Application of Hybrid Sfla and Aco Algorithm to Omega Plate for Drilling Process Planning and Cost Management

Abstract

Tool traveling and tool switching time constitute almost seventy percent (70%) of the total time consumed in drilling process. This fact that 70% of the total time is nonproductive and does not add any value to the job, grabs attention of the researchers and the industrialist for optimization. A literature on drilling process revealed that very few studies have been done on hybridization of metaheuristics for optimization of tool travel time. This research gap is the motivation of the present study. In this study, two metaheuristic approaches – the shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) were hybridized. With respect to hybridization of SFLA and ACO, this study signifies its originality and novelty in which main objective is to minimize the tool travel time. The literature review also revealed that the shortest path generated through commercially available software is not optimal all the time. This aspect emphasizes the application of metaheuristic algorithms on the real-world industrial problems. In this study, the proposed hybrid algorithm was applied to drilling of omega plate which is used in automobile manufacturing industry. The results of the proposed hybrid algorithm were compared with those of manual drilling path and software generated path. The results obtained through proposed hybrid algorithm were improved by 11.1% when compared to results of manual drilling path. The results of proposed algorithm were also better than results of commercial software Creo 6.0 and Siemens NX by 5.9% each. This showed that hybrid algorithm outperformed the commercially available software. This not only validates the efficacy of proposed hybrid algorithm, but also indicates the significance of the metaheuristic algorithm applications in industrial optimization problems.

Optimal Scheduling of Renewable Energy Resources in Energy Management Systems Using Hybrid Genetic Algorithm and Particle Swarm Optimization

“…Singapore can fulfill increasing requirements and integrate new renewable energy sources simultaneously. SG integrates contemporary communication infrastructure with the current power grid to optimize available energy sources at a specific location [2] [25].…”

Optimal Scheduling of Renewable Energy Resources in Energy Management Systems Using Hybrid Genetic Algorithm and Particle Swarm Optimization

“…Singapore can fulfill increasing requirements and integrate new renewable energy sources simultaneously. SG integrates contemporary communication infrastructure with the current power grid to optimize available energy sources at a specific location [2] [25].…”