Evaluating a Sustainable Intelligent Logistic System (ILS) Utilizing O-S Data and Holistic Managerial Models

Abstract

The intelligent logistic system (ILS) has benefited Industry 4.0. ILS assures clean, on-time manufacturing. Due to its unique architectures, qualities, and sensory aspects, the robot logistic system (RLS) is sought after as an ILS in Industry 4.0. In case of COVID-19, multiple nations routinely used RLS as ILS to cleanse areas, check patients, and monitor crowds on highways. Research documents (RDs) show that prior researchers attempted to build the static robot logistic system (RLS) performance mapping index; however, most indexes measured only anatomy performance of static RLS. Thus, few RDs are edited previously in the context of MRLS. It is sensed that few RDs examined MRLS-linked performance mapping indexes, including only regular subjective (S) or objective (O) designs, excluding mixed S-O architectures. Most RDs constantly execute the linguistic variables related to fuzzy, grey, rough, ambiguous, and intuitionistic sets/scales to tackle the uncertainty connected with MRLS designs. The authors prioritize those as RGs. The authors proposed (1) an MRLS performance mapping index with respect to technical, cost, and value O-S architectures for recruiting MRLS, (2) linear information to assign ratings in a range of min-max values choosing from 1 to 100% without executing the linguistic scale, and (3) Holistic Managerial Models (HMM-1 and HMM-2) to handle subjective ratings and significance, assigned by Ex against evaluated O-S architectures under linear scale (1–100%). To prove the concept, RLS performance mapping is shown. Only MRLS recruitment and selection are covered. The effort helps CIM, FMS, and WCMS create sustainable, cleaner operations and achieve future goals.