Scheduling of Single-Arm Cluster Tools with Residency Time Constraints and Chamber Cleaning Operations

Li, Jie; Qiao, Yan; Zhang, Siwei; Li, Zhiwu; Wu, Naiqi; Song, Tairan

doi:10.3390/app11199193

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…Scheduling strategies based on priority, cycling, and WIP were investigated to address both small-scale and large-scale flexible production lines, enhancing product productivity 30 . Zhu et al 31 and Li et al 32 both investigated a cluster tool scheduling problem in manufacturing systems to prevent deadlock. The objective of this problem was to optimize the maximum completion time and WIP inventory, and a nonlinear programming model was established.…”

Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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With the increasing demand for customization, flow shop scheduling tends to process multi-variety small batch products. Thus, there are frequent switches between batches. Frequent switchovers between different sets are required during batch changeover, where machine setups may be necessary. This paper investigates scheduling optimization and work-in-process inventory optimization problems in a hybrid flow shop with rolling processing requirements. Developing continuous processing conditions to meet rolling processing requirements presents a significant challenge. In this work, continuous processing conditions are derived. Subsequently, a linear programming model is designed to find an optimal solution for this scheduling problem. Formulas for calculating work-in-process inventory are presented. To enhance work-in-process storage, a greedy algorithm with bubble sort and a Q-learning algorithm with a modified ε-policy are proposed to find an optimized product sequence and reduce work-in-process inventory, respectively. Numerical experiments evaluate the effectiveness and efficiency of the proposed methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

Lu,

Liu,

Zhang

2024

Preprint

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“…For the case of multicluster tool systems, with a parametric number of production stations in each cluster, the number of studies towards synchronization of the cluster tool and scheduling analysis of the manufacturing unit is increasing (indicatively, see [10][11][12][13][14]). The goal of these studies is to increase the production speed and optimize the job scheduling of the manufacturing unit.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Supervisor Architecture for a General Wafer Production System

Koumboulis

Fragkoulis

Georgakopoulos

2023

Sensors

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The current trend in the wafer production industry is to expand the production chain with more production stations, more buffers, and robots. The goal of the present paper is to develop a distributed control architecture to face this challenge by controlling wafer industrial units in a general production chain, with a parametric number of production stations, one robot per two stations where each robot serves its two adjacent production stations, and one additional robot serving a parametric number of stations. The control architecture is analyzed for individual control units, one per robot, monitoring appropriate event signals from the control units of the adjacent robots. Each control unit is further analyzed to individual supervisors. In the present paper, a modular parametric discrete event model with respect to the number of production stations, the number of buffers, and the number of robotic manipulators is developed. A set of specifications for the total system is proposed in the form of rules. The specifications are translated and decomposed to a set of local regular languages for each robotic manipulator. The distributed supervisory control architecture is developed based on the local regular languages, where a set of local supervisors are designed for each robotic manipulator. The desired performance of the total manufacturing system, the realizability, and the nonblocking property of the proposed architecture is guaranteed. Finally, implementation issues are tackled, and the complexity of the distributed architecture is determined in a parametric formula. Overall, the contribution of the present paper is the development of a parametric model of the wafer manufacturing systems and the development of a parametric distributed supervisory control architecture. The present results provide a ready-to-hand solution for the continuously expanding wafer production industry.

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Scheduling analysis of automotive glass manufacturing systems subject to sequence-independent setup time, no-idle machines, and permissive maximum total tardiness constraint

He,

Qiao,

et al. 2024

Engineering Applications of Artificial Intelligence

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Scheduling of Single-Arm Cluster Tools with Residency Time Constraints and Chamber Cleaning Operations

Cited by 9 publications

References 27 publications

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

A Q-learning Algorithm for Two-Stage Hybrid Flow Shop Scheduling

A Distributed Supervisor Architecture for a General Wafer Production System

Scheduling analysis of automotive glass manufacturing systems subject to sequence-independent setup time, no-idle machines, and permissive maximum total tardiness constraint

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