimization , engineering , database , programming language , operating system , mathematics
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.