Optimizing Robot Selection in Manufacturing: A WASPAS Approach for Multi-Criteria Decision-Making

Abstract

Choosing the most suitable robot from multiple options is a challenging task in multi-criteria decision-making (MCDM). This study highlights the application of the WASPAS method, which integrates the Weighted Sum Model and the Weighted Product Model with additive and multiplicative components, to address a real-world robot selection problem. The evaluation considers 10 alternative robots based on five key criteria: handling capacity (HC), load capacity (LC), velocity (V), cost (C), and reach (R). To facilitate the decision-making process, an MCDM-based objective weighting tool was employed to assign appropriate significance to each criterion. Robots play a pivotal role in modern manufacturing by executing repetitive, complex, or hazardous tasks with accuracy, enhancing productivity and quality. With the diverse range of robots available, selecting the optimal one necessitates a thorough assessment of various factors to ensure alignment with specific operational needs. This study utilizes the WASPAS method, a hybrid approach combining the strengths of the Weighted Sum Model (WSM) and Weighted Product Model (WPM), to identify optimal parameter combinations while disregarding less significant factors. The findings demonstrate that the WASPAS method is a reliable and effective tool for selecting the most suitable robot based on technical performance and cost-effectiveness. Robots 1, 3, and 2 were identified as the top-performing options, offering an ideal balance across the evaluated criteria. Conversely, lower-ranked robots showed deficiencies in handling capacity, velocity, and cost. Overall, the WASPAS method proves to be a robust decision-making approach, aiding in the optimization of robot selection by integrating technical and economic factors to enhance efficiency and performance in manufacturing processes.