An Approach to Efficient Test Scheduling for Automated Production Systems

Land, Kathrin; Cha, Suhyun; Vogel‐Heuser, Birgit

doi:10.1109/indin41052.2019.8972166

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Epitropakis et al (2015) developed a method to find a prioritization technique that intends to fulfill several criteria. There are also several approaches that are specialized for a specific form of testing such as automotive (Haghighatkhah, 2020), user interfaces (Nguyen et al, 2019) or production systems (Land et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

XCSF for Automatic Test Case Prioritization

Rosenbauer

Stein

Pätzel

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

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Testing is a crucial part in the development of a new product. Due to the change from manual testing to automated testing, companies can rely on a higher number of tests. There are certain cases such as smoke tests where the execution of all tests is not feasible and a smaller test suite of critical test cases is necessary. This prioritization problem has just gotten into the focus of reinforcement learning. A neural network and an XCS classifier system have been applied to this task. Another evolutionary machine learning approach is the XCSF which produces, unlike XCS, continuous outputs. In this work we show that XCSF is superior to both the neural network and XCS for this problem.

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Section: Related Workmentioning

confidence: 99%

XCSF for Automatic Test Case Prioritization

Rosenbauer

Stein

Pätzel

et al. 2020

Proceedings of the 12th International Joint Conference on Computational Intelligence

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“…For this purpose, computer-aided process planning (CAPP) software packages have been developed to automate the planning and scheduling of industrial activities by using data structures such as lists [15], queues [16], tensors [17], graphs [18], or trees [19] to represent the activities and applying deterministic [20] or stochastic approaches [21] to allocate tasks, while minimizing the constraints with heuristics [22], optimization processes [23], tabu search [24], dispatching rules [25], auction mechanism [26], machine learning [27], or multiagent systems (MAS) [28].…”

Section: Introductionmentioning

confidence: 99%

“…Current efforts of automated scheduling in the automotive industry are mostly focused on developing intelligent systems for enterprise resource planning (ERP) [10], automated production systems (aPS) [15], supply chain management (SCM) [29], including order-to-delivery (OTD) process [30], Sales and operations planning (S&OP) [31], warehouse system automation [32] and just-in-time routing for delivery [33].…”

Section: Introductionmentioning

confidence: 99%

Automated Task Scheduling for Automotive Industry

Lewandowski

Olszewska

2020

2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES)

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Nowadays, the automotive industry requires an increased use of intelligent systems to endure. In this paper, we present a new solution for automated task scheduling to help automotive industry in efficiently managing garage employees' time and improving the effectiveness of the servicing and maintenance tasks of vehicles. The developed approach consists in a set of interconnected web applications with a model-view-controller based-on architecture and expert-knowledge temporal logic rules to automate the assignment of the daily workload for engineers working on multiple workstations within an automotive company. The proposed intelligent system prioritises and selects tasks for these engineers; the scheduled tasks being automatically ordered and displayed accordingly on screens visible in the related garage workstations. This automated task scheduling system has been successfully deployed within Arnold Clark Automobiles Ltd Company, and the performance of this new application used by a group of engineers under real-world operational conditions have been assessed and analysed.

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