Energy-Efficient Straight Robotic Assembly Line Using Metaheuristic Algorithms

This paper focuses on the implementation of metaheuristic algorithms to solve straight robotic assembly line balancing problem with an objective of maximizing line efficiency by minimizing the energy consumption of the assembly line. Reduction in the energy consumption is of high importance these days due to the need of creating environmental friendly industries and also due to the increase in the cost of energy. Due to the availability of different types of robots in the market, there is a necessity of selecting efficient set of robots to perform the tasks in the assembly line and optimizing the efficiency of its usage in the line effectively. Two well-known metaheuristic algorithms: particle swarm optimization (PSO) and differential evolution (DE) are implemented to solve due to the NP-hard nature of the problem. Proposed algorithms are tested on the benchmark problems available in the literature and the detailed comparative results are presented in this paper. It can be seen that proposed DE algorithm could obtain better results when compared with PSO from the experimental study.