An enhanced decision making model for industrial robotic selection using three factors: Positive, abstained, and negative grades of membership

Abstract

Traditional packaging industries that lack automation often grapple with a spectrum of challenges that impede operational efficiency, productivity and overall competitiveness. To maintain quality and safety, the food industry must transition from manual to robotic packaging processes. The most suitable robot for executing such task is identified via a new hybrid fuzzy Stratified Multi-Attribute Decision-Making (S-MADM). This model comprises Fuzzy-PIvot Pairwise RElative Criteria Importance Assessment (F-PIPRECIA) and Stratified Network Mapping (SNM). The Hesitant T-Spherical Fuzzy (HT-SF) set effectively tackles the uncertainty associated with the selection process by introducing three factors, namely, positive, abstained, and negative grades of membership. A case study is presented to demonstrate the novel fusion model and determine the optimal food packaging robot. A total of twelve criteria are selected from the literature based on the decision-maker’s judgment. Among the seven industrial robots, the ”Delta robot” gained the highest ranking (71%), followed by the ”Scara robot” (56%), and the ”Multi-Axis Gantry” (49%). Delta robots, with spider-like limbs, can move gently and accurately at fast speeds, with heavy motors fixed on the frame, allowing for lightweight moving parts. The proposed SNM technique helps to enhance the effectiveness of these alternatives by analyzing the possible, inefficient, and highly influential states of the system. The sensitivity study confirms this analysis and the system’s durability while the comparative study verifies the effectiveness and feasibility of the proposed technique than existing MADM models. This paradigm enables company stakeholders to invest in an industrial robot that not only delivers better results but also efficiently overcome barriers associated with manual packaging procedures.