# 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. scheduling; heuristic; makespan minimization +
Footnote †: journal: Applied  
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Footnote 1: email: _Applied_  
Footnote †: journal: Applied  
DJSS problem (FDJSS or simply FJSS). The FJSS problem has been particularly interesting in recent years in the context of _Industry 4.0_[7], in which the production lines include advanced features that allow the machines to communicate and be reorganized and reconfigured to meet the increasingly challenging production constraints. A crucial aspect that is not fully explored in the existing literature is the automation of _job transfers_ between machines. While task execution on machines can be characterized by processing times, the transfer process involves various settings, including (i) the means of transportation, such as automatic guided vehicles (AGVs) or conveyor belts, and (ii) input and output buffers at the machines, determining their ability to store jobs for processing (e.g., while finishing another task). The store processed jobs (while awaiting the availability of the transport system) (Our definition of _transfer_ pertains to the time between the end of one task and the start of the next for a given job). Certain combinations of transport systems and buffers may be modeled using constant time [8], as is the case when the transport facility is always available, and the machines have sufficiently large input and output buffers. In such scenarios, incorporating transfer time into the processing time is straightforward, and numerous solutions proposed in the literature [4] can be applied. In other cases, transfer time depends on the availability of the transport system and the destination machine [1; 9; 10]. Overall, no single model can cover all alternatives, necessitating ad-hoc modeling for specific combinations. In this study, we consider a scenario inspired by a _fully automated production line_, incorporating a conveyor belt as a means of transportation and no buffer at the machines. In this setup, once a task is completed, the job is immediately placed on the belt to free the machine. If the next machine is occupied, the job remains on the belt until the destination machine becomes available. The JSS problem is well-known to be NP-hard [11], and the variant considered here is at least as challenging [12].