Fixed Task Scheduling of Industrial Robot using Genetic Algorithm Based Travelling Salesman Problem
Sasmita Nayak1, Neeraj Kumar2, B. B. Choudhury3

1Sasmita Nayak*, Ph.D. Scholar, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India.
2Neeraj Kumar, Department of Mechanical Engineering, Suresh Gyan Vihar University (SGVU), Jaipur, Rajasthan, India.
3B. B. Choudhury, Department of Mechanical Engineering, Indira Gandhi Institute of Technology (IGIT), Odisha, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 1771-1775 | Volume-9 Issue-5, March 2020. | Retrieval Number: D1595029420/2020©BEIESP | DOI: 10.35940/ijitee.D1595.039520
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Abstract: The task scheduling of any industrial robots is a prior requirement to effectively use the capability by obtaining shortest path with optimum completion time. In this article, we have presented Travelling Salesman Problem (TSP) with Genetic Algorithm (GA) search technique based task scheduling technique for obtaining optimum shortest path of the task. TSP finds an optimal solution to search for the shortest route by considering every location for completing the required tasks by setting up GA. This article embrace the adaption and implementation of the Genetic Algorithm search strategy for the task scheduling problem in the cooperative control of multiple resources for getting shortest path with minimize the completion time for two zone specific task allocation problem. It can be inferred from the simulation results that the Genetic Algorithm search technique can be considered as a viable solution for the task scheduling problem. 
Keywords: Genetic algorithm (GA), Travelling Salesman Problem (TSP), Robot, Task Scheduling, Cooperative control.
Scope of the Article: Computer-supported cooperative work