Simulation based evaluation of different empty vehicle management strategies with considering future transport jobs

Schmaler, Robert; Schmidt, Thorsten; Schoeps, Matthias; Luebke, Joerg; Hupfer, Ralf; Schlaus, Nikolas

doi:10.1109/wsc.2017.8248071

Cited by 11 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applications investigating Task Allocation problems often facilitate DES-based models (e.g., [13,[49][50][51]). Thereby, any changes to the environment (especially vehicle movements) occur by temporally and spatially discretised steps along the navigation graph.…”

Section: Simulation Characteristicsmentioning

confidence: 99%

Experimental Evaluation of AGV Dispatching Methods in an Agent-Based Simulation Environment and a Digital Twin

2023

View full text Add to dashboard Cite

A critical part of Automated Material Handling Systems (AMHS) is the task allocation and dispatching strategy employed. In order to better understand and investigate this component, we here present an extensive experimental evaluation of three different approaches with randomly generated, as well as custom designed, environment configurations. While previous studies typically focused on use cases based on highly constrained navigation capabilities (e.g., overhead hoist transport systems), our evaluation is built around highly mobile, free-ranging vehicles, i.e., Autonomous Mobile Robots (AMR) that are gaining popularity in a broad range of applications. Consequently, our experiments are conducted using a microscopic agent-based simulation, instead of the more common discrete-event simulation model. Dispatching methods often are built around the assumption of the asynchronous evaluation of an event-based model, i.e., vehicles trigger a cascade of individual dispatching decisions, e.g., when reaching intersections. We find that this does not translate very well to a fleet of highly mobile systems that can change direction at any time. With this in mind, we present formulations of well known dispatching approaches that are better suited for a synchronous evaluation of the dispatching decisions. The formulations are based on the Stable Marriage Problem (SMP) and the Linear Sum Assignment Problem (LSAP). We use matching and assignment algorithms to compute the actual dispatching decisions. The selected algorithms are evaluated in a multi-agent simulation environment. To integrate a centralised fleet management, a digital twin concept is proposed and implemented. By this approach, the fleet management is independent of the implementation of the specific agents, allowing to quickly adapt to other simulation-based or real application scenarios. For the experimental evaluation, two new performance measures related to the efficiency of a material handling system are proposed, Travel Efficiency and Throughput Effort. The experimental evaluation indicates that reassignment mechanisms in the dispatching method can help to increase the overall efficiency of the fleet. We did not find significant differences in absolute performance in terms of throughput rate. Additionally, the difference in performance between SMP- and LSAP-based dispatching with reassignment seems negligible. We conclude with a discussion, where we consider potential confounding factors and relate the findings to previously reported results found in the literature.

show abstract

Section: Simulation Characteristicsmentioning

confidence: 99%

Experimental Evaluation of AGV Dispatching Methods in an Agent-Based Simulation Environment and a Digital Twin

2023

View full text Add to dashboard Cite

show abstract

“…Schmaler et al [21] introduced the possibility of available forecast data into empty vehicle dispatching strategies for the unified AMHS of a semiconductor manufacturing facility. This study shows that the scenario of using only information regarding upcoming tool events, and thus only half of all possible future event information, performs better than a watermark-based method.…”

Section: Literature Reviewmentioning

confidence: 99%

Clustered Multi-Task Sequence-to-Sequence Learning for Autonomous Vehicle Repositioning

et al. 2021

View full text Add to dashboard Cite

Clustered multi-task learning, which aims to leverage the generalization performance over clustered tasks, has shown an outstanding performance in various machine learning applications. In this paper, a clustered multi-task sequence-to-sequence learning (CMSL) for autonomous vehicle systems (AVSs) in large-scale semiconductor fabrications (fab) is proposed, where AVSs are widely used for wafer transfers. Recently, as fabs become larger, the repositioning of idle vehicles to where they may be requested has become a significant challenge because inefficient vehicle balancing leads to transfer delays, resulting in production machine idleness. However, existing vehicle repositioning systems are mainly controlled by human operators, and it is difficult for such systems to guarantee efficiency. Further, we should handle the small data problem, which is insufficient for machine learning because of the irregular time-varying manufacturing environments. The main purpose of this study is to examine CMSL-based predictive control of idle vehicle repositioning to maximize machine utilization. We conducted an experimental evaluation to compare the prediction accuracy of CMSL with existing methods. Further, a case study in a real largescale semiconductor plant, demonstrated that the proposed predictive approach outperforms the existing approaches in terms of transfer efficiency and machine utilization.INDEX TERMS Clustered multi-task learning, sequence-to-sequence, vehicle repositioning, idle vehicle balancing, automated material handling systems, overhead hoist transports.

show abstract

“…Kim et al [11] and Vahdani [12] proposed an idle vehicle circulation policy to balance the number of idle vehicles among the bays. More recently, Lee et al [13] and Schemaler et al [14] utilized the information of future transports to respond to highly dynamic manufacturing environments.…”

Section: Introductionmentioning

confidence: 99%

An MDP-Based Lifter Assignment Algorithm for Inter-Line Transportation in Semiconductor Fabrication

Shin

Jang²,

Kim

2021

Preprint

View full text Add to dashboard Cite

As semiconductor device geometries continue to shrink, the semiconductor manufacturing process becomes increasingly complex. This usually results in unbalanced utilization of machines and decreases overall productivity. One way to resolve such a problem is to share the resource capacity between different lines divided by floors. To this end, designing an efficient lifter assignment method to more efficiently manage transfer requests (TRs) of wafer lots to different floors is required. Motivated by this, our study addresses the assignment of lifters for delivering wafer lots to different floors. Unlike previous studies, which consider the current state of the system, our study considers both the current and possible future states of the system. We formulate an optimization model based on the Markov decision process. Then, we design an efficient method as a solution using both clustering and tournament selection methods. Experiments based on historical data confirm the effectiveness of the proposed algorithm in reducing travel times and delivery delays compared to the benchmark rules in practice. Sensitivity analysis demonstrates the robustness of the proposed model as the number of TRs increased. The proposed approach is expected to yield significant economic savings in both operating costs and labor.

show abstract

Simulation based evaluation of different empty vehicle management strategies with considering future transport jobs

Cited by 11 publications

References 15 publications

Experimental Evaluation of AGV Dispatching Methods in an Agent-Based Simulation Environment and a Digital Twin

Experimental Evaluation of AGV Dispatching Methods in an Agent-Based Simulation Environment and a Digital Twin

Clustered Multi-Task Sequence-to-Sequence Learning for Autonomous Vehicle Repositioning

An MDP-Based Lifter Assignment Algorithm for Inter-Line Transportation in Semiconductor Fabrication

Contact Info

Product

Resources

About