Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Park, Gyunam; Song, Minseok

doi:10.1109/icpm.2019.00027

Cited by 42 publications

(29 citation statements)

References 32 publications

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“…The paper by Di Francescomarino et al provides an overview over the current state of the art in process prediction [10]. In very recent publications, neural networks are employed for simulating process logs [7] and supporting resource allocation in business processes [31].…”

Section: Deep Learning and Nlp In Bpmmentioning

confidence: 99%

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

2021

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Complaints about finished products are a major challenge for companies in the medical technology industry, where product quality is directly related to public health and therefore strictly regulated. In this paper, we examine how available data can be used to provide automated support to the complaint handling processes in the medical technology companies. We identify the automation potentials in the 8D reference process for complaint management and discuss their organizational and technical challenges. Using data from a large manufacturer of medical products, we show how partial process automation can be achieved in practice by designing, implementing, and evaluating a deep learning-based prototype for automatically suggesting a likely error code for future complaints, given their textual description. Our approach is able to assign the correct error code for more than 75% of all cases and outperforms the conventional classification approaches used as a baseline comparison. Our results show that partial automation of a complaint management process by means of deep learning can be achieved in practice.

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Section: Deep Learning and Nlp In Bpmmentioning

confidence: 99%

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

2021

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“…There are number of approaches for resource allocation that rely on applying predictive models. In [15] an offline prediction model based on LSTM is combined with extended minimum cost and maximum flow algorithms.…”

Section: Resource Allocationmentioning

confidence: 99%

Deep Reinforcement Learning for Resource Allocation in Business Processes

Żbikowski¹,

Ostapowicz²,

Gawrysiak³

2021

Preprint

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Assigning resources in business processes execution is a repetitive task that can be effectively automated. However, different automation methods may give varying results that may not be optimal. Proper resource allocation is crucial as it may lead to significant cost reductions or increased effectiveness that results in increased revenues. In this work, we first propose a novel representation that allows modeling of a multi-process environment with different process-based rewards. These processes can share resources that differ in their eligibility. Then, we use double deep reinforcement learning to look for optimal resource allocation policy. We compare those results with two popular strategies that are widely used in the industry. Learning optimal policy through reinforcement learning requires frequent interactions with the environment, so we also designed and developed a simulation engine that can mimic real-world processes. The results obtained are promising. Deep reinforcement learning based resource allocation achieved significantly better results compared to two commonly used techniques.

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“…Predictive monitoring has been built on different machine and deep-learning techniques, and also on their ensemble [9]. Different research works have recently illustrated that the socalled Long Short-Term Memory networks (LSTMs) generally outperform other methods (see, e.g., [14], [23], [12]). Therefore, while our explanation framework is independent of the machine-or deep-learning technique that is employed, we operationalize it with LSTMs.…”

Section: A Prediction Of Process-related Kpismentioning

confidence: 99%

Explainable Predictive Process Monitoring

Galanti

Coma-Puig²,

Leoni

et al. 2020

2020 2nd International Conference on Process Mining (ICPM)

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Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

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Prediction-based Resource Allocation using LSTM and Minimum Cost and Maximum Flow Algorithm

Cited by 42 publications

References 32 publications

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

Toward Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry

Deep Reinforcement Learning for Resource Allocation in Business Processes

Explainable Predictive Process Monitoring

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