Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

• Published in: Energies (Basel) Vol. 13; no. 18; p. 4948
• Main Authors: Abderrahim, Moussa, Bekrar, Abdelghani, Trentesaux, Damien, Aissani, Nassima, Bouamrane, Karim
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020; MDPI
• Subjects: Algorithms; Allocations; Automated guided vehicles; Automatic; Automation; battery management; Completion time; Computer Science; Energy efficiency; energy optimization; Engineering Sciences; Heuristic; Industrial plants; Job shops; job-shop scheduling; Manufacturing; Manufacturing industry; Materials handling; Operation scheduling; Operations Research; Optimization; Production lines; Replenishment; Robots; Schedules; Scheduling; Task scheduling; transport constraints; job-shop scheduling; battery management; energy optimization; transport constraints; automated guided vehicles
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13184948

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.