TY - JOUR
T1 - Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill
T2 - A Case Study
AU - Krishnamoorthy, Parkavi
AU - Satheesh, N.
AU - Sudha, D.
AU - Sengan, Sudhakar
AU - Alharbi, Meshal
AU - Pustokhin, Denis A.
AU - Pustokhina, Irina V.
AU - Setiawan, Roy
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - In the Flexible Manufacturing System (FMS), where material processing is carried out in the form of tasks from one department to another, the use of Automated Guided Vehicles (AGVs) is significant. The application of multiple-load AGVs can be understood to boost FMS throughput by multiple orders of magnitude. For the transportation of materials and items inside a warehouse or manufacturing plant, an AGV, a mobile robot, offers extraordinary industrial capabilities. The technique of allocating AGVs to tasks while taking into account the cost and time of operations is known as AGV scheduling. Most research has exclusively addressed single-objective optimization, whereas multi-objective scheduling of AGVs is a complex combinatorial process without a single solution, in contrast to single-objective scheduling. This paper presents the integrated Local Search Probability-based Memetic Water Cycle (LSPM-WC) algorithm using a spinning mill as a case study. The scheduling model's goal is to maximize machine efficiency. The scheduling of the statistical tests demonstrated the applicability of the proposed model in lowering the makespan and fitness values. The mean AGV operating efficiency was higher than the other estimated models, and the LSPM-WC surpassed the different algorithms to produce the best result.
AB - In the Flexible Manufacturing System (FMS), where material processing is carried out in the form of tasks from one department to another, the use of Automated Guided Vehicles (AGVs) is significant. The application of multiple-load AGVs can be understood to boost FMS throughput by multiple orders of magnitude. For the transportation of materials and items inside a warehouse or manufacturing plant, an AGV, a mobile robot, offers extraordinary industrial capabilities. The technique of allocating AGVs to tasks while taking into account the cost and time of operations is known as AGV scheduling. Most research has exclusively addressed single-objective optimization, whereas multi-objective scheduling of AGVs is a complex combinatorial process without a single solution, in contrast to single-objective scheduling. This paper presents the integrated Local Search Probability-based Memetic Water Cycle (LSPM-WC) algorithm using a spinning mill as a case study. The scheduling model's goal is to maximize machine efficiency. The scheduling of the statistical tests demonstrated the applicability of the proposed model in lowering the makespan and fitness values. The mean AGV operating efficiency was higher than the other estimated models, and the LSPM-WC surpassed the different algorithms to produce the best result.
KW - Manufacturing system
KW - automated guided vehicles
KW - computer integrated manufacturing
KW - makespan
KW - spinning mill
KW - water cycle algorithm
UR - http://www.scopus.com/inward/record.url?scp=85147313073&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3236843
DO - 10.1109/ACCESS.2023.3236843
M3 - Article
AN - SCOPUS:85147313073
SN - 2169-3536
VL - 11
SP - 9389
EP - 9402
JO - IEEE Access
JF - IEEE Access
ER -