A HYBRID MCDM APPROACH FOR MINING ROBOT SELECTION BASED ON ANALYTIC HIERARCHY PROCESS

Abstract

Amidst the 4.0 industrial revolution, technological advancements have created vast opportunities for enhancing automation within manufacturing processes. The integration of robots to supplant human labor stands as a pivotal development. These robots, now ubiquitous across various sectors, are instrumental in bolstering precision, speed, and quality control in production lines. However, the multiplicity of different robotic kinds with differing characteristics like load capacity, speed, and cost makes it difficult to choose the best robot for a given application. To address this issue, the adoption of multi-criteria decision-making (MCDM) methods has emerged as a valuable strategy for facilitating informed choices. Recent research endeavors have leveraged MCDM techniques to assess and compare robots across a spectrum of criteria. By employing these methodologies, industries can optimize robot selection processes, thereby enhancing operational efficiency. In our current investigation, we introduce a novel hybrid MCDM approach that amalgamates analytic hierarchy process (AHP) weighting with Additive Ratio ASsessment (ARAS), Complex PRoportional ASsessment (COPRAS), and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) methodologies to evaluate and rank mining robots. This methodology involves constructing weights based on expert insights, while also acknowledging the significance of pairwise criterion comparisons. Our findings indicate that robot 7 emerges as the most favorable choice, while robot 2 ranks as the least optimal option, demonstrating consistent outcomes across all three proposed methodologies. These findings have significant ramifications for manufacturers, providing them with crucial direction in negotiating the complex network of factors involved in choosing robots that meet production requirements.