Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Abstract

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

“…Fatemi-Anaraki et al [9] devised a mixed-integer linear programming (MILP) model, integrating three acceleration constraints to solve the no-wait JSP involving material exchanges among multiple robots. Gaiardelli et al [10] introduced a randomized heuristic approach and considered dynamic job arrivals to design a fully rescheduling strategy. Kong and Wang [11] proposed an improved discrete particle swarm algorithm based on Pareto optimization to solve the multi-objective FJSP, which includes setup time, processing time, and handling time.…”